Utilizing Wireless-based Data Collection Units for Automated Vehicle Movement Data Collection

AN ABSTRACT OF THE DISSERTATION OF

Amirali Saeedi for the degree of Doctor of Philosophy in

Industrial Engineering presented on February 22 2013

Title Utilizing Wireless-based Data Collection Units for Automated Vehicle

Movement Data Collection

Abstract approved ______________________________________________ David S Kim

There are many different types of automatic data collection technologies that have

been used in transportation system applications such as pneumatic tubes radar video

cameras inductive loops detectors wireless toll tags and global positioning systems

(GPS) Nevertheless there are still multiple examples of important and helpful

transportation system data that still require manual data collection In this research

the automatic transportation system data collection capabilities are expanded by

enhancements in the use of wireless communications technology

In recent years smartphones and electronic peripherals with wireless communication

capabilities have become very popular Many of these electronic devices include a

Bluetooth or Wi-Fi wireless radio whose presence in a vehicle can be used as a

vehicle identifier With wireless on-board devices available now and in the future

this research explores how roadside data collection units (DCUs) communicating with

on-board devices can be used for the automated data collection of important road

system data such as intersection performance data

To this end two approaches for wirelessly collecting vehicle movement over a short

road segment were explored One approach utilized the collection and triangulation of

wireless signal strength data and demonstrated the capabilities and limitations of this

approach The second approach focused on developing methods for utilizing wireless

signal strength data for vehicle point detection and identification

The vehicle point detection methods developed were applied to collect travel time

data over signalized arterial roads and to collect intersection delay data for a three

way stop controlled intersection The results from these case studies indicate a

significant advantage in the proposed data collection system over the existing data

collection approaches presented in the literature

copyCopyright by Amirali Saeedi

February 22 2013

All Rights Reserved

Utilizing Wireless-based Data Collection Units for Automated Vehicle Movement

Data Collection

by

Amirali Saeedi

A DISSERTATION

submitted to

Oregon State University

in partial fulfillment of

the requirements for the

degree of

Doctor of Philosophy

Presented February 22 2013

Commencement June 2013

Doctor of Philosophy dissertation of Amirali Saeedi presented on

February 22 2013

APPROVED

Major Professor representing Industrial Engineering

Head of the School of Mechanical Industrial and Manufacturing Engineering

Dean of the Graduate School

I understand that my dissertation will become part of the permanent collection of

Oregon State University libraries My signature below authorizes release of my

dissertation to any reader upon request

Amirali Saeedi Author

ACKNOWLEDGEMENTS

I would especially like to thank my advisors Dr David S Kim and Dr J David

Porter for guiding me through this research with excellent patience and for the given

encouragements I would also like to thank my committee members Dr Toni Doolen

Dr Karen Dixon Dr David Hurwitz and Dr Scott Leavengood for their suggestions

and for reviewing this work

This work has received major benefits from discussions with other graduate students

in the Industrial Engineering Department at OSU among them Dr Sejoon Park

TABLE OF CONTENTS

Page

1 INTRODUCTION 1

11 RESEARCH MOTIVATION 1

12 RESEARCH OBJECTIVES 3

13 RESEARCH CHALLENGES 6

14 CONNECTION TO VEHICLE-TO-INFRASTRUCTURE RESEARCH 8

15 RESEARCH CONTRIBUTION 8

16 THESIS ORGANIZATION 10

2 LITERATURE REVIEW 11

21 TRAFFIC MEASURES OF EFFECTIVENESS 11

211 INTERSECTION PERFORMANCE MEASURES 15

22 AUTOMATIC DATA COLLECTION TECHNOLOGIES 17

221 EXISTING TRAVEL DATA COLLECTION SYSTEMS 21

23 BLUETOOTH TECHNOLOGY 25

231 BLUETOOTH-BASED DATA COLLECTION SYSTEMS 25 232 BLUETOOTH SIGNAL DISTANCE-SIGNAL STRENGTH RELATIONSHIP 29

24 CONNECTED VEHICLE RESEARCH INITIATIVE 30

3 METHODOLOGY 34


32 TRILATERATION APPROACH TO ESTIMATE VEHICLE MOVEMENT37

321 BLUETOOTH SIGNAL STRENGTH ACQUISITION 38 322 ESTIMATING DISTANCE FROM RSSI 39 323 PARTICLE SWARM OPTIMIZATION 43 324 SIGNAL LOCALIZATION APPROACHES 46

TABLE OF CONTENTS (Continued)

Page

325 TRILATERATION STUDY TO DEVELP A SIGNAL PROPAGATION MODEL 49 326 FITTING THE ENVIRONMENT POWER DECAY FACTOR 52 327 ACCURACY ASSESSMENT 54 328 CONCLUSIONS AND LIMITATIONS 55

33 VEHICLE POINT DETECTION SYSTEM 56

331 VEHICULAR MOVEMENTS NEAR A DATA COLLECTION UNIT AND THE RSSI-DISTANCE RELATIONSHIP 57 332 POINT DETECTION ALGORITHM 61 333 VALIDATION EXPERIMENTS 68

4 APPLICATION CASE STUDIES 80

41 TRAVEL TIME STUDY 80

411 GENERATION OF TRAVEL TIME SAMPLES USING MAC ADDRESS DATA 82 412 TRAVEL TIME SAMPLE GENERATION SUPPLEMENTED WITH RSSI DATA 83

42 INTERSECTION PERFORMANCE 88

421 INTERSECTION TEST SETUP 90 422 TEST RESULTS AND CONCLUSIONS 91

5 CONCLUSIONS 95

BIBLIOGRAPHY 98

APPENDICES 104

APPENDIX A BLUETOOTH INQUIRY PROCEDURE 105

APPENDIX B TRILATERATION ALGORITHM CODE 109

APPENDIX C PARTICLE SWARM OPTIMIZATION ALGORITHM IN VISUAL BASIC 113

LIST OF FIGURES

Figure Page

11 A Set of Three Wireless DCUs Installed at a Signalized Intersection 5

12 Schematic Representation of Bluetooth DCU Detection Range 7

31 Intersection of Three Circles at a Single Point 48

32 Trilateration Test Experiment Setup 50

33 Scanners Arrangement in an L Shape 50

34 Error Comparisons for Estimated Distances Using the Developed Signal Propagation Model for Both Test Cellphones 53

35 Highlighted Example of a Through Pass RSSI and Distance Sample Data 59

36 Highlighted Example of a Stop Far From Stop Line RSSI and Distance Sample Data 59

37 Highlighted Example of a Stop Near Stop Line RSSI and Distance Sample Data 60

38 Multiple detections within the DCUs detection zone 63

39 Schematic representation of multiple detections in a single trip forming a group 65

310 RSSI values over time for two devices in a probe vehicle arriving and waiting at an intersection 68

311 DCU installation on Camp Adair Road Benton County 71

312 Histogram of the difference between the time the probe vehicle passed the DCU on Camp Adair Road and the MAC address record with the highest RSSI 71

313 DCU Location on Wallace Road Salem Oregon 73

314 Histogram of the difference between the time the probe vehicle passed the DCU on Wallace Road and the MAC address record with the highest RSSI 73

315 Southernmost DCU locations on Highway 99W Tigard Oregon 74


LIST OF FIGURES (Continued)

Figure Page

317 Histogram of the difference between the time the probe vehicle passed DCU 12 on Highway 99W and the MAC address record with the highest RSSI 77

318 Histogram of accuracy of the time stamp before the RSSI values rapidly decrease 79

41 Location of Bluetooth DCUs on Highway 99W (Tigard OR) 82

42 Test Setup Utilized in the Intersection Control Delay Experiment 89


LIST OF TABLES

Table Page

21 Selected arterial performance measures 13

22 Most Common Automated Data Collection Technologies Used in Transportation Applications 18

31 Bluetooth Classifications 36

32 Random locations selected for Test Cell Phone 1 51

33 Signal Propagation Model Accuracy Test Results 55

34 Sample of MAC Address Data from an Installed Bluetooth DCU 64

41 Cell Phone 1 Sample Probe Vehicle Test Results for the Road Segment between Johnson St and the 217 NB Ramp 85

42 Average Absolute Travel Time Sample Errors in Seconds for Different Travel Time Calculation Approaches 86

43 The Volume Of Travel Time Samples Generated Compared To Traffic Volume 88

44 Summary Results from the Intersection Delay Study 92

A1 Discovery probability of Bluetooth devices in relation to inquiry time (Nicolai amp Kenn 2007) 108

DEDICATION

To my parents for their unconditional love and endless patience

1 INTRODUCTION



cameras inductive loops detectors wireless toll tags and global positioning system

(GPS) Nevertheless there are many types of transportation system data that still

require manual data collection In this research the capabilities of automatic

transportation system data collection are expanded by enhancements in the use of

wireless communications technology The introduction to this research will begin

with an overview of the motivating transportation system data collection application

for which automation will be beneficial This will be followed by a specification of

the research objectives and an overview of the research approach This introductory

chapter will conclude with a discussion of the research contributions presented and

the organization of this thesis

11 RESEARCH MOTIVATION

Intersections in urban and rural highway networks have a significant role on the

operation and performance of the traffic system since the performance of an

intersection controls the performance of the roads meeting at that intersection

Intersections can be bottlenecks in a road network with respect to throughput

affecting the capacity of the road system and its efficiency (as measured by travel

2

times) which may result in an impact on traffic congestion and safety The effect of

intersections on road network performance has been studied in previous research

(Ibrahim et al 2008 Sharma amp Swami 2010) Accordingly there has been research

directed at intersection design and control to increase capacity and provide high levels

of safety (Pickrell amp Neumann 2001) While the importance of intersections on the

performance of the traffic system and on safety has been recognized the acquisition

of intersection performance data still relies on manual data collection methods

Although more advanced automated methods are being tested but they are not

common practice yet There exist a number of different intersection performance

measures However the 2010 Highway Capacity Manual (Transportation Research

Board 2010) designates average control delay as the primary performance measure

for signalized intersections Control delay is defined as the total delay a vehicle

experiences due to interaction with the traffic control device at the intersection It

includes delay due to deceleration stopping and acceleration There are no currently

available low-cost automated data collection methods that can be utilized to collect

data to estimate control delay

In addition to intersections there are a variety of road segments that are ldquoareas of

interestrdquo for engineers where manual data collection is required These road segments

usually have some performance functional or geometric characteristics that require

engineers to conduct on-site data collection studies to investigate the cause for

existing issues or to predict vulnerable situations (eg for future developments)

3

Generally the criteria listed below can help to identify areas of interest for

transportation engineers

- Performance criteria Highly congested routes intersections with excessive

delays and corridors with high accident rates are main areas of interest For

these situations road segment data can help engineers to identify solutions to

the problems

- Functional criteria Some road segments may have been assigned a unique

functionality that distinguishes them from other road segments Airport roads

industrial roads interstate highways and roads located on evacuation plans

are main examples of this category (Kirby amp Pickett 2001) Road segment

data can be used to determine road segment performance for specific

functions

- Geometric criteria Sometimes a physical factor can change or affect (usually

for a short period of time) the normal functionality of a road segment

Examples could be construction zones accident sites or roads adjacent to an

attraction (high demand) center (US Department of Transportation 2008)

12 RESEARCH OBJECTIVES




vehicle identifier In the future it is envisioned that vehicles will be equipped with

4

on-board Dedicated Short Range Communication (DSRC) devices With wireless on-

board devices available now and in the future this research will explore how roadside

data collection units (DCUs) communicating with on-board devices can be used to

address the lack of automated data collection for important road system components

such as intersections

The objective of this research is to explore the capabilities of wireless radio

frequency technology with respect to automated vehicle data collection An

exploration of collecting vehicle movement data utilizing triangulation of wireless

signal data is conducted and demonstrates the capabilities and limitations of this

approach The research then focuses on developing the knowledge of how wireless

radio frequency technology can be utilized to provide automated vehicle point

detection and identification capability In this research vehicle point detection refers

to the determination of the time a traveling vehicle just crosses a specific line crossing

a road The wireless vehicle point detection and identification capability can then be

used to collect road segment data from which performance measures such as control

delay (and other measures) can be estimated The purpose of this study is to utilize

wireless technologies to collect vehicle movement data and the focus is not on

obtaining performance measures However to validate the wireless data collection

system proposed in this study the application of the proposed system in obtaining

travel time data over signalized arterial roads and intersection delay were

demonstrated through two case studies Estimating these measures assumes that a

DCU 1 DCU 2 DCU 3

5

sufficient number of travelling vehicles are equipped or contain the wireless

technology that can be wirelessly detected and used as a vehicle identifier

For the intersection control delay application multiple wireless DCUs can be

placed at known distances along a road both before and after an intersection as

depicted in Figure 11 By using multiple DCUs data on vehicle position over time

may be collected for those vehicles containing detectable wireless devices This data

can be used to compute performance measures such as travel times through areas of

interest and can be used to show how congestion builds over time in specific areas

due to non-recurring events

Figure 11 A Set of Three Wireless DCUs Installed at a Signalized Intersection

6

Compared to other technologies that have both vehicle point detection and

identification capabilities (eg video and GPS technology) wireless data collection

units are inexpensive and may be deployed as portable or permanently installed

DCUs Permanently installed DCUs may also be connected to central traffic data

management systems through an existing network infrastructure or through wireless

technologies During this study the concept has been tested through both temporary

and permanent deployment of the wireless-based data collection units

13 RESEARCH CHALLENGES

One characteristic of wireless data collection systems is that they typically have a

large detection range At a roadside DCU a vehicle containing a discoverable

Bluetooth device is often detected multiple times as it travels within the antenna

coverage area of the DCU as depicted in Figure 12 Each detection of the same

device in a vehicle will have a different time stamp When utilized for travel time data

collection this characteristic of vehicle data collected using Bluetooth technology has

been widely acknowledged by several researchers (Van Boxel et al 2011 Tsubota et

al 2011) as the main cause for travel time sample inaccuracy Due to these spatial

errors associated with data collected by Bluetooth DCUs researchers believe that

Bluetooth wireless data collection is not precise enough for travel time data collection

on shorter arterial segments (Saito amp Forbush 2011 Wasson et al 2008) The spatial

errors associated with wireless data collection is the main challenge addressed in this

research

7

Figure 12 Schematic Representation of Bluetooth DCU Detection Range

To develop the point detection capability with wireless DCUs received signal

strength indicator (RSSI) data is utilized Within the Bluetooth technology

communication protocol RSSI data are available at the same time devices are

detected In other wireless platforms such as Wi-Fi ZigBee and DSRC the RSSI

data are also available both during the inquiry process and once a connection (ie

pairing) of devices has been established The key feature of RSSI data that helps

develop point detection capability is the correlation of RSSI values with distance The

basic idea that is expanded upon in this research is that when a vehicle is detected

multiple times as it travels through the DCU antenna coverage area RSSI data can be

8

utilized to identify the detection that corresponds to when the vehicle was the closest

to the DCU

14 CONNECTION TO VEHICLE-TO-INFRASTRUCTURE RESEARCH

The ideas proposed in this research are implemented as a wireless-based Vehicle-To-

Infrastructure (V2I) data collection system that utilizes an existing infrastructure

based on the Bluetooth wireless communications protocol The focus on Bluetooth

technology is due to the presence of many detectable Bluetooth devices that can

currently be found in traveling vehicles Thus research findings can be tested and

utilized on actual roads with varying traffic characteristics Accordingly this research

can also provide more near-term data collection solutions for the types of applications

discussed earlier The research developments will also contribute to the Connected

Vehicle Research initiative for V2I communications (US Department of

Transportation 2010) In the Connected Vehicle Research federal initiative DSRC

wireless technology operating in 59 GHz band is utilized instead of Bluetooth which

operates at 24 GHz Nevertheless the approach and methods developed in this

research are applicable to other wireless technologies and in particular to DSRC

15 RESEARCH CONTRIBUTION

In this research the limits of using a single wireless roadside DCU as a point

detection device for vehicles containing a detectable wireless device are explored

9

The spatial errors associated with wireless data collection from moving vehicles is the

main challenge addressed in this research The approach explored to address these

spatial errors is the acquisition and processing of RSSI data which can be obtained at

the same time device identification occurs The resulting point detection precision

realized is sufficient for a variety of road segment data collection applications This is

demonstrated using Bluetooth wireless technology Multiple DCUs were utilized as

point detection units at a three-way intersection to collect vehicle movement data

Results obtained from the wireless data collection were compared to ldquoground truthrdquo

data obtained from video recordings

Although Bluetooth technology was used as the implementation platform the

approach and principles utilized are applicable to other wireless technologies In

particular the approach of utilizing and processing RSSI data is also applicable to

DSRC wireless technology

The results and approach developed in this research and implemented in

Bluetooth offers a current solution for a low-cost automated data collection system

that is capable of providing data that is unavailable through most current automatic

data collection techniques (with the exception of video image processing and GPS

probe vehicle data collection ndash neither of which is low cost) Data from multiple

Bluetooth-based DCUs can collect sufficiently precise vehicle movement data over

many types of road segments for a variety of purposes With respect to intersections

there are multiple topics in practice and research that would benefit from the

availability of such data Some of these topics are reducing fuel consumption and

10

emissions through traffic signal strategies active traffic management for signalized

networks assessing signal timing and control strategies and intersection performance

and travel time reliability

16 THESIS ORGANIZATION

In the next chapter a summary of the literature relevant to this research is presented

Next the methodologies investigated in this research are explained in detail

including two wireless-based traffic data collection methods A series of test

experiments are conducted to validate the methods proposed in this research and a

summary of the results of these experiments is provided Finally a few examples of

the real-world applications of the proposed techniques are presented

11

2 LITERATURE REVIEW

In this chapter a review of the literature concerning modern traffic data collection

technologies is provided This review focuses on advanced non-intrusive traffic data

collection technologies that do not interrupt traffic during installation andor

operation

First an introduction of traffic performance measures and more specifically

measures of effectiveness applicable to intersections is presented Next a summary of

existing automatic data collection systems is presented with more detail on travel time

and other wireless data collection systems currently in practice Finally a summary of

the connected vehicle research initiative is presented and its relevance to this study is

briefly discussed

21 TRAFFIC MEASURES OF EFFECTIVENESS

The Federal Highway Administration (FHWA) defines a performance measure as the

ldquouse of statistical evidence to determine the progress towards specific defined

operational objectivesrdquo A performance measure provides a basic understanding of

whether different aspects of the transportation system performance are getting better

or worse (Balke et al 2005) For example arterial performance measures obtained

over time can help transportation managers and operators to better evaluate the

effectiveness of signal timing plans and other operational improvements In the long

12

run planning agencies would be able to monitor the arterial network and understand

the effects of changing land uses (Bertini et al 2001)

According to Schrank et al (2007) the public motoring cost during traffic

congestion delay is worth more than a billion dollars in extra travel time extra energy

consumption and extra environmental impacts Signalized intersections are usually

designed with the primary emphasis on minimizing delay Traffic signals when

implemented properly can reduce delay and accidents and improve the quality of

traffic movement (Courage amp Parapar 1975) Signal timing strategies include the

minimization of stops delays fuel consumption and air pollution emissions and the

maximization of progressive movement through a system (Li et al 2004) Improving

signal timing reduces fuel consumption and emissions hence improving air quality

Signal retiming is a process that optimizes the operation of signalized

intersections through a variety of low-cost improvements including the development

and implementation of new signal timing parameters phasing sequences improved

control strategies and occasionally minor roadway improvements Sunkari (2004)

has summarized a comprehensive list of successful examples for signal retiming

projects demonstrating a significant amount of savings in delay time and fuel

consumption at intersections

Researchers use a wide variety of traffic characteristics and performance

measures to study traffic problems For intersections agencies use different

performance measures to quantify the efficiency of roadway systems and evaluate the

movement operations Typical intersection data collection has been limited to

13

volume occupancy travel time and average speed and the assessment has been

conducted off-line (US Department of Transportation 2010) In other words the

researcher collected the data in the field and returned to the office for further data

analysis Therefore there was always a time lag between when the observation was

conducted and when the analysis was completed Recently a trend toward online data

collection techniques has been raised in studies related to roadway and intersection

operations performance (Balke et al 2005 US Department of Transportation 2010

Bonneson amp Abbas 2002) By using real-time traffic data drivers can be kept

updated about the traffic conditions to make their driving safer and more efficient

Shaw (2003) has conducted an extensive survey on arterial performance measures In

his study an overview of the most common arterial performance measures is

presented These measures are summarized in Table 21

Table 21 Selected arterial performance measures

Metric Metric

1 Maximum Speed 10 Queue Length

2 Average Speed 11 Platoon Ratio1

3 Speed Index2 12 Number of Stops

1 Ratio of platoon miles traveled to vehicle miles traveled 2 A ratio that is calculated by dividing a roadway segmentacutes average observed travel speed by the posted speed limit for that segment

14

Metric Metric

4 Density 13 Signal Failure

5 Travel Time 14 Duration of Congestion

6 Travel Time Variance 15 Number of Incidents

7 Average Delay 16 Incidents Duration

8 Maximum Delay 17 Nonrecurring Delay

9 Level of Service (LOS)3 18 Emissions

The traffic performance measures presented in Table 21 are among the most

common metrics used to support decisions that may results in changes to the

transportation system Many transportation agencies have begun to introduce explicit

transportation system performance measures into their policies planning and

programming activities (Pickrell amp Neumann 2001) Depending on the area of use

some performance measures may be more explanatory and useful than others In

addition the metrics presented in Table 21 can be further customized for different

areas of interest Travel time speed and volume are major arterial performance

measures (Wolfe et al 2001) Travel time and volume data collection based on the

reading of time-stamped media access control (MAC) addresses from Bluetooth

3 A performance measure used by traffic engineers to determine the effectiveness of elements of a transportation system

15

enabled devices has been in use for several years (Wasson et al 2008) A basic setup

to collect travel time data via Bluetooth includes a reader unit and an antenna These

two components collectively are referred to as the data collection unit (DCU) With

DCUs placed at different locations along a road segment computing the time-stamp

differences for the same MAC address read at each DCU could generate travel time

samples

211 INTERSECTION PERFORMANCE MEASURES

Engineers and researchers tend to use specific performance measures for different

traffic infrastructures as they are more descriptive to those particular systems In this

section a series of intersection performance measures are explained These measures

are commonly used in studies focused on arterial roads and intersections

As a performance measure delay plays a critical role when evaluating service

level at signalized and un-signalized intersections The average time that vehicles

spend at an intersection is directly related to the average delay for that particular

intersection The 2010 Highway Capacity Manual (HCM) designates average control

delay as the primary performance measure for signalized intersections

(Transportation Research Board 2010) Control delay is used in traffic signal timing

as well as to obtain the level of service (a common intersection measure of

performance) for a particular intersection The Highway Capacity Manual defines

level of service for signalized and un-signalized intersections as a function of the

16

average vehicle control delay This measure is popular since it can be presented to

people without any knowledge in transportation engineering

Two major delay types are defined for intersections stopped time delay and

control delay Stopped delay is defined as the time a vehicle is stopped at an

intersection Control delay is defined as the total delay due to the interaction of

vehicles with the type of control utilized at the intersection and includes deceleration

delay stopped delay and acceleration delay (Quiroga amp Bullock 1998)

Theoretically control delay is measured by comparison with the uncontrolled

condition ie the difference between the travel time that would have occurred in the

absence of the intersection control and the travel time that results because of the

presence of the intersection control Existing techniques for measuring control delay

are inaccurate and time-consuming Therefore control delay is rarely measured

Acceleration and deceleration time and distance data or vehicle trajectories are

other important intersection data and are mainly related to signal timing

performance and safety aspects of the intersection design Acceleration and

deceleration distances and times associated with speed change cycles (stop start and

slow down speed up maneuvers) under normal driving conditions is essential for the

analysis of determining geometric stopped and queuing components of intersection

control delay Vehicle trajectory information is also essential for development and

calibration of simulation models the analysis of operating cost fuel consumption and

pollutant emissions Many efforts have been summarized in the literature to model

acceleration and deceleration profiles (Akcelik amp Besley 2001 Bennett 1994)

17

Unfortunately due to the complexity of such maneuvers the literature on vehicle

trajectories has been very limited Since direct observation and measurement of

vehicle trajectories tend to be inaccurate and impractical the developed models have

been limited to historical or simulation data

In this research the data collection efforts and proposed methodologies are

mainly focused on intersection delay vehicular speed and travel times at signalized

arterial roads Travel time and delay are intuitive performance measures

understandable for both engineers and public road users which can provide valuable

information on the quality of roadway system

22 AUTOMATIC DATA COLLECTION TECHNOLOGIES

To frame the relevance of the proposed research a review of the current automated

traffic data collection technologies and a review of the relevant research literature

were conducted Table 22 summarizes the most common automatic data collection

techniques used in transportation applications

18

Table 22 Most Common Automated Data Collection Technologies Used in Transportation Applications

bull Technology bull Pros bull Cons

Inductive Loop

bull Mature well understood technology

bull Large experience base

bull Provides basic traffic parameters (eg volume presence

occupancy speed headway and gap)

bull Insensitive to inclement weather such as rain fog and snow

bull Provides best accuracy for count data as compared with other

commonly used techniques

bull Common standard for obtaining accurate occupancy

measurements

bull High frequency excitation models provide classification data

bull Installation requires pavement cut

bull Improper installation decreases pavement life

bull Installation and maintenance require lane closure

bull Wire loops subject to stresses of traffic and temperature

bull Multiple detectors usually required to monitor a location

bull Detection accuracy may decrease when design requires

detection of a large variety of vehicle classes

bull Destroyed by utility cuts or pavement milling operations

Microwave Radar

bull Typically insensitive to inclement weather at the relatively short

ranges encountered in traffic management applications

- Direct measurement of speed

- Multiple lane operation available

bull Continuous Wave (CW) Doppler sensors cannot detect

stopped vehicles

19

(Continued)

TECHNOLOGY PROS CONS

Infrared

(Laser Radar)

bull Transmits multiple beams for accurate measurement of vehicle

position speed and class

bull Multiple-lane operation available

bull Operation may be affected by fog when visibility is less

than ~6 m (20 ft) or blowing snow is present

bull Installation and maintenance including periodic lens

cleaning require lane closure

Ultrasonic


bull Capable of over height vehicle detection

bull Large Japanese experience base

bull Environmental conditions such as temperature change and

extreme air turbulence can affect performance

bull Large pulse repetition periods may degrade occupancy

measurement on freeways with vehicles traveling at

moderate to high speeds

Bluetooth

bull Multiple-lane operation

bull Can operate during night time and all weather conditions

bull Non-intrusive

bull Installation and maintenance does not need to interrupt the traffic

bull Cannot capture the entire traffic Can only sample the

vehicles with active an Bluetooth device onboard

bull Spatial errors

20

(Continued)


Video Image

Processor

bull Monitors multiple lanes and multiple detection zoneslanes

bull Easy to add and modify detection zones

bull Rich array of data available

bull Provides wide area detection when information gathered at one

camera location can be linked to another


cleaning require lane closure when camera is mounted over

roadway (lane closure may not be required when camera is

mounted at side of roadway)

bull Performance affected by inclement weather such as fog

rain and snow vehicle shadows vehicle projection into

adjacent lanes occlusion day-to-night transition

vehicleroad contrast and water salt grime icicles and

cobwebs on camera lens

bull Requires15-to21-m(50-to70-ft) camera mounting height (in

a side-mounting configuration) for optimum presence

detection and speed measurement

bull Some models susceptible to camera motion caused by

strong winds or vibration of camera mounting structure

21

As can be seen from the information in Table 22 there are major limitations with

existing technologies in terms of cost flexibility and richness of data that the

technology offers Additionally automatic data collection technologies and

particularly wireless-based systems tend to generate a lot of data However the

output data is not always accurate and useful Therefore to get reliable results from

any automatic data collection system it is necessary to take extra steps before and

after data collection occurs to guarantee the acquisition of quality data For the

purpose of travel time data collection it is necessary to use a series of filtering

techniques to eliminate outlier input data and apply prediction and smoothing

techniques to generate reliable travel time estimates

Among all the existing data collection systems this research is primarily focused

on travel time data collection systems In the next section an overview of the existing

travel data collection systems is presented

221 EXISTING TRAVEL DATA COLLECTION SYSTEMS

Travel time data is one of the most important traffic measures of performance A

wide range of automatic travel time data collection systems have been in practice for

a long time In this section an overview of these technologies is presented

The TransGuide traffic management center monitors traffic operations on a

network of freeways and major arterial streets covering most of the metropolitan area

of San Antonio TX One of the main components of the monitoring system is a series

of sensors (mainly loop detector pairs and sonic detectors) spaced roughly 05 miles

22

apart that provide the capability to measure point speeds Based on these point

speeds the system estimates travel times to specific landmarks and displays the

estimated travel times on dynamic message signs (DMSs) located approximately

every 2 to 3 miles For each pair of adjacent detector stations the system determines

the lowest of the two detector station speeds and assigns that speed to the road

segment that connects the adjacent detector stations The system converts each partial

road segment speed into an equivalent travel time and adds all of the partial segment

travel times to produce an estimate of the total travel time between the DMS location

and the major interchange

Besides TransGuide there are three other major traffic data detection and

archiving programs in Texas TransVista in El Paso TransStar in Houston and

DalTrans in Dallas The TransVista system uses a combination of point loop detectors

and fifty-five cameras to monitor sections of the roadway DMSs and lane control

signs are used to disseminate information about freeway conditions to the travelers

The freeway sections visible through the cameras can be accessed on the Internet

(PBSampJ 2001)

The TransStar system in Houston uses Automatic Vehicle Identification (AVI) to

monitor freeway traffic and disseminate information through popular media outlets

such as radio television and DMSs Primary information transmitted are travel time

estimates and speed data Vehicles with transponders are used as probes AVI readers

are installed along freeways to detect when the probe vehicles pass the point of

23

installation The time difference between detection of a probe vehicle at successive

AVI reader locations is used to arrive at travel time estimates and speed

The DalTransTransVision system in Dallas uses a combination of loop detectors

and closed circuit cameras to provide information about incidents lane closures and

speeds in the Dallas and Fort Worth areas The information is provided to the public

using DMSs or it can be accessed on the Internet

The Florida Department of Transportation (DOT) collects real-time data on a 40-

mile segment on the I-4 corridor near Orlando using loop detectors (coupled as dual-

loops) A nonlinear time series model developed at the University of Central Florida

was used to predict travel times This forecasted travel time information was

transmitted to the public through a website The Wisconsin DOT provides an estimate

of current travel times on the I-94 I-894 and I-43 corridors near Milwaukee using

inductive loop detectors and freeway cameras

In California a study is underway to start using remote sensor nodes to monitor

traffic Several magnetic sensors are placed in or above the road These sensors detect

presence of vehicles by measuring the change in the magnetic field produced above

the sensor and transmit the data back to an access point The system is controlled by

an energy saving protocol called PEDAMACS This protocol controls the data

transfer between the access point and the sensors via radio signals in order to

minimize the time the sensors are functioning When the sensors are not needed they

go into sleep mode to save energy The access point which can be placed adjacent to

24

the road can be accessed by the transportation management centers to remotely

obtain data and control the nodes

Traffic sensors are the most critical elements of an Intelligent Transportation

System (ITS) Different types of traffic sensors with different qualities are available

However existing systems are expensive and require a high level of maintenance

Neal and Yance (2005) developed the Advanced Re-locatable Traffic Sensor (ARTS)

system for use in construction zones and incident management The prototype smart

sensor system developed is highly portable and equipped with a wireless

communication subsystem The system enables real-time data acquisition processing

and communication to a Traffic Management Center (TMS) for appropriate actions

The ARTS system is built of several components that make the overall functionality

of the system possible including a Doppler microwave radar a digital compass a

GPS positioning subsystem a satellite packet data terminal and an on-board

computer system The unit is powered up by a solar portable system The unit is

capable of accurately measuring traffic counts speed volume and headway but is

much more costly than the concept proposed in this research

A considerable amount of research has addressed the use of Bluetooth-based data

collection systems on highways and arterial roads In recent years the use of time-

stamped media access control (MAC) address data acquired from Bluetooth-enabled

devices to collect travel time data has received significant attention in the past few

years In the next section the Bluetooth technology is briefly introduced and a

25

summary of the Bluetooth-based data collection systems is provided The Bluetooth

technology is later revisited with great detail in Chapter 3

23 BLUETOOTH TECHNOLOGY

Bluetooth is a short-range wireless communications protocol operating in the license-

free 24 GHz frequency band In contrast to Wi-Fi which offers higher transfer rates

and distance Bluetooth is characterized by its low power requirements and low-cost

transceiver chips The Bluetooth technology is embedded in many devices such as

cellphones smartphone and PDAs laptop computers and tablets Bluetooth hands-

free sets and speakerphones (Jawanda 2012) ABI Research a market intelligence

company specializing in global technology markets recently reported that it expects

cumulative Bluetooth device shipments to reach 20 billion by 2017 (ABI Research

2012)

231 BLUETOOTH-BASED DATA COLLECTION SYSTEMS

The research conducted by Young (2007) is one of the first studies where Bluetooth

technology was utilized for the acquisition of traffic data In this study MAC

addresses were used as a unique identification number to tag vehicles in conjunction

with a time stamp for a known location These time-stamped data were later paired

with similar data collected elsewhere The differences in time between detections and

the distance between collection locations were used to calculate a space mean speed

26

Wasson et al (2008) reports on the results of a study conducted by the Indiana

Department of Transportation and Purdue University where several Bluetooth data

collection units were used for several days to collect time-stamped MAC addresses

along a 10-mile corridor of I-65 near Indianapolis Indiana The road segment where

the MAC address data were collected included both signalized arterials and interstate

highway segments to demonstrate the use of Bluetooth-based data to obtain travel

time information The results of the study showed that arterial data have a larger

variance due to the impact of signals and the noise that is introduced when the

vehicles constantly enter and leave the arterial

Tarnoff et al (2010) compared data from GPS-equipped probe vehicles to

Bluetooth-based data collected for the same routes They concluded that the travel

times calculated from the Bluetooth data are very close to freeway GPS data

However due to low market penetration of the Bluetooth enabled devices the study

population is smaller than the data provided by third party companies for GPS-

equipped vehicles Haghanhi et al (2010) who also worked on the same corridor as

Tarnoff et al (2010) also reported the same issues when using the Bluetooth-based

data collection approach

Quayle at al (2010) studied arterial travel times in Portland Oregon Using

several Bluetooth data collection units data were collected for 27 days along a 25-

mile signalized suburban arterial route in addition to data from GPS floating car data

for one day They concluded that the travel times calculated based on Bluetooth-

based data were close to the GPS floating car data (with an average error of 1525

27

seconds) and that Bluetooth offers a low cost technique for collecting data that can

reduce the amount of needed manual work

Tsubota et al (2011) performed arterial traffic congestion analysis using the

Bluetooth duration data The research proposed the idea of utilizing the duration data

(ie the time it takes Bluetooth devices to pass through the detection zone of

Bluetooth scanners) to derive intersection performance measures at signalized

intersections The research team however has not reported any experiments to

further validate and compare the results with real world data The research also

acknowledged the presence of various traffic modes and need for filtering unwanted

samples from the arterial traffic

Young (2012) conducted a study on Bluetooth traffic monitoring technology for

use as permanent sensors delivering travel time data on Maryland freeways To

validate the accuracy of Bluetooth-based travel times the data were compared to the

data obtained from automatic traffic recorder systems installed on US route 29 west

of Baltimore MD as well as the travel times obtained from the toll tag system on a

section of I-80 in San Francisco CA

The city of Houston TX in collaboration with the Texas Transportation Institute

(TTI) conducted several projects to demonstrate the use of Bluetooth-based data

collection systems to estimate urban travel times (Puckett amp Vickich 2010) The

researchers concluded that Bluetooth-based traffic data collection technology is a

viable option for the collection of travel time data Based on an assumption of a single

Bluetooth source per vehicle the researchers claim to have captured 11 of the

28

traffic volume with their Bluetooth DCUs in Houston Texas The researchers also

showed that their Bluetooth-based travel time estimates are comparably close to

travel time estimates calculated using toll tag data

Bullock et al (2009) extended the use of Bluetooth-based data collection

technology to applications other than roadside travel time data In their research

Bluetooth-based data were used to estimate passenger delays at queues for security

areas of the Indianapolis International Airport Short range (Class II) Bluetooth

modules were placed inside enclosures on both the upstream and downstream sides of

a security checkpoint The selection of Class II transceivers was due to a desire to

minimize the variations in travel times detected due the close proximity of the two

detection units inside of the airport terminal The researchers concluded that the

number of Bluetooth sources recorded corresponded to a range from 5 to 68 of

passengers assuming one Bluetooth-enabled device per passenger only The research

has captured the changes in travel times passing through the security to be correlated

with the number of passengers screened at the checkpoint However ground truth

travel time data for passenger transit times through security were not available for

comparison

Day et al (2009) investigated the potential of using Bluetooth-based data to

estimate delays at construction work zones in real-time Their study was conducted

over a period of twelve weeks on a rural interstate work zone in Northwest Indiana

The researchers showed that by presenting the motorists with the Bluetooth-based

travel time data for both the main segment and the temporary detour they were able

29

to show that more motorists utilized the detour route than when no travel time data

were provided Day et al (2010) utilized travel times collected using Bluetooth

technology to measure the effectiveness of signal timing Barcelo et al (2010)

utilized the travel time data to predict short-term travel times using Kalman filtering

No papers utilizing multiple Bluetooth-based DCUs in the same area and

triangulation to estimate vehicle location have been found

232 BLUETOOTH SIGNAL DISTANCE-SIGNAL STRENGTH

RELATIONSHIP

A number of researchers have examined the use of Received Signal Strength

Indicator (RSSI) data from Bluetooth devices as a means to provide location

information in indoor and outdoor environments These studies mainly try to

accurately locate a signal source by processing the signal strength data At the time

this research was conducted no other study could be found on utilizing signal

strength data to enhance traffic data collection In general researchers have followed

two different approaches to obtain distance measures from RSSI readings The first

approach uses empirical data to map RSSI values to specific locations in the

environment under study An example of this approach is the work performed by

Feldmann et al (2003) where a regression model was deployed to estimate the

distance for the observed RSSI values The second approach utilizes radio frequency

propagation models as a basis for distance estimation Kotanen et al (2003) and Fink

et al (2009) are examples of such work

30

Ahmed et al (2008) reported on a prototypical proof-of-concept implementation

of a Bluetooth and wireless mesh networks platform for traffic network monitoring

In this study Bluetooth detection data are used to obtain travel time and speed

samples To improve the accuracy of the results the system takes advantage of RSSI

data collected during the inquiry process The basic idea behind this system is that

Bluetooth devices will generate stronger signal strength when they are closer to a

receiver The study did not specifically mention any details about the procedures and

routines used to obtain RSSI data or how the RSSI data were processed According to

their test results in some cases the system failed to correctly predict the location of

the vehicle when it passed a detection unit The noisy nature of the RSSI values and

the simplicity of the time stamp selection algorithm used could have affected the

results

24 CONNECTED VEHICLE RESEARCH INITIATIVE

The US Department of Transportation (USDOT) initiated the Intelligent

Transportation Systems (ITS) program to solve transportations issues related to

safety mobility and environment friendliness by integration of intelligent vehicles

and intelligent infrastructure The goal of the Connected Vehicle Research initiative

(previously known as the IntelliDrive program) is to provide connectivity between

vehicles infrastructure and passenger wireless devices to ensure safety mobility and

environmental benefits (US Department of Transportation 2010) The wireless technology

designed for this type of automotive connectivity purposes is known as Dedicated

31

Short Range Communications (DSRC) DSRC operates on the 59 GHz frequency

band and has been assigned by the USDOT for the use of automotive safety

applications (US Department of Transportation 2010) DSRC is a secure and

reliable form of communication making it the focus of many vehicle-to-vehicle

(V2V) or vehicle-to-infrastructure (V2I) applications In the ITS strategic plan the

USDOT is committed to use wireless technologies such as DSRC for active safety in

both V2V and V2I applications (Amanna 2009) The ITS program has identified the

following areas to focus research activities of the connected vehicle research (Row et

al 2008)

Technology scanning and research to identify and study a wide range of

potential technology solutions

Research demonstration and evaluation of technology-enabled safety

applications

Establishment of test beds to support operational tests and demonstrations

for public and private sector use

Development of architecture and standards to provide an open platform for

wireless communications between vehicles and roadside infrastructure

Study of non-technical issues such as privacy liability and application of

regulation

Research on ancillary benefits to mobility and the environment

32

In general the Connected Vehicle Research program is mainly focusing on crash

prevention and efficiency improvement through the integration of intelligent vehicles

and intelligent infrastructure The ultimate goal of the project is to provide an

infrastructure where vehicles can identify threats and hazards on the roadway and

communicate this information over wireless networks to alert and warn other drivers

Over the last five years various states have been participating in this program and

among those California Michigan and Arizona have initiated major projects (US

Department of Transportation 2010)

In response to the USDOT ITS program initiation several protocols and programs

for a portable DSRC system have been proposed Maitipe and Hayee (2010) have

presented a study to develop implement and demonstrate a DSRC-based V2I

information relay system for improving traffic efficiency and safety in the work-zone

related congestion buildup on US roadways Their study included two sets of road

side units (RSU) and on-board units (OBU) The RSU devices are portable systems

that can be installed temporarily near work zones The RSU unit plays the role of a

central dispatcher for a series of OBUs in the range When a vehicle equipped with an

OBU approaches the work zone traffic data will be transmitted to the RSU and after

data analysis by RSU information will be broadcasted to all OBU devices in range

that have not reached the work zone Jang et al (2010) have proposed a smart

roadside system providing driver assistance and traffic safety warnings Wang (2009)

has presented an Intellidrive application for signalized intersection safety where

signals can dynamically respond to hazardous conditions to avoid red-light running

33

related collision The proposed collision avoidance system is based on a model for

red-light running prediction with Intellidrive V2I communications that is specially

designed for all-red extension for IntelliDrive-enabled vehicles to improve red-light

running prediction

The great advantage of these DSRC-based systems is the ability of two-way

communication between OBUs within one-kilometer range and RSUs RSUs transmit

data for all OBUs in the proximity However the major drawback of this system is

how to retrofit the OBUs to all existing users Thus only vehicles equipped with

OBUs (test units) can benefit from this system at this time which is a very small

portion of the traffic

34

3 METHODOLOGY

In this chapter the approach and methods utilized to collect vehicle movement data

over time with wireless data collection units are presented The methods presented in

this chapter can be implemented using a wide range of wireless networking protocols

including Wi-Fi DSRC Bluetooth and ZigBee Bluetooth was selected as the

implementation platform due to the current use of Bluetooth devices in vehicles

today thus allowing real-world testing to evaluate the methods developed A detailed

introduction of the Bluetooth technology is provided in section 31

Two different approaches were explored that differ with respect to the nature of

the vehicle movement data sought and the usefulness of the results obtained The

first approach is wireless signal trilateration The objective of this approach is to use

wireless vehicle identification and signal strength data to dynamically collect vehicle

position data within the discovery range of the data collection units The objective of

this approach is to collect data that can be used to identify a vehiclersquos trajectory over

time and hence could also be used to extract several intersection performance

measures such as intersection control delay and accelerationdeceleration times The

results obtained did not provide the accuracy and consistency needed to identify a

vehiclersquos trajectory over time However the presentation of the methodology

identifies current data collection limits with the wireless technology utilized

The second approach seeks to utilize the data collection units as point detection

systems Wireless vehicle identification and signal strength data are used to estimate

35

when a vehicle has passed an imaginary line extended from the data collection unit

across the road By utilizing a series of data collection units that are separated by

known distances along a road segment the objective is to accurately estimate travel

times between any pair of units This information can be used to estimate other useful

performance measures such as average control delay and the average time spent at an

intersection A significant challenge in this approach is to address the large coverage

area of the data collection units and the resultant potential spatial errors when

utilizing the data collection units as point detection units

This chapter begins with a presentation of needed background information First

an overview of Bluetooth technology is presented which also includes a description of

the Bluetooth scanning or inquiry procedure Relevant signal propagation concepts

are then introduced The application of these concepts to obtain vehicle movement

data and accurate travel time data is investigated using two proposed approaches In

the first approach trilateration concepts are used to collect vehicle location data In

the second approach signal strength data are utilized to estimate the time that

vehicles pass a Bluetooth-based data collection unit The data collection approaches

are explained in detail in separate sections and further validation tests are presented


The Bluetooth Special Interest Group (SIG) first published the Bluetooth wireless

communications protocol in 1998 By 2010 over 13000 companies had joined the

SIG including almost every major commercial electronics manufacturer The

36

Bluetooth protocol includes specifications for the frequency (spectrum) utilized

interference handling range and power consumption of the technology The SIG

created three Bluetooth classes that differ with respect to the distance that

communications between devices was intended to work reliably (see Table 31) For

this research a Class I Bluetooth module was used to achieve longer ranges and more

reliability

Table 31 Bluetooth Classifications

Bluetooth Classification Effective Range

Class I 300ft

Class II 33ft

Class III 3ft

In this study Bluetooth technology has been used to build a data collection unit

(DCU) The DCU is a device that identifies Bluetooth-enabled devices within its

discovery range by continuously performing a ldquoBluetooth inquiry processrdquo that seeks

to identify other Bluetooth devices with communications range Since each Bluetooth

device has a unique MAC address the DCU can distinguish between different

Bluetooth devices and therefore different vehicles that house these devices as they

travel The inquiry process is a complex procedure that is explained in detail in the

Bluetooth specification documents published by Bluetooth Special Interest Group A

brief summary of this procedure comes next

37

The Bluetooth protocol implements a master-slave structure (similar to a server-

client structure in a LAN network) When a master device wants to initiate a

connection it first performs a process that searches for other discoverable master and

slave devices in range In the Bluetooth specification this scanning procedure is

referred to as the inquiry process In this research the data collection unit operates as a

master device and is programmed to continually repeat the inquiry process while also

never establishing a connection with other slave devices The Bluetooth inquiry

process follows a series of steps defined by the protocol in which a master device

attempts to detect other devices responding to an inquiry message The specific

mechanism by which Bluetooth devices identify and establish communication with

multiple Bluetooth devices is called frequency hopping and is probabilistic in nature

Therefore the inquiry process may not detect all Bluetooth devices within

communication range of the master device To increase the chances of detecting all

possible Bluetooth devices nearby the inquiry process can be repeated multiple

times For more details on the Bluetooth inquiry process see Appendix

32 TRILATERATION APPROACH TO ESTIMATE VEHICLE MOVEMENT

The first approach investigated in this research was to use a trilateration technique to

estimate vehicle movement through an intersection covered by at least three DCUs

From a mathematical perspective if the distance of an object to at least three different

locations is a known then there is a unique location in three-dimensional space where

the object must reside Trilateration is the process of solving for this location (the

38

technique is also used in GPS to determine location) By using multiple DCUs and

trilateration the objective is to collect vehicle position data over time through the

DCU coverage area for those vehicles containing active Bluetooth devices

Since the trilateration approach is based on distances to known locations it is

necessary to estimate the distance between data collection units and the vehicle (with

active Bluetooth device on board) from signal strength data The details of Bluetooth

signal strength acquisition are presented in section 321 Next the conversion of

signal strength data into distance estimates using a signal propagation model is

presented The accuracy potential of the trilateration approach is demonstrated

through a field experiment Prior to the accuracy assessment the environmental power

decay factor of the signal propagation model was estimated from signal strength data

collected from Bluetooth devices at specific locations relative to three DCUs The

accuracy of the trilateration approach was then evaluated using new random

Bluetooth device locations

321 BLUETOOTH SIGNAL STRENGTH ACQUISITION

In the literature of the Bluetooth data collection systems two types of possible

methods for acquiring the Bluetooth received signal strength information (Received

Signal Strength Indication or RSSI) have been proposed [Bluetooth Special Interest

Group 2011 Bluetooth SIG 2004] the connection-based method and the inquiry-

based method In the connection-based method a communication connection between

the DCU and a mobile Bluetooth device must be established before signal strength

39

measurements can be obtained In a peer-to-peer connection mode signal strength is

much easier to obtain than extracting inquiry based signal strength That is because all

Bluetooth software (or more accurately Bluetooth drivers) is supplied with a large

library of ready-to-use functions that can be called within a Bluetooth connection

Among these ready-to-use functions there is a function that returns the signal

strength for the active connection The problems with connection based signal

strength are response time and the requirement for authorization to establish a

connection before obtaining signal strength information In addition on many

Bluetooth devices the transmission power adjustment which is used to adjust power

usage based on signal strength values eliminates the correlation between the signal

strength measurement and the distance between a mobile Bluetooth device and the

DCU The inquiry-based method retrieves the RSSI measure from the inquiry

response without establishing any connection to the mobile Bluetooth device The

RSSI can be obtained during the Bluetooth inquiry procedure at the same time that

the MAC address is read and thus does not add any additional processing andor

delays when reading MAC addresses (Almaula amp Cheng 2006) Therefore the

inquiry-based method is used to obtain RSSI data for distance estimation process in

this study

322 ESTIMATING DISTANCE FROM RSSI

The basis for almost all signal localization methods is the Received Signal Strength

Indication (RSSI) RSSI number is a unit of measurement that represents the radio

40

power level received at a destination RSSI is usually presented in units of energy

which can be both absolute (mW) and relative (dBm) representations Absolute power

of a signal is measured in wattage (W) The Bel or Decibel system can only describe

relative power For example a gain of 3 dB means the signal is 2 times as strong as it

was before but the dB scale does not define the amount of power transmitted at

source The dBm system instead indicates the power relative to 1 milliwatt of power

This conversion can be done using x = 10 logଵ(P) where x is power in dBm and P is

power in milliwatt In order to use signal strength for localization RSSI values must

be converted to accurate distance estimates In the literature two main approaches

have been used to obtain distance measures from RSSI values The first approach

uses an empirical method to map RSSI values to specific locations in the environment

under study A dense population of locations ensures a rich sampling of the RSSI

throughout the environment (Awad et al 2007 Almaula amp Cheng 2006 Feldmann

et al 2003) This method requires a location-RSSI map preparation stage and a

developed map cannot be applied to a different location Additionally this method is

only applicable to the devices and location used to develop map of RSSI values

Therefore this method is not a good candidate for the purpose of this project The

second approach utilizes a radio signal propagation model There is an extensive body

of literature devoted to understanding and modeling radio signal propagation

(Kotanen et al 2003 Fink et al 2009 Thapa amp Case 2003) An overview of the

signal propagation model used for this research is provided in this section In this

approach power loss throughout the environment is computed as a function of the

41

distance between antennas and fit to the entire environment by a power decay factor

so that the amount of loss (in milliwatt) can be measured (Fink et al 2009)

= ( ఒ )ଶ Equation 31 ସగௗ

10 logଵ 119875 minus 10 logଵ 119875௧ = 20 logଵ ൬ 120582 4120587൰ + 10119899 logଵ

1 119889

Where P(t) is the signal strength received at a distance d and P(t) is the signal

strength transmitted presented in mW λ is the wavelength The factor n represents the

path loss exponent (aka environment power decay factor) and is affected by the

external factors like multi-path fading absorption air temperature etc The

propagation model presented in Equation 31 can be generally used for any signal A

log10 was applied to both sides of the model presented in Equation 31 to work with

dBm (see Equation 32 for the results)

The signal propagation model used for this study (presented in Equation 32) is

based on the work completed by Morrow (2002) This model was utilized to translate

RSSI levels into distance estimates In this model power loss throughout the

environment is computed as a function of the distance between antennas of two

communicating devices and is adjusted for a particular environment by an

environment power decay factor

Equation 32

Where PL is the received power level relative to the transmitted power (RSSI

explained in units of dBm) λ is the Bluetooth wavelength in meters n is the

environment power decay factor and d is the distance at the receiversrsquo location (for

42

Bluetooth λ = 0122 meters is used) Numerous environmental factors can be

introduced into a signal propagation model such as Doppler effect multi-path fading

etc These factors can quickly increase the complexity of the model and hence reduce

its usability By considering λ = 0122 and solving equation 32 for d the signal

propagation model can be formatted as Equation 33

షరబమఱళ

d = 10 భబ Equation 33

In addition to the theoretical model presented in Equation 33 several other

empirical models were tested Empirical models were examined to check if other

nonlinear models offered a better fit than the signal propagation model An example

of one empirical model is presented in Equation 34

d = A times PL Equation 34

Where A and B are parameters of the empirical model

The parameters for different signal propagation models were fit to data generated

by obtaining signal strength readings from Bluetooth devices placed at known

distances to multiple DCUs Details of this data acquisition are presented in section

325 The Parani UD-100 Bluetooth modules utilized to generate this data report

RSSI levels in units of dBm Since the power level transmitted by most of

commercial Bluetooth-enabled devices (such as cellphones headsets PDAs etc) is

about 0 dBm (1 milliwatt) the RSSI level received at the scanner device can be used

as PL By knowing the received RSSI level and having a proper environment power

decay factor one can use the propagation model presented in Equation 33 to

calculate the distance estimate

43

The value for the power decay factor was determined empirically from generated

data A meta-heuristic optimization algorithm called particle swarm optimization was

utilized to compute the value of this parameter This method was applied to data

generated by placing Bluetooth devices at random positions with known distances to

fixed DCU locations and recording the RSSI levels received from the Bluetooth

devices (18 samples for each of the two test cell phone) The particle swarm

optimization in this study tries to find the best value for the environment power decay

factor (n) so that when the sample pairs of distance-RSSI are inserted into the

propagation model the total squared distance error is minimized An overview of

meta-heuristic algorithms and specifically the particle swarm optimization method is

presented next

323 PARTICLE SWARM OPTIMIZATION

Particle swarm optimization is a general class of metaheurstic algorithms A

metaheuristic refers to a computational method that attempts to optimize a problem

iteratively by following a general search strategy Metaheuristic algorithms are

heuristics in that in most cases optimality is not guaranteed but they have been

successfully applied to many real combinatorial and non-linear optimization

problems Metaheuristics algorithms can often generate very good solutions to

difficult optimization problems in a reasonable amount of time

Particle swarm optimization (PSO) was first introduced by Kennedy and Eberhart

and was first intended for simulating social behavior (Kennedy amp Eberhart 1995)

44

Kennedy and Eberhartrsquos work was originally influenced by earlier research completed

by Heppner and Grenander about bird flocks searching for corn (Heppner amp

Grenander 1990)

In PSO a number of entities which are referred to as particles are initialized in

the solution space of a problem or function Each particle will give an objective

function value (Poli et al 2007) In each iteration every particle moves through the

search space by combining some aspect of the history of its own current and best

locations with that of other particles with some random perturbations The next

iteration takes place after all particles have been moved Eventually the swarm (all of

the particles) as a whole like a flock of birds collectively foraging for food is likely

to move close to an optimal value A wide variety of PSO algorithms have been

proposed and the specific PSO algorithm implemented is presented next

The particle swarm optimization starts with a random value for the environment

power decay factor and then begins an iterative process to improve this value For this

study the algorithm initialized 100 random values for the environment power decay

factor (n) Each of these values which can be a candidate solution for n is called a

particle It is important to mention that the algorithm has no knowledge of the

underlying propagation model which helps to build the objective function for this

study Thus it has no way of knowing if any of the candidate solutions are near to or

far away from a near-optimum solution The algorithm simply uses the objective

function to evaluate its candidate solutions and operates upon the resultant fitness

values that is the difference between the estimated distance using an RSSI sample and

45

a random value for n in the propagation model and the actual distance for the

corresponding RSSI The algorithm maintains the sum of squares for all distance

errors and tries to minimize this value by improving the particlersquos locations The

algorithm keeps track of the best value for each particle (local optimum) and for the

entire particles population (global optimum) to move toward the best fitness value

The steps of a basic particle swarm optimization algorithm that were followed to

estimate the environment power decay factor n in the propagation model

1 Start with an initial set of particles typically randomly distributed

throughout the design space In this study particles are random values for the

environment power decay factor n in the signal propagation model For this

study a number of 100 particles were initialized with random starting values

The uniform random number generation method in visual basic was utilized to

initialize the particles

2 Calculate a velocity vector for each particle in the swarm The velocity and

position update step (step 3) are responsible for the optimization ability of the

algorithm The velocity of each particle is updated using the following

equation

119907(119905 + 1) = 119908119907(119905) + 119888ଵ119903ଵ[119909ො(119905) minus 119909(119905)] + 119888ଶ119903ଶ[119892(119905) minus 119909(119905)]

i is the index of the particle 119907(119905) is the velocity of particle i at time t 119909(119905)

is the position of the particle i at time t The parameters w 119888ଵ and 119888ଶ are user-

supplied coefficients ( 0 ≦ 119908 lt 12 0 ≦ 119888ଵ ≦ 2 0 ≦ 119888ଶ ≦ 2 ) 119909ො(119905) is the

46

individual best candidate solution for particle i at time t and g(t) is the

swarmrsquos global best candidate solution at time t These parameters were also

initialized randomly For each set of candidate solutions (aka particles)

fitness evaluation is conducted by supplying the candidate solution to the

signal propagation model Pairs of collected Distance-RSSI data are provided

to the algorithm for the fitness evaluation process For each iteration the

estimated distance is compared to the actual distance to compute the fitness

value

3 Update the position of each particle using its previous position and the

updated velocity vector to reduce the total fitness values Once the velocity for

each particle is calculated each particlersquos position is updated by applying the

new velocity to the particlersquos previous position

119909(119905 + 1) = 119909(119905) + 119907(119905 + 1)

4 Go to Step 2 and repeat until total fitness values converge to zero

The algorithm presented in this section was implemented using Microsoft Visual

Basic The algorithm was replicated a few times with particle swarm sizes of 100 and

1000 Each replication of the algorithm requires several minutes to complete and R^2

values of higher than 090 were achieved

324 SIGNAL LOCALIZATION APPROACHES

Triangulating an objects position is a method of determining the location of the

object based on its distance relative to other known locations or signal sources In

47

general there are two triangulation approaches In the first approach that is usually

referred to as triangulation knowing the coordinates (xy) of the three stations and the

distances (r) from the stations to the location of the object it is easy to find the circle

equations that represent all potential points for the location of the object relative to

those three reference points To find a single point that represents the actual location

of the object one needs to solve the three simultaneous circle equations (see Figure

31) However it is very difficult to solve these equations since they are nonlinear

Therefore there is a need for a simpler method If one pair of equations for the circles

are taken and subtracted one from the other the result will be the equation of the line

passing through the two points of intersection of the two circles (see Equation 35)

ଶ

ቊCircle a (x minus xୟ)ଶ + (y minus yୟ)ଶ = rୟ Equation 35 Circle b (x minus xୠ)ଶ + (y minus yୠ)ଶ = rୠଶ

ଶCircle b minus Circle a 2x(xୟ minus xୠ) minus ൫xୟଶ minus xୠଶ൯ + 2y(yୟ minus yୠ) minus ൫yୟଶ minus yୠଶ൯ = rୠଶ minus rୟ

The big advantage being that the result is therefore a linear equation which is

much easier to deal with Next by subtracting any other two of the three circle

equations a second line equation would be obtained The intersection of these two

lines is the location of the object Both line equations will pass through two points of

intersection between each pair of circles and one of these intersection points is the

point that the object is located at

48

Figure 31 Intersection of Three Circles at a Single Point

The triangulation method explained so far is very simple and works as long as

these three circles really intersect at a single point However that is not always the

case In signal propagation the distance estimates always have some error which

prevents the three circles from intersecting at a single point Thus there would be a

need for a different algorithm to handle such errors and still be able to estimate the

location of the object To mitigate the errors resulting from the propagation model a

trilateration process can be utilized In geometry trilateration is an iterative process to

determine absolute or relative location of an unknown point by measurement of

distances from a number of known points using the geometry of circles andor

spheres Trilateration is extensively used in commercial navigation applications

including Global Positioning Systems (GPS)

49

In the case of a two-dimensional problem when it is known that a point is located

on at least three curves such as the boundaries of three circles then the circle centers

and the three radii provide sufficient information to narrow the possible locations

down to one location However if these three circles do not intersect on a single point

an iterative process can be used to relatively scale the circles radii to force these three

circles to intersect at one point This extra step is needed when errors resulting from

the inaccuracy of the propagation model are present Both algorithms explained in

this section are implemented using Python language and are presented in the

Appendix section B

325 TRILATERATION STUDY TO DEVELP A SIGNAL PROPAGATION

MODEL

For this experiment three Bluetooth scanner devices were used Bluetooth scanners

were attached to 24 GHz omnidirectional antennas to match the setup configuration

implemented for a research project conducted for Oregon Department of

Transportation A large parking lot on Oregon State University campus was selected

to conduct this experiment Figure 32 illustrates the test site for this experiment The

antennas were placed in an lsquoLrsquo shape configuration 10 meters separated from each

other (see Figure 33 for setup arrangement)

50

Figure 32 Trilateration Test Experiment Setup

Figure 33 Scanners Arrangement in an L Shape

51

For this experiment a stratified random sampling approach was selected to evenly

distribute sample locations across the discovery range of the units The discovery

range (which represents an imaginary intersection with its four approaches) was

divided into 9 different areas Defined areas (numbered from 1 to 9 in Figure 33)

were sorted in a random order and for each area two random samples were collected

Pairs of locations (x y) were generated randomly for each defined area To facilitate

the test process Table 32 was developed For each row in this table two

experimental Bluetooth-enabled cell phone devices were placed at the location noted

on column three Next signal strength levels from all three Bluetooth scanners were

measured and recorded (ie RSSI 1 RSSI 2 and RSSI 3) The signal propagation

model was calibrated based on the collected data

Table 32 Random locations selected for Test Cell Phone 1

Region Location ( x y ) RSSI 1 RSSI 2 RSSI 3

1 7 -4 22 -79 -86 -58

2 1 1 2 -52 -70 -66

3 8 11 21 -69 -69 -68

4 5 10 6 -61 -59 -70

5 4 2 11 -57 -73 -62

6 5 13 12 -68 -61 -71

7 7 -2 16 -72 -71 -62

8 1 -1 1 -60 -74 -63

52

9 9 19 23 -70 -59 -73

10 3 22 2 -81 -57 -86

11 8 7 23 -69 -73 -65

12 2 12 0 -60 -52 -72

13 4 3 9 -55 -73 -61

14 6 16 10 -64 -59 -70

15 6 24 13 -80 -62 -73

16 2 11 2 -63 -58 -78

17 3 18 0 -65 -44 -68

18 9 19 18 -77 -67 -72

326 FITTING THE ENVIRONMENT POWER DECAY FACTOR

Using the data collected during the study presented in section 325 and by utilizing a

particle swarm optimization a number of mathematical models were developed to

describe the signal propagation phenomena (See Table 32 for the data) As it was

explained earlier these models include the theoretical signal propagation model based

on Morrowrsquos work (2002) and a few empirical models For each mode an R-square

value was calculated based on the data used to fit modelrsquos parameters Higher R-

square values indicate that the model (and its parameters) do a better job in

converting RSSIs into distance estimations Among these models however the model

based on the theoretical power loss model generated the highest level of accuracy

53

Estimated Distance Error

0 20 40 60 80

The propagation model equation with its calibrated parameters is presented in

Equation 36 that has a value of 168 for n

RSSI = 168 logଵ(d) + 346 Equation 36

As a part of the model development process the distance estimates for each

sample location based on the recorded RSSI levels were compared to the actual

distance from the sample location to the DCUs Figure 34 illustrates actual and

estimated distances versus recorded RSSIs In the figure actual distance samples for

each random location is marked using dots for each test cell phone

50

0

-50

Error

(dis

tance) -100

-150

-200

-250-

-300-

Estimated Distance

Error

RSSI-

Figure 34 Error Comparisons for Estimated Distances Using the Developed Signal

Propagation Model for Both Test Cellphones

100

54

327 ACCURACY ASSESSMENT

Next a second field test was conducted to evaluate the accuracy of the developed

signal propagation model This experiment was completed at the same test site with

similar setup configuration An assessment of the propagation model (Equation 36)

accuracy was conducted using a number of location-RSSI pairs The propagation

model developed in previous step was used to estimate the location of the Bluetooth

device merely based on the RSSI levels recorded on three DCUs In this step the

RSSI values recorded from three DCUs were translated into three distance estimates

Finally the iterative trilateration algorithm was utilized to obtain relative location

estimates for each sample reading The location estimations were compared against

actual locations and the distance between these two points were used as the error

value The results are summarized in Table 33 The first two columns show the

location of the test cell phone relative to the antenna 1 (see Figure 33) The next three

columns show the distance estimates from each antenna using the recorded RSSI

Columns six and seven show the estimated location using the iterative trilateration

technique The last column represents the Euclidean distance between the actual

location and the estimated location that serves as the error

55

Table 33 Signal Propagation Model Accuracy Test Results

Actual Location (xy) Estimated Distances (m) Predicted Location Linear

Distance -4 22 235479 266011 128617 69664 169191 12086 1 2 105718 166782 166782 63365 633656 6876 11 21 227846 250745 113351 76476 178288 4614 10 6 98085 128617 13625 80814 753122 2454 2 11 174415 189681 90452 85775 157753 8128 13 12 174415 174415 128617 99999 132830 3262 -2 16 281277 296543 159149 83619 188778 10754 -1 1 159149 197314 143883 71398 109409 12848 19 23 204947 159149 182048 13624 119434 12293 22 2 189681 143883 182048 13391 106354 12193 7 23 250745 281277 143883 69750 169301 6069 12 0 113351 5992 220213 12670 036762 0765 3 9 143883 189681 113351 63983 118166 4413 16 10 182048 159149 120984 12067 149317 6307 24 13 235479 113351 189681 23794 16048 3054 11 2 113351 75186 151516 12333 693284 5109 18 0 159149 44654 21258 16590 468432 4891 19 18 243112 220213 159149 12275 170651 6788

Ave 6828 STD 3763

328 CONCLUSIONS AND LIMITATIONS

Although the estimated locations obtained through the trilateration method are close

to actual locations on average the test results are not satisfactory on an individual

case basis In those cases with large errors the predicted locations are a few meters

off from the actual location of the test cell phones

There are several sources of inaccuracy in the environment that makes the

trilateration approach inappropriate for the outdoor application proposed in this

research The signal strength indicator itself is a measure that has some noise in its

56

nature Moreover physical phenomena such as the Doppler effect multi-path and

fading are other factors that can introduce error to the signal strength Additionally

the dynamic movement of physical objects on the road (ie vehicles bicyclists and

pedestrians) is another factor that makes the outdoor trilateration based on the signal

strength challenging

In the rest of this research another approach based on the wireless received signal

strength is pursued The new approach is less sensitive to the natural noise and

fluctuation within the RSSI levels Furthermore the new approach utilizes RSSI data

from a group of detections for the same device to obtain location data and does not

compare detections from different devices

33 VEHICLE POINT DETECTION SYSTEM

In this section a different approach for collecting vehicle movement data is presented

The objective is to explore and test how the DCUs that collect data wirelessly can be

utilized as point detection units A point detection unit identifies the time that a

vehicle has just passed an imaginary line on the road that originates from the data

collection unit By utilizing multiple DCUs that function as point detection units a

vehicles trajectory through a road segment can be approximated The objective of this

method is less ambitious with respect to the vehicle movement data level of detail

sought in the Trilateration approach To achieve this goal time stamped wireless data

obtained by a DCU from passing vehicles which includes RSSI levels is utilized to

estimate when a vehicle was the closest to the data collection unit When multiple

57

DCUs are utilized on a road segment this information can be used to derive several

performance measures such as travel times and intersection delay

The remainder of this section starts with a presentation of the background theory

supporting how RSSI data can be used to estimate when vehicles just pass a DCU

This theory is implemented in a RSSI-based point detection algorithm that is

described next Finally a validation test experiment and results for the proposed

algorithm are presented

331 VEHICULAR MOVEMENTS NEAR A DATA COLLECTION UNIT

AND THE RSSI-DISTANCE RELATIONSHIP

Vehicle trajectories as a vehicle travels by a DCU can take many different forms

Vehicles may pass the DCU at a fairly constant speed or could be accelerating or

decelerating Vehicles may also stop near the DCU before passing it If a vehicle

contains a detectable wireless device the DCU will be detecting the vehicle multiple

times as it travels in the coverage area of the DCU In this section theoretical signal

propagation models are utilized to understand how the signal strength of the DCU

detections will vary over time for different vehicle trajectories

In this research it is assumed that a DCU is located at the stop line located on a

signalized intersection The vehicle trajectories analyzed include through passes and

stops due to a red light The stops may occur relatively close or far from the stop line

For these three cases numerical examples of RSSI levels as a function of distance

and time (ie vehicle trajectory) are generated and plotted (see Figures 35 36 and

58

37) The predicted RSSI levels as a function of distance and time are obtained from

the model presented in section 325 (Equation 36) The plots show the time on the X

axis (in seconds) as the vehicle enters and leaves the intersection the predicted RSSI

level on the left Y axis (in dbm) and the distance from the perpendicular line on the

road extended from the DCU on the right Y axis (in meters) It is assumed that the

design vehicle has a constant speed of 35 MPH when approaching the stop line and it

takes about 10 seconds to stop or return to the constant speed if a stop occurs For

example assume that a vehicle at time t is 90 meters away from the perpendicular

line extended on the road from the DCU Assuming that the lateral distance from the

vehiclersquos lane to the DCU is about 8 meters (26 feet is the width of two standard

lanes) this will result in a direct distance of radic8ଶ + 90ଶ = 9035 meters from the

DCU Using the propagation model introduced in Equation 36 the expected RSSI

level at this distance is -67dbm The output of the propagation model in Equation 36

is a positive number since the propagation model represents the RSSI level change as

the signal travels over a given length (path loss) An average cell phone transmits a

power level of 1 milliwatt (0 dbm) Therefore the RSSI level at a distance of 9053

meters should be -67 dbm

To consider the impact of environmental noise on the RSSI levels recorded and

the vehiclersquos movement effect on the RSSI levels a random value from a normal

distribution was also added to RSSI values predicted by equation 36 These plots

(Figures 35 36 and 37) show a sample of RSSI behavior as a vehicle enters and

leaves a signalized intersection

59

0

200

400

600

800

1000

-120

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance

Figure 35 Highlighted Example of a Through Pass RSSI and Distance Sample Data

0

100

200

300

400

500

600

700

800

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10

0 0 5 10 15 20 25 30 35

RSSI

Distance

Figure 36 Highlighted Example of a Stop Far From Stop Line RSSI and Distance

Sample Data

60

-100 0 100 200 300 400 500 600 700 800

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance

Figure 37 Highlighted Example of a Stop Near Stop Line RSSI and Distance Sample

Data

Figure 35 shows the scenario when a vehicle arrives at the intersection during a

green light In this example there is no congestion at the intersection Therefore the

vehicle travels through the intersection with a constant speed As the vehicle enters

the discovery range of the DCU located at the intersection the RSSI levels start to

increase until a maximum point and then decrease until the vehicle leaves the

intersection Theoretically the maximum point is recorded when the vehicle was the

closest to the DCU Therefore the time-stamped data record with the highest RSSI

value is the best estimate of the time that vehicle has passed the DCU

Figure 36 illustrates the second scenario in which the vehicle stops due to the red

light In this case the vehicle stops far from the stop line because a queue has formed

in front of the stop line During the time that vehicle has stopped it is unlikely to see

large differences in RSSI levels since the vehiclersquos distance to the DCU remains

constant Once the vehicle starts to move again the RSSI level increases as the

61

vehicle gets closer to the stop line and then decreases as the vehicle continues beyond

the stop line Again the time-stamped data record with the maximum RSSI level best

estimates the time the vehicle passed the stop line

Figure 37 shows the third scenario in which the vehicle also stops due to the red

light In this scenario however the vehicle stops very close to the stop line In this

case it is not easy to use the RSSI data to predict when vehicle has passed the stop

line since it is less likely that the time-stamped data record with the maximum RSSI

value is the best estimate of the time that vehicle has passed the stop line This can be

attributed to the effect of noise on the RSSI levels which are often greater than the

predicted difference in RSSI levels when a vehicle stops close to the DCU and when

it is adjacent to the DCU This complexity is usually magnified in real-world

scenarios in which the noise levels of the RSSI data can be significantly higher due to

other factors such as interference and the presence of other vehicles

332 POINT DETECTION ALGORITHM

In this section a point detection algorithm using time-stamped RSSI data associated

with a traveling vehicle is presented The algorithm presented here utilizes the

distance-RSSI relationship introduced in section 331 and generates an estimate for

the time that a vehicle has just passed an imaginary line on the road that originates

from the data collection unit A sample of data collected from one DCU is shown in

Table 34 These data represent a sample of MAC addresses collected over a seven-

minute period from one of the installed DCUs Truncated MAC addresses are shown

62

in the first column and the timestamps are shown in the second column For the

purposes of this research the combination of a truncated MAC address and its

corresponding timestamp (ie date and time) are referred to as detection The three

columns on the left in Table 34 show the MAC addresses in the sequence that they

were detected by the DCU The three columns on the right in Table 1 show the same

data sorted by MAC address Nine different MAC addresses were detected over the

seven-minute period A single MAC address may be detected multiple times as it

passes the detection zone of a DCU The antenna attached to the DCU utilized to

collect the sample data in Table 34 covers a large area of the road (approximately

1200 feet of the road 600 feet before and after the DCU) Since a vehicle traveling

on this road at a particular speed will be in the length of road covered by the DCUs

antenna for couple of seconds multiple detections of MAC addresses should occur

(see Figure 38) For the sample data presented in Table 34 the speed limit was 45

MPH which requires an average vehicle 18 seconds to travel the length of the

antennarsquos discovery range The combined effects of the probabilistic nature of the

Bluetooth inquiry procedure the variations in radio frequency signal strength of the

radios of Bluetooth enabled devices and unknown and uncontrollable factors

affecting radio frequency communications (eg interference multi-path) explain why

active Bluetooth devices in different passing vehicles moving at the same speed may

be detected a different number of times

To facilitate the description of issues related to locating vehicles location based

on MAC address data the concept of a group of MAC addresses will be defined and

63

illustrated A group will be defined as a collection of MAC address detections for the

same MAC address that represent one trip (in a single direction) of the corresponding

Bluetooth-enabled device through the length of road covered by the DCUs antenna

along the road that it is monitoring For example the trip illustrated in Figure 38

shows a group size of 3

Figure 38 Multiple detections within the DCUs detection zone

64

Table 34 Sample of MAC Address Data from an Installed Bluetooth DCU

MAC Addresses in Sequence MAC Add Date Time 283AC3 6262012 170009 0D1BA6 6262012 170013 0D1BA6 6262012 170021 5F9FB8 6262012 170053 5F9FB8 6262012 170057 A5E228 6262012 170057 5F9FB8 6262012 170102 5F9FB8 6262012 170106 5F9FB8 6262012 170110 5F9FB8 6262012 170114 5F9FB8 6262012 170119 ACC9B5 6262012 170127 ACC9B5 6262012 170131 ACC9B5 6262012 170147 ACC9B5 6262012 170151 A5E228 6262012 170159 A5E228 6262012 170215 C2BBD0 6262012 170306 9C09B2 6262012 170310 C2BBD0 6262012 170310 9C09B2 6262012 170315 C2BBD0 6262012 170315 C21146 6262012 170433 C21146 6262012 170437 C21146 6262012 170441 86F2DF 6262012 170532 A5E228 6262012 170608 A5E228 6262012 170702

MAC Addresses Sorted MAC Add Date Time 0D1BA6 6262012 170013 0D1BA6 6262012 170021 283AC3 6262012 170009 5F9FB8 6262012 170053 5F9FB8 6262012 170057 5F9FB8 6262012 170102 5F9FB8 6262012 170106 5F9FB8 6262012 170110 5F9FB8 6262012 170114 5F9FB8 6262012 170119 86F2DF 6262012 170532 9C09B2 6262012 170310 9C09B2 6262012 170315 A5E228 6262012 170057 A5E228 6262012 170159 A5E228 6262012 170215 A5E228 6262012 170608 A5E228 6262012 170702 ACC9B5 6262012 170127 ACC9B5 6262012 170131 ACC9B5 6262012 170147 ACC9B5 6262012 170151 C21146 6262012 170433 C21146 6262012 170437 C21146 6262012 170441

C2BBD0 6262012 170306 C2BBD0 6262012 170310 C2BBD0 6262012 170315

In the data shown in Table 34 the smallest time interval observed between

detections with the same MAC address is four seconds because the DCUs were

programmed to repeat the initiation of an inquiry procedure that is four seconds in

length to cover the entire Bluetooth frequency spectrum and hence detect all

Bluetooth-enabled devices nearby Table 34 also shows different MAC addresses

65

that have the same timestamps (eg 9C09B2 and C2BBD0) These MAC

addresses were detected in the same inquiry period and represent either multiple

devices in the same vehicle or multiple vehicles with active devices passing the

reader at about the same time

For the purpose of identifying those MAC address detections that correspond to

when the vehicle was the closest to the imaginary line originating at a DCU Figure

39 shows an example in which a vehicle was detected three times passing a DCU

(shown as location numbers 1 2 and 3) In this example at the timestamp of the

MAC address detected at location number 2 the vehicle was closest to the DCU For

vehicles that arrive at an intersection and have to wait for the traffic signal to change

their number of MAC address detections (or group size) can grow rapidly This can

result in large errors when calculating traffic performance measures such as travel

time samples when an inappropriate timestamp is chosen

Figure 39 Schematic representation of multiple detections in a single trip forming a

group

66

As explained before the method developed in this research to select a single

MAC address detection is based on the RSSI The RSSI is known to be correlated

with distance where a larger RSSI value indicates that the DCU and Bluetooth device

are closer together than a lower RSSI value (Awad et al 2007 Kotanen et al 2003

Pei et al 2010)

Although RSSI is correlated with distance it is also affected by the movement of

the Bluetooth mobile device In theory moving toward and away from the DCU can

generate Doppler effect which in some cases can reduce the signal strength level

received at the DCU Multi-path is another propagation phenomenon that results

when radio signals reaching the receiving antenna come from two or more paths This

phenomenon can have both constructive and destructive effects on the signal strength

Modeling the effect of these phenomena is complex and beyond the scope of this

research however it is accounted for indirectly by adding the noise adjustment to the

data in Figures 35 36 and 37 For example in Figure 39 a device that is stationary

at location 2 will often generate a MAC address detection with a higher RSSI than

when the device is at location x but in movement For DCUs located at signalized

intersections this implies that using the MAC address detection with the highest

RSSI is often not the detection that represents the time the vehicle was closest to the

DCU

For a DCU located at a signalized intersection the method used to identify the

MAC address detection representing the time when the vehicle is closest to the DCU

is based on the rate of change in RSSI values between consecutive MAC address

67

detections within a group In this approach the MAC address detection selected is

that detection after which the subsequent RSSI values decrease at the highest rate

This rate of change can be obtained using following equation 37

େ୳୬୲ ୗୗ୴୧୭୳ୱ ୗୗ Equation 37 େ୳୬୲ ୧୫ୱ୲ୟ୫୮୴୧୭୳ୱ ୧୫ୱ୲ୟ୫୮

In those cases where this decreasing rate of change is not observed the last MAC

address detection in a group is saved Often the MAC address detection selected also

has the largest RSSI value Intuitively the method described attempts to detect the

time that a vehicle has just started to leave the intersection after passing the DCU

Figure 310 shows a real example of RSSI data over time for multiple reads of the

same Bluetooth-enabled devices during a single probe vehicle trip past a DCU In this

example a probe vehicle carrying two Bluetooth-enabled cell phone devices

approached an operating DCU at the signalized intersection of SW Durham Rd and

Highway 99W The probe vehicle stopped for a while at this intersection and then left

the discovery area of the DCU The dashed line represents the manually recorded

time that corresponds to when the probe vehicle just passed the imaginary line

perpendicular to the road extending from the DCU In Figure 310 the most accurate

timestamps are identified by the larger squares for both cell phones These

timestamps represent the time the probe vehicle started to leave the intersection and

are characterized by a rapid decrease in the RSSI levels after these points

68

Figure 310 RSSI values over time for two devices in a probe vehicle arriving and

waiting at an intersection

333 VALIDATION EXPERIMENTS

A series of experiments were conducted to test the accuracy of the proposed point

detection system in three different environments The purpose of these experiments

was to assess differences between the time stamp of the automatically selected record

(utilizing the point detection algorithm presented in section 332) and the time the

vehicle crosses an imaginary line extending from the DCU antenna across and

perpendicular to the road A schematic of the general physical experimental setup is

presented in Figure 39 A DCU and antenna are installed at some location adjacent to

the roadway being monitored (point A in Figure 39) The distance d from point A in

69

Figure 39 and the roadway will vary depending on the available mounting structures

at the location where the DCU is placed

In these experiments a probe vehicle containing active Bluetooth devices with

known MAC addresses travels past a DCU multiple times At the same time there is a

person operating a laptop computer that is communicating with the DCU The

communication of the DCU and laptop computer permits synchronization of the DCU

time and laptop time Software is provided to help the laptop operator record the

exact time that a vehicle passes the DCU (crosses line AB in Figure 39) When the

front of the vehicle just passes line AB in Figure 39 the operator will press the

ldquoEnterrdquo key on the laptop keyboard When this key is pressed a time stamp will be

automatically generated and stored in a file

If feasible in a particular test environment the vehicle will be driven past the DCU

at a fixed speed Different speeds can be examined to see if speed has an effect on the

differences between the timestamp of the selected record and the time the vehicle

passes the DCU

The DCU will be scanning continuously during the experiment Knowing the time

that the vehicle passed the DCU and assuming a constant travel speed the time that

the vehicle was a specific distance from the DCU can be computed For example the

time that the vehicle is at points 1 2 and 3 in Figure 39 can be computed from the

time the vehicle passed the DCU (point x) The distance that each point is from point

x (d1 d2 d3) is known By matching the MAC address timestamps to various

locations it is possible to map RSSI values to different vehicle locations

70

The first test environment used for validation was a low traffic free flowing rural

road near Corvallis Oregon (Camp Adair Road adjacent to EE Wilson Wildlife Area

ndash Figure 311) Due to low traffic volumes it was possible to drive at various constant

speeds past the DCU The probe vehicle traveled passed the DCU 30 times (15 times

traveling east and 15 times traveling west) for each speed tested The speeds tested

were 25 35 and 45 mph The DCU was located 36 feet from the road and the antenna

was mounted on a tripod at a height of 70 inches Two different cell phones with

Bluetooth communications active were used in the tests The responses computed

from the recorded data were the time differences between the MAC address records

with the highest RSSI reading and the times the vehicle crossed the line drawn from

the DCU perpendicular to the road A histogram of all the test results is shown in

Figure 312 Most time differences are less than two seconds with a maximum

difference of 75 seconds The average time difference was 023 seconds with a

standard deviation of 137 seconds Negative time differences occur when the time

stamp of the highest RSSI record occurs after the vehicle passes the DCU

71

Figure 311 DCU installation on Camp Adair Road Benton County

Figure 312 Histogram of the difference between the time the probe vehicle passed

the DCU on Camp Adair Road and the MAC address record with the highest RSSI

72

The second test environment was Wallace Road which is located on the west side

of the city of Salem Oregon This road is also a free-flowing road with no signals

located near the DCU but a single signal between the DCU locations The Oregon

Department of Transportation (ODOT) Intelligent Transportation Systems (ITS) Unit

operates a test site on this road located at latitude-longitude of N 44972858 and W -

123066321 (see Figure 313) Wallace Road runs north and south with two lanes in

both directions and the speed limit is 45 MPH Forty total probe vehicle runs (20

traveling northbound and 20 traveling southbound) were made past the DCU with

two different cell phones with active Bluetooth communications present in the

vehicle

The responses computed from the recorded data were the time differences

between the MAC address records with the highest RSSI reading and the times the

vehicle crossed the line drawn from the DCU perpendicular to the road A histogram

of all the test results is shown in Figure 314 Most time differences are less than two

seconds with a maximum difference of five seconds The average time difference

was 023 seconds (the same as for Camp Adair Road) with a standard deviation of

124 seconds Negative time differences occur when the time stamp of the highest

RSSI record occurs after the vehicle passes the DCU

73

Figure 313 DCU Location on Wallace Road Salem Oregon


the DCU on Wallace Road and the MAC address record with the highest RSSI

74

The third test environment was along Highway 99W in Tigard Oregon Five

DCUs have been installed at five different intersections (see Figures 315 and 316)

Figure 315 Southernmost DCU locations on Highway 99W Tigard Oregon

75


The Highway 99W tests were conducted at DCU 12 which is located at a

signalized intersection Forty total probe vehicle runs (20 traveling northbound and 20

traveling southbound) were made past the DCU with two different cell phones with

active Bluetooth communications present in the vehicle Two responses were

recorded and analyzed

The first response computed from the recorded data were the time differences


vehicle crossed the line drawn from the DCU perpendicular to the road This is the

same response as used in the prior test environments which were both free-flowing

76

roads A histogram of all the test results is shown in Figure 317 Most time

differences are less than two seconds with a maximum difference of 43 seconds The

average time difference was 31 seconds with a standard deviation of 99 seconds

The results were similar to the other test environments except for five test runs where

the response was greater than 11 seconds (42 41 18 12 and 12 seconds) With these

five responses removed the average maximum and standard deviation of the

response are -003 seconds 33 seconds and 13 seconds respectively

In those instances where large time differences were observed the probe vehicle

was stopped by the signal and stationary The stationary position of the probe vehicle

was one or two vehicle lengths before the line drawn from DCU 12 perpendicular to

the road When stationary yet close to the DCU higher RSSI readings were obtained

than when the probe vehicle was moving across the line drawn from the DCU In this

case the MAC address record with the highest RSSI reading occurred when the probe

vehicle was close in distance to the line drawn from the DCU but still relatively far

with respect to time since the vehicle was waiting for a green light to proceed As

seen in Figure 312 all of the relatively large time differences are positive

differences which occur when the MAC address with the highest RSSI reading

occurs before the probe vehicle passes the line drawn from the DCU Negative time

differences occur when the time stamp of the highest RSSI record occurs after the

vehicle passes the DCU In such cases as just described the accuracy of the travel

time samples generated will be reduced The time interval between when the highest

RSSI reading was recorded and when the vehicle passed the DCU will be added to

77

the travel time along the road section that occurs after the signal Similarly this time

difference will not be included in the travel time for the road section occurring before

the signal


DCU 12 on Highway 99W and the MAC address record with the highest RSSI

The second response recorded and analyzed is based on a method to eliminate

these occasional large errors caused when a vehicle stops just before passing the

DCU In this method the single record selected from a group of MAC addresses is the

record after which the subsequent RSSI values decrease at the highest rate In those

78

cases where this decrease is not observed the last record in a group is saved Since the

highest RSSI record can often be when a vehicle stops close to but before passing the

DCU the RSSI values should also decrease rapidly once a vehicle passes the DCU

after the vehicle has a green signal

Figure 310 showed the time stamps and associated RSSI for two Bluetooth

devices (two cell phones) when a vehicle stops and waits close to the DCU as it

travels through the intersection The large circles represent the time stamps associated

with the highest RSSI Since the vehicle stops near the DCU the time stamps which

are associated with the highest RSSI are considerably earlier than the time when the

vehicle passes the DCU

The responses computed using this method were compared to the times the probe



seconds with a maximum difference of 21 seconds The average time difference was

22 seconds with a standard deviation of 41 seconds The performance is more

accurate and consistent than saving the record with the highest RSSI value

79

Figure 318 Histogram of accuracy of the time stamp before the RSSI values rapidly

decrease

80

4 APPLICATION CASE STUDIES

The Bluetooth-based vehicle point detection system introduced in this research can be

employed to collect vehicle movement data in different areas of the transportation

system The vehicle movement data collected can then be utilized to estimate traffic

performance measures for analysis and planning studies

In this section two particular applications of the Bluetooth-based vehicle

point detection system are presented In these applications vehicle movement data

are utilized to derive two commonly used traffic performance measures intersection

delay and travel time The most common current data collection methods employed to

collect data to estimate these performance measures are expensive and in some cases

inaccurate (Bonneson amp Abbas 2002 Middleton et al 2009 Balke et al 2005)

and labor intense In contrast the data collection method developed in this research

provides a low-cost solution to estimate these performance measures with high levels

of accuracy

41 TRAVEL TIME STUDY

The use of time-stamped media access control (MAC) address data acquired from

Bluetooth-enabled devices to collect travel time data has received significant attention

in recent years However past research has mainly focused on the application of

Bluetooth technology to obtain travel time data on free flowing roads A smaller

amount of research has addressed the use of Bluetooth-based data collection systems

81

on arterial roads and in particular for the collection of travel times between

signalized intersections where the travel time accuracy has been questionable The

point detection system presented in Chapter 3 was utilized to develop a methodology

to collect accurate travel time data between signalized intersections using a

Bluetooth-based data collection system The proposed RSSI-based travel time data

collection method can be implemented using any wireless technology that provides a

unique identification number to distinguish between different mobile devices and an

associated signal strength measurement during the wireless communication process

The results of this research have been attested with a Bluetooth-based data

collection system that consists of five DCUs permanently installed at consecutive

signalized intersections along an urban high-volume signalized arterial (ie

Highway 99W) running north-south in Tigard OR This system was introduced and

used earlier in Chapter 3 to perform a timestamp selection accuracy study These

DCUs are located at intersections because of the availability of power and access to a

communications network through signal control cabinets Figure 41 shows the five

consecutive signalized intersections (approximately one mile apart from each other)

where DCUs are installed ie Durham Rd McDonald St Johnson St 217 NB ramp

and 64th Ave

82

Figure 41 Location of Bluetooth DCUs on Highway 99W (Tigard OR)

411 GENERATION OF TRAVEL TIME SAMPLES USING MAC ADDRESS

DATA

To begin the discussion of travel time sample generation the specific road segment of

interest must be defined In this research the road segments for which travel time

samples are desired start at an imaginary line extending from a roadside DCU across

and perpendicular to the road and end at a similar line drawn from another DCU at a

different location along the same road Travel time samples are computed as the time

difference between detections of the same MAC address at each DCU This research

focuses on the case when these roadside DCUs are installed at signalized intersections

83

located on arterial roads ldquoGround truthrdquo travel times for a specific vehicle will be

defined as the time difference between when the vehicle crosses the previously

defined imaginary lines (determined and recorded by an observer inside the probe

vehicle) All travel time sample accuracy results are relative to the ground truth travel

times

412 TRAVEL TIME SAMPLE GENERATION SUPPLEMENTED WITH

RSSI DATA

Based on the definition of a road segment introduced earlier the most accurate travel

time samples generated from MAC address data will utilize those MAC address

detections that correspond to when the vehicle was the closest to the imaginary line

originating at a DCU As explained in section 332 the method developed in this

research to select a single MAC address detection from the group is based on the

RSSI The method used to identify the MAC address detection representing the time

when the vehicle is closest to the DCU is based on the rate of change in RSSI values

between consecutive MAC address detections within a group In this approach the

MAC address detection selected is that detection after which the subsequent RSSI

values decrease at the highest rate In those cases where this decreasing rate of change

is not observed the last MAC address detection in a group is saved Often the MAC

address detection selected also has the largest RSSI value Intuitively the method

84

described attempts to detect the time that a vehicle has just started to leave the

intersection after passing the DCU

An experiment was conducted on Highway 99W in Tigard Oregon to assess

differences between travel times calculated using time-stamped MAC addresses

associated with the first detection last detection and average of the first and last

detections in a group and travel times calculated with time-stamped MAC address

detections selected utilizing RSSI data presented in section 332 The experimental

data were collected from a probe vehicle containing two active Bluetooth devices

(cell phones 1 and 2) with known MAC addresses that traveled past the five DCUs

installed on Highway 99W multiple times A laptop computer was used in the

experiment to facilitate the collection of manual travel times An observer pressed the

ldquoEnterrdquo key on the laptop when the vehicle passed the DCUs to instruct a software

application to automatically generate and store a timestamp in a file The DCUs

scanned continuously during the experiment A total of 20 probe vehicle runs (10

traveling northbound and 10 traveling southbound) were made past all five DCUs

Adjacent signalized intersections on Highway 99W were paired to form a total of

four road segments with an average length of one mile For each probe vehicle run on

each defined segment four different travel time samples were generated utilizing the

various methods for selecting MAC address detections form a group Next the

calculated travel times were compared against the ground truth travel times

collected The absolute difference between the calculated travel time samples and

ground truth travel times were recorded as the error for each travel time calculation

85

method Table 41 summarizes the errors for the calculated travel time samples using

different timestamp selection approaches for cell phone 1 for the road segment

between Johnson St and the 217 NB Ramp

Table 41 Cell Phone 1 Sample Probe Vehicle Test Results for the Road Segment


Non RSSI-based Methods RSSI

Metho d

Ground Truth Travel Times

Absolute Error Relative to Ground Truth Travel Times

Run Firs t Last (F+L)

2 First Last (F+L)2

RSSI Method

1 128 135 131 125 124 004 011 007 001 2 225 148 206 149 149 036 001 017 000 3 212 212 212 203 203 009 009 009 000 4 249 136 212 139 135 114 001 037 004 5 151 301 226 251 253 102 008 027 002 6 238 134 206 137 133 105 001 033 004 7 141 259 220 229 229 048 030 009 000 8 258 245 251 239 240 018 005 011 001 9 158 256 227 247 246 048 010 019 001

10 341 202 252 156 154 147 008 058 002 11 151 143 147 142 140 011 003 007 002 12 142 140 141 134 134 008 006 007 000 13 246 233 239 241 240 006 007 001 001 14 202 140 151 138 136 026 004 015 002 15 203 203 203 156 153 010 010 010 003 16 234 229 231 226 225 009 004 006 001 17 144 148 146 139 138 006 010 008 001 18 246 248 247 243 242 004 006 005 001 19 155 205 200 153 154 001 011 006 001 20 258 304 301 303 303 005 001 002 000

AVG (sec) 2785 73 147 135 STDV (sec) 2988 64 1414 122

86

The test results presented in Table 41 clearly show that the travel time samples

obtained with the RSSI-based method are significantly more accurate and precise

than those obtained with any other method For this example road segment the

average travel times generated using the RSSI-based method had an average error of

135 seconds compared to the ground truth travel times recorded On the other

hand the travel times generated using first-to-first MAC address timestamps (ie the

most common approach cited in the literature to obtain travel time samples) had an

average error of 2785 seconds which is significantly higher than the calculated error

for the proposed method Table 42 summarizes the test results for all four road

segments

Table 42 Average Absolute Travel Time Sample Errors in Seconds for Different

Travel Time Calculation Approaches

Segment Cell Phone First-to-First Last-to-Last Average-to-

Average RSSI Method

Durham Rd McDonald St

1 1955 (1312) 890 (597) 1145 (768) 265 (178) 2 1305 (876) 475 (319) 770 (517) 315 (211)

McDonald St Johnson St

1 855 (629) 610 (449) 590 (434) 305 (224) 2 611 (449) 537 (395) 532 (391) 353 (259)

Johnson St 217 NB ramp

1 2785 (2193) 730 (575) 1470 (1157) 135 (106) 2 1568 (1235) 384 (303) 790 (622) 268 (211)

217 NB ramp 64th Ave

1 3310 (2348) 575 (408) 1600 (1135) 150 (106) 2 2358 (1672) 295 (209) 1216 (862) 295 (209)

Average

1 2226 (1620) 701 (507) 1201 (874) 214 (154) 2 1460 (1058) 423 (306) 827 (598) 308 (223)

All 1843 (1339) 562 (407) 1014 (736) 261 (188)

87

A series of paired t-tests were performed to check if there is indeed a statistical

difference between the error in the travel time samples calculated from the first-to-

first last-to-last and average-to-average travel time calculation methods when

compared to the error in the travel time samples obtained with the RSSI-based

method The results show that significant differences exist (with a maximum p-value

of 0006) between the RSSI-based method and each of the other methods for both

test cell phones on all four road segments Additionally a series of Pitman-Morgan

tests for equal variance between the RSSI-based method and the other methods were

conducted This test is appropriate for paired data Of the 24 tests conducted 16

rejected the null hypothesis of equal variance at a 95 significance level The tests

where the null hypothesis was not rejected were mostly with the last-to-last method

which was the second most accurate

The aggregated test results in Table 42 for both Bluetooth-enabled cell phones

tested over all road segments suggest that the travel time samples generated with the

RSSI-based method are significantly better (ie have less error) than the travel time

samples calculated by utilizing the first-to-first last-to-last and average-to-average

methods Among the methods used in the literature the travel time samples calculated

using the last-to-last detection timestamps are better than first-to-first and average-to-

average This makes sense since the last detection timestamps are closer to the time

that a particular vehicle has left the intersection Due to the wait time delay that

vehicles experience at signalized intersections the first-to-first approach results in

poor accuracy

88

Travel times between the DCUs available for use in this research were collected

continuously from June 9 2011 through February 29 2012 and from May 7 2012

through May 29 2012 Table 43 shows the average daily traffic volumes and the

average number of travel time samples collected by the system

Table 43 The Volume Of Travel Time Samples Generated Compared To Traffic

Volume

Road Segment Travel Direction

Avg Daily ofTravel Time

Samples

Avg DailyTraffic Volume

Avg TravelTimesAvgTraffic Vol

DCU 9 -DCU 10 North 1306 21192 62 South 1369 23423 58




42 INTERSECTION PERFORMANCE

This section evaluates the application of the proposed Bluetooth-based vehicle point

detection system to acquire travel times for vehicles approaching and leaving an

intersection The travel time data samples collected at the intersection also include the

time that it takes for a vehicle to interact with the control mechanism at the

intersection This additional time duration introduces some delay to the traffic passing

through an intersection The validity of the approach was investigated through an

experiment conducted at an intersection with large amounts of pedestrian and cyclist

89

traffic relative to vehicle traffic The test location was at the intersection of NW

Monroe Ave and NW Kings Blvd near the Oregon State University campus in

Corvallis Oregon (Figure 42) The test was completed on a Friday during noon rush

hour (from 1100 am to 1200 pm) This three-way stop-controlled intersection has

several businesses in the vicinity and experiences highmedium levels of traffic in

different modes including passenger vehicles buses pedestrians and bicyclists The

frequent occurrence of different travel modes introduces a very high level of

complexity to the system since active Bluetooth devices are present in all travel

modes This makes the test intersection ideal for the purposes of this study since it

represents one of the more complicated environments where such a system may be

deployed

Figure 42 Test Setup Utilized in the Intersection Control Delay Experiment

90

The goal of this experiment was to compare the time duration that vehicles spend

at the intersection obtained from the wireless data collection system to the same data

obtained manually For the purpose of this experiment ground-truth intersection time

duration data was obtained by video recording the intersection In the next section a

summary of the test setup is provided

421 INTERSECTION TEST SETUP

The overall setup configuration for this test is presented in Figure 43 As Figure 43

illustrates three battery powered DCUs were utilized in this test and they were

located so that the beginning stop and the end points of the functional area of the

intersection could be monitored for eastbound traffic The DCUs were constantly

scanning for Bluetooth-enabled devices and trying to predict when a device passed

the imaginary perpendicular line extended from the DCU onto the road MAC address

data were collected for one hour Two high definition (HD) and high-speed cameras

were utilized to record traffic at the DCU locations The high-speed feature allowed

for the recording of up to 60 frames per second which made it possible to track

moving objects with very high accuracy The video recorded by the cameras was used

for validation purpose and made it possible to see exactly when a detected vehicle just

passed the DCU unit

91

DCU 1 DCU 3 DCU 2

NW

Kin

g s B

lvd

Monroe Ave

Dutch Bros

Rogers Graff

N University Christian Center

X

X

150 ft 50 ft


422 TEST RESULTS AND CONCLUSIONS

A total of 54 unique MAC addresses were detected by all three DCUs during the one-

hour data collection period By reviewing the video data from both cameras a total of

25 unique MAC addresses were matched to a single vehicle (or a platoon of vehicles)

passing the DCU locations In these particular cases it was easy to identify the

vehicles since there was no other traffic present near the DCUs In cases when a

platoon of vehicles passed the DCU location it was not clear exactly which

individual vehicle contained the active Bluetooth device However since the accuracy

assessment is based on measured travel time between reference points (ie DCU

locations) this did not affect the test results It is important to mention that a total of

400 vehicles were identified after reviewing the video data for the entire one-hour

92

data collection period This means that the installed DCU system was able to capture

travel times for approximately 6 of the total traffic Out of 25 samples the travel

times recorded by the DCUs were the same as those calculated from the video in 14

cases In the other 11 cases a maximum error of 6 seconds was recorded with an

average error of 114 seconds The summary of the results is presented in Table 44

Table 44 Summary Results from the Intersection Delay Study

MAC Travel Time From

DCUs (sec)

Travel Time From Video (sec)

Travel Time Error (sec)

Address DCU1 ndash

DCU2

DCU2 ndash

DCU3

DCU1 ndash

DCU2

DCU2 ndash

DCU3 Error 1 Error 2

1 1CA12F47 1 7 1 7 0 0 2 18A36B03 NA 15 NA 15 0 3 7EADFBB8 10 6 10 12 0 6 4 D168B66C 5 13 5 13 0 0 5 437FEA02 16 9 16 15 0 6 6 6CA73F7A 10 5 12 5 2 0 7 6CA73F7A NA 5 NA 9 4 8 7E7428B0 5 4 5 4 0 0 9 9FB714E6 4 7 4 7 0 0

10 FE734A5C 11 7E255147 NA 13 NA 13 0 12 AE6DFA3B 5 7 5 7 0 0 13 580E9E36 5 9 10 4 5 5 14 4BDE10B9 12 6 12 6 0 0 15 166700E0 11 5 11 8 0 3 16 1C1419BF 17 689634C0 6 10 6 10 0 0 18 7E5D3E85 16 4 16 4 0 0 19 1CA1A736 20 3D1F94A4 9 5 9 5 0 0 21 3D1F94A4 NA 2 NA 2 0 22 0C5AEDD 16 5 16 5 0 0

93

D

23 BE6BEDC D 7 4 7 4 0 0

24 BE461DE4 25 3EECAF75 14 7 14 7 0 0

Average Travel Time 041 114

Max (seconds) 5 6

In Table 44 the cells with ldquoNArdquo indicate that there was no record from that road

segment for a particular vehicle (identified by the detected MAC address) This is

mainly because that particular vehicle has not passed all three DCUs Therefore no

travel time could be calculated for the road segment utilizing the missing DCUs For

four MAC address samples presented in Table 44 (10 16 19 and 24) all fields have

been left blank For these samples the DCUs have detected the presence of an active

Bluetooth device However it was impossible to relate the MAC address detections to

visual data collected by video recording the intersection

For this study the functional area of the intersection can be defined as the length

of the road between DCU1 (150ft upstream of the stop line) and DCU 3 (50ft

downstream of the stop bar) Within this length of the road vehicles approaching the

intersection on the eastbound of NW Monroe Ave interact with the traffic control

device (a stop sign in this test) The time duration is defined as the time it takes for a

vehicle to travel between DCU 1 and DCU 3 which can be used as an intersection

performance measure To obtain this duration data those vehicles that passed all three

DCUs on the eastbound of NW Monroe Ave were selected Ten vehicles among the

25 vehicles in Table 44 drove eastbound past all three DCUs The average duration

94

time for these passes obtained from wireless data collection system is 165 seconds

The average duration time for the same test period obtained from video recordings is

182 seconds Therefore there is 17 seconds error in the time duration data obtained

from wireless-based vehicle movement data collection system

95

5 CONCLUSIONS

One key to developing strategies for improving traffic system performance is to first

develop an understanding of how traffic conditions evolve over time which requires

real time data collection Collecting such data has been limited by the types and costs

of automated data collection technologies such as inductive loop detectors radar-

based detection systems and video image processing The central idea investigated in

this study is to utilize multiple wireless data collection units (DCUs) to automatically

collect vehicle location and movement data at ldquoareas of interestrdquo within the road and

highway system For the purpose of this project Bluetooth technology was selected

as the implementation platform due to the vast use of Bluetooth devices in vehicles

today thus allowing real-world testing to evaluate the methods developed Bluetooth

based data collection units are inexpensive and may be configured as portable or

permanently installed The data collection unit may be connected to central servers

through an existing network infrastructure or through wireless technologies

The research in this thesis shows how wireless technology can be utilized to offer

a low cost automated data collection system that is capable of being employed in a

variety of situations and which can also provide data on vehicle location and

movement over time This type of data is unavailable through most current automatic

data collection techniques (with the exception of video image processing) One

application area for this research is intersections where multiple intersection

performance measures can be estimated from the types of data collected

96

automatically utilizing the results of this research There are multiple other

application areas in practice and research that would benefit from the type of data that

can now be be automatically collected Some other topics that may benefit from such

data are reducing fuel consumption and emissions through improving traffic signal

strategies utilizing active traffic management for arterial networks and workzones

and assessing signal timing and control strategies

The technology platform utilized in this research represents a Vehicle-To-


based on Bluetooth wireless communications protocol The intent is not to add to the

direction of the ldquoConnected Vehicle Researchrdquo initiative for Vehicle-To-Vehicle

(V2V) and V2I communications which utilizes dedicated short-range communication

(DSRC) operating at 59 GHz but to provide a more near term data collection

solution for an on-going problem Results and methods that are produced in the

proposed research can be applied within the Connected Vehicle Research utilizing

DSRC As such this research shows the feasibility of an idea that may be

implemented in the near term due to the pervasiveness of Bluetooth technology but

one that can also be transferred and implemented within the DSRC technology

platform

To employ the vehicle data collection system proposed in this research for some

applications such as accurate vehicle movement trajectories at an intersection more

accuracy has yet to achieve Future research can investigate the possibility of utilizing

a cluster of DCUs with a closer proximity in order to obtain more data from the

97

vehicles within the intersection The possibility of developing more advanced

techniques to process RSSI data for location estimations

98

BIBLIOGRAPHY

1 ABI Research Bluetooth Smart Will Drive Cumulative Bluetooth Enabled

Device Shipments to 20 billion by 2017 [Internet]

httpwwwabiresearchcompressbluetooth-smart-will-drive-cumulative-

bluetooth-en Accessed on 26 October 2012

2 Akcelik R and Besley M (2001) Acceleration and deceleration models

23rd Conference of Australian Institutes of Transport Research (CAITR

2001) Monash University Melbourne Australia PP 10--12

3 Amanna A (2009) Overview of IntelliDriveVehicle Infrastructure

Integration (VII) Virginia Tech Transportation Institute

4 Awad A and Frunzke T and Dressler F (2007) Adaptive distance

estimation and localization in WSN using RSSI measures Digital System

Design Architectures Methods and Tools pp 471--478

5 Bahl P and V N Padmanabhan (2000) RADAR An in-building RF-based

user location and tracking system In IEEE Infocom March 2000

6 Balke KN Charara H and Parker R (2005) Real-Time Measures of

Traffic Signal Performance Development of a Traffic Signal Performance

Measurement System (TSPMS)

7 Bennett CR (1994) Modeling driver acceleration and deceleration behavior

in New Zealand ND Lea International Vancouver Canada

8 Bertini R L S Hansen S A Byrd and T Yin (2001) PORTAL

Experience Implementing the ITS Archived Data User Service in Portland

Oregon Transportation Research Record Journal of the Transportation

Research Board No 1917 Transportation Research Board of the National

Academies Washington DC PP 90--99

9 Bonneson J Abbas M of Transportation Research T D Office T I and

Institute T T (2002) Intersection Video Detection Manual Texas

Transportation Institute Texas A amp M University System

99

10 Boxel D V Schneider W H Bakula C (2011) An Innovative Real-Time

Methodology for Detecting Travel Time Outliers on Interstate Highways and

Urban Arterials Presented at the Transportation Research Board 2011 Annual

Meeting Washington DC

11 Courage K and Parapar S (1975) Delay and fuel consumption at traffic

signals Traffic Engineering 45(11)

12 Ferris B D Hahnel and D Fox(2006) Gaussian processes for signal

strength-based location estimation in Robotics Science and Systems

Philadelphia PA

13 Fink J and Michael N and Kushleyev A and Kumar V (2009)

Experimental characterization of radio signal propagation in indoor

environments with application to estimation and control Intelligent Robots

and Systems 2009 IROS 2009 IEEERSJ International Conference on IEEE

PP 2834--2839

14 GonzalezH J Han X Li M Myslinska and J P Sondag ldquoAdaptive fastest

path computation on a road network a traffic mining approachrdquo Proceedings

of the 33rd international conference on Very large data bases pp 794ndash805

2007

15 Gorce J M and K Jaffres-Runser and G de la Roche (2007) Deterministic

approach for fast simulations of indoor radio wave propagation IEEE

Transactions on Antennas and Propagation vol 55 no 3 pp 938ndash 948

16 Hossain AKM and Soh WS (2007) A comprehensive study of bluetooth

signal parameters for localization Personal Indoor and Mobile Radio

Communications 2007 PIMRC 2007 IEEE 18th International Symposium

on IEEE PP1--5

17 Howard A S Siddiqi and G S Sukhatme (2006) An experimental study of

localization using wireless ethernet in Field and Service Robotics Springer

Tracts in Advanced Robotics Springer Berlin vol 24 pp 145ndash153

100

18 Hull B V Bychkovsky Y Zhang K Chen M Goraczko A Miu E Shih

H Balakrishnan and S Madden ldquoCartel a distributed mobile sensor

computing systemrdquo Proceedings of the 4th international conference on

Embedded networked sensor systems pp 125ndash138 2006

19 Ibrahim MR and Karim MR and Kidwai FA (2008) The effect of digital

countdown display on signalized junction performance American Journal of

Applied Sciences 5(5) PP 479--482

20 Jang J Kim H and Cho H (2010) Smart roadside server for driver

assistance and safety warning Framework and applications In Ubiquitous

Information Technologies and Applications (CUTE) Proceedings of the 5th

International Conference on pages 1ndash5 IEEE

21 Jumsan K and Rhee S and Hwang Z (2005) Vehicle passing behaviour

through the stop line of signalised intersection Vol 6 PP 1509--1517

22 Kirby W Pickett PE (2001) Traffic Data and Analysis Manual Texas

department of Transportation

23 Kotanen A and Hannikainen M and Leppakoski H and Hamalainen TD

(2003) Experiments on local positioning with Bluetooth Information

Technology Coding and Computing [Computers and Communications] pp

297--303

24 Ladd A M and K E Bekris A Rudys L E Kavraki and D S Wallach

(2002) Robotics-based location sensing using wireless ethernet in Proc of

ACM Int Conf on Mobile Computing and Networking Atlanta GA pp

227ndash238

25 Li X J Han J-G Lee and H Gonzalez (2007) Traffic density-based

discovery of hot routes in road networks Proceedings of the 10th International

Symposium on Spatial and Temporal Databases pp 441ndash459

26 Li X Li G Pang S Yang X and Tian J (2004) Signal timing of

intersections using integrated optimization of traffic quality emissions and

101

fuel consumption a note Transportation Research Part D Transport and

Environment 9(5) 401ndash407

27 Madhavapeddy A and Tse A (2005) A study of bluetooth propagation

using accurate indoor location mapping UbiComp 2005 Ubiquitous

Computing Springer PP 105--122

28 Maitipe B and Hayee M I (2010) Development and Field Demonstration

of DSRC-Based V2I Traffic Information System for the Work Zone

Intelligent Transportation Systems Institute Center for Transportation

Studies University of Minnesota

29 Neal E and Yance R (2005) Advanced traffic sensor for work zone and

incident management systems Final report NCHRP93 (NCHRP IDEA

program)

30 PBSampJ (2001) Innovative Traffic Data Collection Technical Memorandum

No 1 Prepared for Florida Department of Transportation

31 Pickrell S and Neumann L (2001) Use of performance measures in

transportation decision-making Transportation Research Board Conference

Proceedings

32 Pei L and Chen R and Liu J and Kuusniemi H and Tenhunen T and

Chen Y (2010) Using Inquiry-based Bluetooth RSSI Probability

Distributions for Indoor Positioning Journal of Global Positioning Systems

Vol9 No 2 pp 122--130

33 Quiroga C and D Bullock (1998) Travel Time Studies with Global

Positioning and Geographic Information Systems An Integrated

Methodology Transportation Research Part C Pergamon Pres Vol 6C No

12 pp 101-127

34 Row S M Schagrin and V Briggs (2008) The Future of VII US

Department of Transportation Editor

102

35 Saito M and Forbush T (2011) Automated Delay Estimation at Signalized

Intersections Phase I Concept and Algorithm Development Report number

UT-1105

36 Schilit B A LaMarca G Borriello D M William Griswold E Lazowska

A Bal- achandran and J H V Iverson (2003) Challenge Ubiquitous

location-aware computing and the Place Lab initiative In First ACM

International Workshop of Wireless Mobile Applications and Services on

WLAN

37 Schrank DL and Lomax TJ (2007) The 2007 urban mobility report Texas

Transportation Institute Texas A amp M University

38 Sharma h K Swami M (2010) Effect of Turning Lane at Busy Signalized

At-Grade Intersection under Mixed Traffic in India European Transport

52(2)

39 Shaw T (2003) NCHRP Synthesis 311 --Performance Measures of

Operational

40 Effectiveness for Highway Segments and Systems Transportation Research

BoardWashington DC 2003

41 Sunkari S (2004) The benefits of retiming traffic signals ITE journal

74(4)26ndash29

42 Transportation Research Board (2010) Highway Capacity Manual 2010

National Research Council Washington DC

43 Tsubota T and Bhaskar A and Chung E and Billot R (2011) Arterial

traffic congestion analysis using Bluetooth Duration data Australasian

Transport Research Forum Proceedings

44 US Department of Transportation (Internet) Benefit of Using Intelligent

Transportation Systems in Work Zones ndash A Summary Report 2008 (accessed

September 2010)

httpwwwopsfhwadotgovwzitswz_its_benefits_summwz_its_benefits_s

ummpdf

103

45 US Department of Transportation (Internet) DSRC Frequently Asked

Questions 2010 (accessed August 2010)

httpwwwintellidriveusaorgaboutdsrc-faqsphp

46 US Department of Transportation (Internet) ITS Strategic Research Plan

2010-2014 2010 (accessed August 2010)

httpwwwitsdotgovstrat_planindexhtm

47 Wang L (2009) On development of intellidrive-based red light running

collision avoidance system California PATH Program University of

California Berkeley

48 Wasson JS Sturdevant JR Bullock DM (2008) Real-Time Travel Time

Estimates Using Media Access Control Address Matching ITE Journal 78(6)

PP 20--23

49 Wolfle G P Wertz and F M Landstorfer (1999) Performance accuracy

and generalization capability of indoor propagation models in different types

of buildings in IEEE Int Symposium on Personal Indoor and Mobile Radio

Communications Osaka Japan

50 Wolfe M and Monsere C and Koonce P and Bertini RL (2007)

Improving Arterial Performance Measurement Using Traffic Signal System

Data Presentation for ITE District 6 Annual Meeting July 2007 Portland

104

APPENDICES

105

APPENDIX A BLUETOOTH INQUIRY PROCEDURE

The inquiry procedure used to discover active Bluetooth devices was introduced

earlier This section provides more detail on the inquiry process that is part of the

Bluetooth discovery protocol

The inquiry procedure in Bluetooth technology is used by a discovering device to

search for other enabled Bluetooth devices within communication range The vehicle

movement data collection methods proposed in this research utilizes this inquiry

process to discover active Bluetooth devices within the discovery range of the DCUs

The details of the inquiry mechanism are beyond the scope of this research However

it is necessary to provide the readers with some background on this process for a

better understanding of the system The wireless-based data collection approaches

presented in this research do not change the standard inquiry mechanism defined in

the Bluetooth protocol specifications

Inquiry is conducted on 32 of the 79 Bluetooth frequencies (other frequencies are

reserved for data communication purposes) The discovery process requires a

Bluetooth device to be in the inquiry sub state when an unknown device is in the

inquiry scan sub state A Bluetooth device enters the inquiry sub state when it is

searching for other Bluetooth devices If a Bluetooth device is in the inquiry scan sub

state it is listening for other inquiring devices An inquiring device transmits inquiry

packets frequently while a scanning device searches scan frequencies at a slower rate

allowing the two devices to find each other relatively quickly Devices in inquiry scan

106

sub state listen on a specific frequency for an inquiry scan window of 1125ms within

a 128 second time interval Every 128 seconds it randomly switches (hops) to other

frequencies The 32 frequencies used for the inquiry procedure are split into two

trains A and B of 16 frequencies each The discovering device does not know which

frequencies its neighbors are currently on so it repeatedly cycles through 16

frequencies within either the A or B train The Bluetooth specification dictates that

upon entering the inquiry sub state an inquiring device repeats a train (A or B) for at

least 256 iterations which takes 256 seconds (each transmission of a train requires

10ms) After 256 seconds the inquiring device switches trains If the device to be

discovered happens to listen on one of the 16 frequencies of that train then it may

receive an ID packet from the inquiring device that is the discovery has occurred At

this stage the device being discovered stops scanning for other inquiry packets and

waits for the handshake process required for a Bluetooth connection If it does not

hear back from the first inquiring device it returns to the scan sub state

For a single inquiry attempt the probability distribution of the inquiry time is the

key to selecting the appropriate inquiry duration The time until a scanning device re-

enters the scan sub state and begins a 1125ms scan window is uniformly distributed

on (0 128) seconds If the scanning frequency is in the inquiry train the scanning

device receives an initial inquiry packet in a time within the scan window distributed

on (0 1125) ms In the simplest form for each inquiry cycle period the probability

of detecting a unique MAC address within the discovery range can be computed from

a theoretical conditional binomial distribution However researchers tend to use

107

results from empirical studies for discovery probabilities If the scan frequency is not

in the train the scanning device drops out of scan sub state but returns 128 s after the

beginning of the previous scan window using a different scan frequency Each time

the scanning device returns to the scan sub state the trains will have either swapped a

frequency or the inquiring device may have switched the train on which it is

transmitting

Due to the theoretical mechanisms of Bluetooth operations discovery process

may not always find all Bluetooth devices in the area For example a device (such as

a cellphone) might be listening on a frequency outside the current train being

scanned Then the device is not discovered within the first train of the inquiry but in

the second when the train is switched However if they switch the train and scan

frequency at the same time and again they happen to be on different trains the device

may go undetected for another cycle Nicolai and Kenn (2007) have calculated

theoretical discovery probabilities for different cycle lengths According to their study

(see Table A1 for the summary) with a cycle length of 384 seconds nearby devices

are detected 993 of the time (this percentage is calculated for a single inquiry

attempt) Repeating the inquiry cycles consecutively can increase this discovery

likelihood

108

Table A1 Discovery probability of Bluetooth devices in relation to inquiry time

(Nicolai amp Kenn 2007)

Inquiry Time (sec) Discovery Probability ()

128 494

256 991

384 993

64 998

1024 999

Typically mobile devices actively listen to all transmissions and this can

significantly waste battery power To minimize power consumption a small

percentage of the Bluetooth mobile devices will be active only during actual data

transfers It is important to mention that the chance of discovery may significantly

drop for some Bluetooth devices that implement some sort of power management

mechanisms in which the Bluetooth module goes on and off periodically to save

battery power There is no way to prevent these devices from entering this ldquosilentrdquo

mode remotely

109

APPENDIX B TRILATERATION ALGORITHM CODE

The literature on global positioning system (GPS) and Bluetooth localization have

proposed several methods for location estimation according to a number of known

reference points also known as Triangulation In general these methods can be

divided into two categories In the first category it is assumed that the accurate

distance between a target point and at least three known reference points is known In

this scenario a one-step triangulation technique can be used to find the relative

location of the target point (Feldmann et al 2003) In the second category the

assumption of having accurate distances from some reference points is no longer

made Instead distance estimates from the target point to at least three reference

points are known In this case iterative trilateration techniques tend to generate the

most accurate results (Lau et al 2008) Since high error rates for Bluetooth signal

propagation models have been reported in the literature the efforts in developing an

accurate localization algorithm for this research mainly concentrated on an iterative

trilateration technique that can better utilize the distance estimates

The trilateration implementation utilized PyBluez which makes it straightforward

to implement an ldquoinquiry with RSSI moderdquo that obtains RSSI values at the same time

that MAC addresses are read PyBluez is an effort to create Python routines around

Bluetooth system resources to allow Python developers to easily and quickly create

Bluetooth applications Python is a versatile and powerful dynamically typed object

oriented language providing syntactic clarity along with built-in memory

110

management so that the programmer can focus on the algorithm at hand without

worrying about memory leaks or matching braces PyBluez works on GNULinux and

Microsoft Windows It is freely available under the GNU General Public License

PyBluez is a Python extension module written in the C programming language that

provides access to system Bluetooth resources in an object oriented modular manner

Some other libraries such as Math and Numpy are utilized for matrix calculations

The iterative trilateration procedure coded and used in this research is presented next

This code implements the iterative process to locate theintersection location of three circlesknown by their radii values

import pickleimport mathimport Test

location=[]file=open(RSSItxt r)RSSI=filereadlines()

n=0for item in RSSI

RSSI[n]=itemrstrip()split()

RSSI[n][0]=int(RSSI[n][0])RSSI[n][1]=int(RSSI[n][1])RSSI[n][2]=int(RSSI[n][2])

n=n+1

def RSSItoD1(rssi)return (mathpow(10(rssi+510301)7624324) - 878673)



============================================================================== def RSSItoD1(rssi) return (7064mathlog(rssi-422436))

111

def RSSItoD2(rssi) return (65811mathlog(rssi-365388)) def RSSItoD3(rssi) return (77221mathlog(rssi-419810))==============================================================================

print RSSItoD()for item in RSSI

tryreload(Test)TestAlgRun(str(RSSItoD1(item[0]))[5] +

+str(RSSItoD2(item[1]))[5] + + str(RSSItoD3(item[2]))[5])

except Exceptionprint Exception Occurred

import mathfrom numpy import matrix

SetupsX=matrix(0 10 0)Y=matrix(0 0 10)P0=matrix(500 3606 6083)P=matrix(00 00 00)alpha=matrix(00 00 10 00 00 10 00 00 10)U=matrix(10 10 00)lamda = 10

def UpdateAlpha(U)for i in range(3)

alpha[i0]=(X[0i]-U[00])(P[0i]-U[02])alpha[i1]=(Y[0i]-U[01])(P[0i]-U[02])

def UpdatePI(U)for i in range(3)

P[0i]=mathsqrt(mathpow(X[0i]-U[00]2) +mathpow(Y[0i]-U[01]2)) + U[02]

def ABSdelP(delP)for i in range(3)

delP[0i]=mathfabs(delP[0i])

def SetU()global Uw1 = (X[00]+X[01]+X[02])30w2 = (Y[00]+Y[01]+Y[02])30

112

w3 = (w1+w2)20U=matrix(str(w1) + + str(w2)+ +str(w3))

def SetU2()global Uw1 = X[00]+1w2 = Y[00]+1w3 = (w1+w2)20U=matrix(str(w1) + + str(w2)+ +str(w3))

Algorithm Execution Bodydef AlgRun(radii)

global lamdaglobal UP0=matrix(radii)SetU()while (lamda gt 0001)

UpdatePI(U)delP=P-P0UpdateAlpha(U)delX=(alphaI)(delPT)lamda=mathsqrt(mathpow(delX[00]2) +

mathpow(delX[10]2))U=U+(delXT)

print 8s8s (U[00] U[01])

if __name__== __main__AlgRun(15 25 20)

113

APPENDIX C PARTICLE SWARM OPTIMIZATION ALGORITHM IN

VISUAL BASIC

Module PSORandom number seedsFriend Z As Double

Control parametersFriend Random_Number_Seed As Double = 1000Friend N_iteration As Double = 10000Friend N_Population As Double = 100Friend N_Replication As Integer = 5Friend N_Termination As Integer = 1000Friend C0 As Double = 1Friend C1 As Double = 2Friend C2 As Double = 2DataConst N_Data = 18SamsungFriend Samsung(N_Data 2 3) As Double RSSI DistanceLGFriend LG(N_Data 2 3) As Double

SolutionStructure Solution

Dim A1 As DoubleDim A2 As DoubleDim A3 As DoubleDim B1 As DoubleDim B2 As DoubleDim B3 As DoubleDim D1 As DoubleDim D2 As DoubleDim D3 As DoubleDim Fit As DoubleDim R As Double

End Structure

Structure ParticleDim VA1 As DoubleDim VA2 As DoubleDim VA3 As Double

114

Dim VB1 As DoubleDim VB2 As DoubleDim VB3 As DoubleDim VD1 As DoubleDim VD2 As DoubleDim VD3 As DoubleDim Solution As Solution

End Structure

Sub main()Z = Random_Number_Seed

Dim i j k l As IntegerDim counter As Integer

Dim Particle(N_Population) As ParticleDim Particle_Local_Best(N_Population) As SolutionDim Particle_Global_Best As Solution

Using MyReader As NewMicrosoftVisualBasicFileIOTextFieldParser(SamsungRSSItxt)

MyReaderTextFieldType = FileIOFieldTypeDelimitedMyReaderSetDelimiters()

Dim currentRow As String()currentRow = MyReaderReadFields()While (currentRowLength gt 0)

currentRow = MyReaderReadFields()Samsung(i 0 0) = CDbl(currentRow(0))Samsung(i 0 1) = CDbl(currentRow(1))Samsung(i 0 2) = CDbl(currentRow(2))

End WhileEnd Using

Using MyReader As NewMicrosoftVisualBasicFileIOTextFieldParser(LGRSSItxt)


Dim currentRow As String()

For i = 0 To N_Data - 1currentRow = MyReaderReadFields()LG(i 0 0) = CDbl(currentRow(0))LG(i 0 1) = CDbl(currentRow(1))LG(i 0 2) = CDbl(currentRow(2))

115

NextEnd Using

Using MyReader As NewMicrosoftVisualBasicFileIOTextFieldParser(SamsungDtxt)



For i = 0 To N_Data - 1currentRow = MyReaderReadFields()Samsung(i 1 0) = CDbl(currentRow(0))Samsung(i 1 1) = CDbl(currentRow(1))Samsung(i 1 2) = CDbl(currentRow(2))

NextEnd Using

Using MyReader As NewMicrosoftVisualBasicFileIOTextFieldParser(LGDtxt)




NextEnd Using

For k = 0 To N_Replication - 1Random_Number_Seed += 10000

Report_File(Results_Samsung_ amp k + 1 amp txt Numberof iteration Global best fit A1 A2 A3 B1 B2 B3 D1 D2 D3adj R^2 amp vbCrLf)

Report_File(Results_LG_ amp k + 1 amp txt Number ofiteration Global best fit A1 A2 A3 B1 B2 B3 D1 D2 D3 adjR^2 amp vbCrLf)

For l = 0 To 1PSO algorithmcounter = 0Particle(0)SolutionA1 = 1

116

Particle(0)SolutionA2 = 1Particle(0)SolutionA3 = 1Particle(0)SolutionB1 = 1Particle(0)SolutionB2 = 1Particle(0)SolutionB3 = 1Particle(0)SolutionD1 = 1Particle(0)SolutionD2 = 1Particle(0)SolutionD3 = 1If l = 0 Then

Particle(0)SolutionFit = Fit(Particle(0)SolutionA1 Particle(0)SolutionA2 Particle(0)SolutionA3 _

Particle(0)SolutionB1Particle(0)SolutionB2 Particle(0)SolutionB3 _

Particle(0)SolutionD1Particle(0)SolutionD2 Particle(0)SolutionD3 SamsungParticle(0)SolutionR)

ElseParticle(0)SolutionFit =

Fit(Particle(0)SolutionA1 Particle(0)SolutionA2 Particle(0)SolutionA3 _


Particle(0)SolutionD1Particle(0)SolutionD2 Particle(0)SolutionD3 LG Particle(0)SolutionR)

End If

Copy_Solution(Particle_Local_Best(0)Particle(0)Solution)

Copy_Solution(Particle_Global_BestParticle_Local_Best(0))

InitializationFor i = 1 To N_Population - 1

If Random(Z) gt 05 Then Particle(i)SolutionA1 = 1 Random(Z)Else Particle(i)SolutionA1 = -1 Random(Z)End IfIf Random(Z) gt 05 Then Particle(i)SolutionA2 = 1 Random(Z)Else Particle(i)SolutionA2 = -1 Random(Z)End IfIf Random(Z) gt 05 Then Particle(i)SolutionA3 = 1 Random(Z)

117

Else Particle(i)SolutionA3 = -1 Random(Z)End IfParticle(i)SolutionB1 = 1 Random(Z)Particle(i)SolutionB2 = 1 Random(Z)Particle(i)SolutionB3 = 1 Random(Z)

Test

Particle(i)SolutionA1 = Random_Uniform(10100)



Particle(i)SolutionB1 = Random(Z)Particle(i)SolutionB2 = Random(Z)Particle(i)SolutionB3 = Random(Z)Particle(i)SolutionD1 = Random(Z)Particle(i)SolutionD2 = Random(Z)Particle(i)SolutionD3 = Random(Z)

If l = 0 ThenParticle(i)SolutionFit =

Fit(Particle(i)SolutionA1 Particle(i)SolutionA2 Particle(i)SolutionA3 _

Particle(i)SolutionB1Particle(i)SolutionB2 Particle(i)SolutionB3 _

Particle(i)SolutionD1Particle(i)SolutionD2 Particle(i)SolutionD3 Samsung Particle(i)SolutionR)

ElseParticle(i)SolutionFit =



Particle(i)SolutionD1Particle(i)SolutionD2 Particle(i)SolutionD3 LG Particle(i)SolutionR)

End If

Copy_Solution(Particle_Local_Best(i)Particle(i)Solution)

If Particle_Global_BestFit gt Particle_Local_Best(i)Fit Then

118

Copy_Solution(Particle_Global_Best Particle_Local_Best(i))

End If

Particle(i)VA1 = Random(Z)Particle(i)VA2 = Random(Z)Particle(i)VA3 = Random(Z)Particle(i)VB1 = Random(Z)Particle(i)VB2 = Random(Z)Particle(i)VB3 = Random(Z)Particle(i)VD1 = Random(Z)Particle(i)VD2 = Random(Z)Particle(i)VD3 = Random(Z)

Next

If l = 0 ThenAdd_Report_File(Results_Samsung_ amp k + 1 amp

txt 0 amp amp Particle_Global_BestFit amp amp _Particle_Global_BestA1 amp amp

Particle_Global_BestA2 amp amp Particle_Global_BestA3 amp amp _Particle_Global_BestB1 amp amp

Particle_Global_BestB2 amp amp Particle_Global_BestB3 amp amp _Particle_Global_BestD1 amp amp

Particle_Global_BestD2 amp amp Particle_Global_BestD3 amp amp Particle_Global_BestR amp vbCrLf)

ElseAdd_Report_File(Results_LG_ amp k + 1 amp txt

0 amp amp Particle_Global_BestFit amp amp _Particle_Global_BestA1 amp amp




End If

IterationFor i = 0 To N_iteration - 1

For j = 0 To N_Population - 1Particle(j)VA1 = C0 Particle(j)VA1 + C1

Random(Z) (Particle_Local_Best(j)A1 - Particle(j)SolutionA1) + _

C2 Random(Z) (Particle_Global_BestA1 -Particle(j)SolutionA1)

119

Particle(j)VA2 = C0 Particle(j)VA2 + C1 Random(Z) (Particle_Local_Best(j)A2 - Particle(j)SolutionA2) + _




Particle(j)VB1 = C0 Particle(j)VB1 + C1 Random(Z) (Particle_Local_Best(j)B1 - Particle(j)SolutionB1) + _

C2 Random(Z) (Particle_Global_BestB1 -Particle(j)SolutionB1)





Particle(j)VD1 = C0 Particle(j)VD1 + C1 Random(Z) (Particle_Local_Best(j)D1 - Particle(j)SolutionD1) + _

C2 Random(Z) (Particle_Global_BestD1 -Particle(j)SolutionD1)





If Particle(j)VA1 gt 40 ThenParticle(j)VA1 = 40

End IfIf Particle(j)VA1 lt -40 Then

Particle(j)VA1 = -40End If

120



Particle(j)VA2 = -40End IfIf Particle(j)VA3 gt 40 Then

Particle(j)VA3 = 40End IfIf Particle(j)VA3 lt -40 Then

Particle(j)VA3 = -40End IfIf Particle(j)VB1 gt 40 Then

Particle(j)VB1 = 40End IfIf Particle(j)VB1 lt -40 Then

Particle(j)VB1 = -40End IfIf Particle(j)VB2 gt 40 Then




Particle(j)VB3 = -40End IfIf Particle(j)VD1 gt 40 Then

Particle(j)VD1 = 40End IfIf Particle(j)VD1 lt -40 Then

Particle(j)VD1 = -40End IfIf Particle(j)VD2 gt 40 Then




Particle(j)VD3 = -40

121

End If

Particle(j)SolutionA1 += Particle(j)VA1Particle(j)SolutionA2 += Particle(j)VA2Particle(j)SolutionA3 += Particle(j)VA3Particle(j)SolutionB1 += Particle(j)VB1Particle(j)SolutionB2 += Particle(j)VB2Particle(j)SolutionB3 += Particle(j)VB3Particle(j)SolutionD1 += Particle(j)VD1Particle(j)SolutionD2 += Particle(j)VD2Particle(j)SolutionD3 += Particle(j)VD3

If l = 0 ThenParticle(j)SolutionFit =

Fit(Particle(j)SolutionA1 Particle(j)SolutionA2Particle(j)SolutionA3 _

Particle(j)SolutionB1 Particle(j)SolutionB2 Particle(j)SolutionB3 _

Particle(j)SolutionD1 Particle(j)SolutionD2 Particle(j)SolutionD3 Samsung Particle(j)SolutionR)

ElseParticle(j)SolutionFit =

Fit(Particle(j)SolutionA1 Particle(j)SolutionA2 Particle(j)SolutionA3 _


Particle(j)SolutionD1 Particle(j)SolutionD2 Particle(j)SolutionD3 LG Particle(j)SolutionR)

End If

If Particle_Local_Best(j)Fit gt Particle(j)SolutionFit Then

Copy_Solution(Particle_Local_Best(j) Particle(j)Solution)

If Particle_Global_BestFit gt Particle_Local_Best(j)Fit Then

counter = 0Copy_Solution(Particle_Global_Best

Particle_Local_Best(j))If l = 0 Then

Add_Report_File(Results_Samsung_ amp k + 1 amp txt i + 1 amp amp Particle_Global_BestFit amp amp _

Particle_Global_BestA1 amp amp Particle_Global_BestA2 amp amp Particle_Global_BestA3 amp amp _

122

Particle_Global_BestB1 amp amp Particle_Global_BestB2 amp amp Particle_Global_BestB3 amp amp _

Particle_Global_BestD1 amp amp Particle_Global_BestD2 amp amp Particle_Global_BestD3 amp amp Particle_Global_BestR amp vbCrLf)

ElseAdd_Report_File(Results_LG_ amp

k + 1 amp txt i + 1 amp amp Particle_Global_BestFit amp amp _Particle_Global_BestA1 amp amp




End IfElse

counter += 1End If

End IfNextIf counter gt N_Termination Then

Exit ForEnd If

NextNext

Next

End Sub

Function Fit(ByVal A1 As Double ByVal A2 As Double ByVal A3 AsDouble ByVal B1 As Double ByVal B2 As Double ByVal B3 As Double ByVal D1 As Double ByVal D2 As Double ByVal D3 As Double ByValData() As Double ByRef R As Double) As Double

Dim i As IntegerFit = 0Dim Average SSTO SSE Average1 Average2 Average3 R1

R2 R3 As Double

6 parameters logFor i = 0 To N_Data - 1

Fit += (Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1) B1) - D1 Data(i 0 0)) ^ 2 _

+ (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A2) B2) -D2 Data(i 0 1)) ^ 2 + _

(Data(i 1 2) - 10 ^ ((Data(i 0 2) - A3) B3) - D3 Data(i 0 2)) ^ 2

123

Fit += (Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1) B1)- MathE ^ (D1 Data(i 0 0))) ^ 2 _

+ (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A2) B2) -MathE ^ (D2 Data(i 0 1))) ^ 2 + _

(Data(i 1 2) - 10 ^ ((Data(i 0 2) - A3) B3) -MathE ^ (D3 Data(i 0 2))) ^ 2

Next

SSTO = 0SSE = 0Average1 = 0Average2 = 0Average3 = 0For i = 0 To 17

Average1 += Data(i 1 0)NextAverage1 = Average1 18

For i = 0 To 17Average2 += Data(i 1 1)

NextAverage2 = Average2 18


NextAverage3 = Average3 18For i = 0 To 17

SSTO += (Data(i 1 0) - Average) ^ 2SSE += (Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1)

B1) - D1 Data(i 0 0)) ^ 2SSE += (Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1) B1)

- MathE ^ (D1 Data(i 0 0))) ^ 2NextR1 = 1 - (SSE (18 - 3)) (SSTO (18 - 1))SSTO = 0SSE = 0For i = 0 To 17



- MathE ^ (D2 Data(i 0 1))) ^ 2NextR2 = 1 - (SSE (18 - 3)) (SSTO (18 - 1))SSTO = 0

124

2

SSE = 0For i = 0 To 17



- MathE ^ (D3 Data(i 0 2))) ^ 2NextR3 = 1 - (SSE (18 - 3)) (SSTO (18 - 1))

R = (R1 + R2 + R3) 3

2 parameters logFor i = 0 To N_Data - 1 Fit += (Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1)

B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

Next

2 parameters log (with penaly)For i = 0 To N_Data - 1 Fit += (Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1)

B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

2 _ + ((Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1) B1)) -

(Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1))) ^ 2 _ + ((Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) -


(Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1))) ^ 2Next

2 parameters with constraints (by penalty function)For i = 0 To N_Data - 1 Fit += (Data(i 1 0) - B1 10 ^ (Data(i 0 0) - A1))

^ 2 _ + (Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1)) ^ 2

+ _ (Data(i 1 2) - B1 10 ^ (Data(i 0 2) - A1)) ^ 2 _ + ((Data(i 1 0) - B1 10 ^ (Data(i 0 0) - A1)) -

(Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1))) ^ 2 _

125

2

+ ((Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1)) -(Data(i 1 2) - B1 10 ^ (Data(i 0 2) - A1))) ^ 2 _

+ ((Data(i 1 2) - B1 10 ^ (Data(i 0 2) - A1)) -(Data(i 1 0) - B1 10 ^ (Data(i 0 0) - A1))) ^ 2

Next

SSTO = 0SSE = 0Average = 0For i = 0 To 17 Average += Data(i 1 0) + Data(i 1 1) + Data(i 1

2)NextAverage = Average 18 3For i = 0 To 18 SSTO += (Data(i 1 0) - Average) ^ 2 + (Data(i 1 1)

- Average) ^ 2 + (Data(i 1 2) - Average) ^ 2 SSE += (Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1)

B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

NextR = 1 - (SSE (18 3 - 2)) (SSTO (18 3 - 1))

Return Fit 3 18

End Function

Sub Copy_Solution(ByRef A As Solution ByVal B As Solution)AA1 = BA1AA2 = BA2AA3 = BA3AB1 = BB1AB2 = BB2AB3 = BB3AD1 = BD1AD2 = BD2AD3 = BD3AFit = BFitAR = BR

End Sub

Function Random(ByRef Z As Double) As Double

126

Linear conguential generatorDim M a c As DoubleM = 2 ^ 31 - 1a = 62089911c = 0Z = ((a Z + c) Mod M)Return Z M

End Function

Function Random_Uniform(ByVal Min As Integer ByVal Max AsInteger) As Integer

Return Int(Random(Z) (Max - Min + 1)) + Min

End Function

Sub Report_File(ByVal File_Name As String ByVal Temp_String AsString)

Delete fileIf MyComputerFileSystemFileExists(File_Name) Then

MyComputerFileSystemDeleteFile(File_Name)End If

Write to fileMyComputerFileSystemWriteAllText(File_Name Temp_String

True)

End Sub

Sub Add_Report_File(ByVal File_Name As String ByVal Temp_String As String)


True)

End SubEnd Module

To this end two approaches for wirelessly collecting vehicle movement over a short

road segment were explored One approach utilized the collection and triangulation of

wireless signal strength data and demonstrated the capabilities and limitations of this

approach The second approach focused on developing methods for utilizing wireless

signal strength data for vehicle point detection and identification

The vehicle point detection methods developed were applied to collect travel time

data over signalized arterial roads and to collect intersection delay data for a three

way stop controlled intersection The results from these case studies indicate a

significant advantage in the proposed data collection system over the existing data

collection approaches presented in the literature


February 22 2013

All Rights Reserved


Data Collection

by

Amirali Saeedi

A DISSERTATION

submitted to




degree of





February 22 2013

APPROVED








ACKNOWLEDGEMENTS








TABLE OF CONTENTS

Page

1 INTRODUCTION 1















3 METHODOLOGY 34





Page









5 CONCLUSIONS 95

BIBLIOGRAPHY 98

APPENDICES 104




LIST OF FIGURES

Figure Page




















Figure Page






LIST OF TABLES

Table Page












DEDICATION


1 INTRODUCTION



















2






















3






the problems






functions










4






















DCU 1 DCU 2 DCU 3

5











6





















research

7











8


to the DCU


















9























10











11

2 LITERATURE REVIEW




operation






briefly discussed









12























13














Metric Metric





14

Metric Metric


















15






samples















16























17















18



Inductive Loop









measurements










Microwave Radar






stopped vehicles

19

(Continued)


Infrared

(Laser Radar)








Ultrasonic









Bluetooth



bull Non-intrusive




bull Spatial errors

20

(Continued)


Video Image

Processor




















21






















22
















(PBSampJ 2001)






23






















24






















25












2012)








26























27























28

















comparison






29








RELATIONSHIP














30












results










31









al 2008)




applications






regulation


32























33




running prediction







34

3 METHODOLOGY





















35






















36






reliability



Class I 300ft

Class II 33ft

Class III 3ft










37






















38






















39


















this study




40























41





1 119889














Equation 32




42






షరబమఱళ


















43











presented next











44



Grenander 1990)




















45

















equation





46








value





119909(119905 + 1) = 119909(119905) + 119907(119905 + 1)









47











ଶ









48













49









Appendix section B


MODEL








50



51














1 7 -4 22 -79 -86 -58

2 1 1 2 -52 -70 -66

3 8 11 21 -69 -69 -68

4 5 10 6 -61 -59 -70

5 4 2 11 -57 -73 -62

6 5 13 12 -68 -61 -71

7 7 -2 16 -72 -71 -62

8 1 -1 1 -60 -74 -63

52

9 9 19 23 -70 -59 -73

10 3 22 2 -81 -57 -86

11 8 7 23 -69 -73 -65

12 2 12 0 -60 -52 -72

13 4 3 9 -55 -73 -61

14 6 16 10 -64 -59 -70

15 6 24 13 -80 -62 -73

16 2 11 2 -63 -58 -78

17 3 18 0 -65 -44 -68

18 9 19 18 -77 -67 -72











53


0 20 40 60 80









50

0

-50

Error

(dis

tance) -100

-150

-200

-250-

-300-

Estimated Distance

Error

RSSI-



100

54


















55



Distance -4 22 235479 266011 128617 69664 169191 12086 1 2 105718 166782 166782 63365 633656 6876 11 21 227846 250745 113351 76476 178288 4614 10 6 98085 128617 13625 80814 753122 2454 2 11 174415 189681 90452 85775 157753 8128 13 12 174415 174415 128617 99999 132830 3262 -2 16 281277 296543 159149 83619 188778 10754 -1 1 159149 197314 143883 71398 109409 12848 19 23 204947 159149 182048 13624 119434 12293 22 2 189681 143883 182048 13391 106354 12193 7 23 250745 281277 143883 69750 169301 6069 12 0 113351 5992 220213 12670 036762 0765 3 9 143883 189681 113351 63983 118166 4413 16 10 182048 159149 120984 12067 149317 6307 24 13 235479 113351 189681 23794 16048 3054 11 2 113351 75186 151516 12333 693284 5109 18 0 159149 44654 21258 16590 468432 4891 19 18 243112 220213 159149 12275 170651 6788

Ave 6828 STD 3763









56






















57






















58























59

0

200

400

600

800

1000

-120

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


0

100

200

300

400

500

600

700

800

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10

0 0 5 10 15 20 25 30 35

RSSI

Distance


Sample Data

60

-100 0 100 200 300 400 500 600 700 800

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


Data














61






















62























63







64




C2BBD0 6262012 170306 C2BBD0 6262012 170310 C2BBD0 6262012 170315






65















group

66





Pei et al 2010)














DCU




67




















68












69















passes the DCU








70
















71




72










vehicle









73




74




75











76























77



the signal







78

















79


decrease

80














of accuracy







81


















and 64th Ave

82



DATA








83





times


RSSI DATA













84























85







Metho d




2 First Last (F+L)2

RSSI Method

1 128 135 131 125 124 004 011 007 001 2 225 148 206 149 149 036 001 017 000 3 212 212 212 203 203 009 009 009 000 4 249 136 212 139 135 114 001 037 004 5 151 301 226 251 253 102 008 027 002 6 238 134 206 137 133 105 001 033 004 7 141 259 220 229 229 048 030 009 000 8 258 245 251 239 240 018 005 011 001 9 158 256 227 247 246 048 010 019 001

10 341 202 252 156 154 147 008 058 002 11 151 143 147 142 140 011 003 007 002 12 142 140 141 134 134 008 006 007 000 13 246 233 239 241 240 006 007 001 001 14 202 140 151 138 136 026 004 015 002 15 203 203 203 156 153 010 010 010 003 16 234 229 231 226 225 009 004 006 001 17 144 148 146 139 138 006 010 008 001 18 246 248 247 243 242 004 006 005 001 19 155 205 200 153 154 001 011 006 001 20 258 304 301 303 303 005 001 002 000

AVG (sec) 2785 73 147 135 STDV (sec) 2988 64 1414 122

86










segments




Average RSSI Method


1 1955 (1312) 890 (597) 1145 (768) 265 (178) 2 1305 (876) 475 (319) 770 (517) 315 (211)


1 855 (629) 610 (449) 590 (434) 305 (224) 2 611 (449) 537 (395) 532 (391) 353 (259)


1 2785 (2193) 730 (575) 1470 (1157) 135 (106) 2 1568 (1235) 384 (303) 790 (622) 268 (211)


1 3310 (2348) 575 (408) 1600 (1135) 150 (106) 2 2358 (1672) 295 (209) 1216 (862) 295 (209)

Average

1 2226 (1620) 701 (507) 1201 (874) 214 (154) 2 1460 (1058) 423 (306) 827 (598) 308 (223)

All 1843 (1339) 562 (407) 1014 (736) 261 (188)

87






















poor accuracy

88






Volume



Samples















89











deployed


90


















passed the DCU unit

91

DCU 1 DCU 3 DCU 2

NW

Kin

g s B

lvd

Monroe Ave

Dutch Bros

Rogers Graff


X

X

150 ft 50 ft













92








DCUs (sec)



Address DCU1 ndash

DCU2

DCU2 ndash

DCU3

DCU1 ndash

DCU2

DCU2 ndash




93

D

23 BE6BEDC D 7 4 7 4 0 0

24 BE461DE4 25 3EECAF75 14 7 14 7 0 0


Max (seconds) 5 6


















94





95

5 CONCLUSIONS





















96


















platform





97



98

BIBLIOGRAPHY




























99









Philadelphia PA





PP 2834--2839




2007







on IEEE PP1--5




100



















297--303




227ndash238






101












program)





Proceedings




Vol9 No 2 pp 122--130




12 pp 101-127



102



UT-1105





WLAN





52(2)


Operational




74(4)26ndash29








September 2010)


ummpdf

103





2010-2014 2010 (accessed August 2010)




California Berkeley



PP 20--23








104

APPENDICES

105






















106























107






transmitting












likelihood

108




128 494

256 991

384 993

64 998

1024 999








mode remotely

109






















110











n=0for item in RSSI



n=n+1





111















112







print 8s8s (U[00] U[01])


113


VISUAL BASIC





End Structure


114


End Structure








End WhileEnd Using





115

NextEnd Using





NextEnd Using





NextEnd Using





116









End If





117


Test













End If



118


End If


Next











End If





119




















120


















121

End If










End If








122








End IfElse

counter += 1End If


Exit ForEnd If

NextNext

Next

End Sub



R2 R3 As Double





123




Next













124

2





R = (R1 + R2 + R3) 3


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

Next


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

2 _ + ((Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1) B1)) -





^ 2 _ + (Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1)) ^ 2


(Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1))) ^ 2 _

125

2



Next




B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

NextR = 1 - (SSE (18 3 - 2)) (SSTO (18 3 - 1))

Return Fit 3 18

End Function


End Sub


126


End Function



End Function





True)

End Sub



True)

End SubEnd Module


February 22 2013

All Rights Reserved


Data Collection

by

Amirali Saeedi

A DISSERTATION

submitted to




degree of





February 22 2013

APPROVED








ACKNOWLEDGEMENTS








TABLE OF CONTENTS

Page

1 INTRODUCTION 1















3 METHODOLOGY 34





Page









5 CONCLUSIONS 95

BIBLIOGRAPHY 98

APPENDICES 104




LIST OF FIGURES

Figure Page




















Figure Page






LIST OF TABLES

Table Page












DEDICATION


1 INTRODUCTION



















2






















3






the problems






functions










4






















DCU 1 DCU 2 DCU 3

5











6





















research

7











8


to the DCU


















9























10











11

2 LITERATURE REVIEW




operation






briefly discussed









12























13














Metric Metric





14

Metric Metric


















15






samples















16























17















18



Inductive Loop









measurements










Microwave Radar






stopped vehicles

19

(Continued)


Infrared

(Laser Radar)








Ultrasonic









Bluetooth



bull Non-intrusive




bull Spatial errors

20

(Continued)


Video Image

Processor




















21






















22
















(PBSampJ 2001)






23






















24






















25












2012)








26























27























28

















comparison






29








RELATIONSHIP














30












results










31









al 2008)




applications






regulation


32























33




running prediction







34

3 METHODOLOGY





















35






















36






reliability



Class I 300ft

Class II 33ft

Class III 3ft










37






















38






















39


















this study




40























41





1 119889














Equation 32




42






షరబమఱళ


















43











presented next











44



Grenander 1990)




















45

















equation





46








value





119909(119905 + 1) = 119909(119905) + 119907(119905 + 1)









47











ଶ









48













49









Appendix section B


MODEL








50



51














1 7 -4 22 -79 -86 -58

2 1 1 2 -52 -70 -66

3 8 11 21 -69 -69 -68

4 5 10 6 -61 -59 -70

5 4 2 11 -57 -73 -62

6 5 13 12 -68 -61 -71

7 7 -2 16 -72 -71 -62

8 1 -1 1 -60 -74 -63

52

9 9 19 23 -70 -59 -73

10 3 22 2 -81 -57 -86

11 8 7 23 -69 -73 -65

12 2 12 0 -60 -52 -72

13 4 3 9 -55 -73 -61

14 6 16 10 -64 -59 -70

15 6 24 13 -80 -62 -73

16 2 11 2 -63 -58 -78

17 3 18 0 -65 -44 -68

18 9 19 18 -77 -67 -72











53


0 20 40 60 80









50

0

-50

Error

(dis

tance) -100

-150

-200

-250-

-300-

Estimated Distance

Error

RSSI-



100

54


















55



Distance -4 22 235479 266011 128617 69664 169191 12086 1 2 105718 166782 166782 63365 633656 6876 11 21 227846 250745 113351 76476 178288 4614 10 6 98085 128617 13625 80814 753122 2454 2 11 174415 189681 90452 85775 157753 8128 13 12 174415 174415 128617 99999 132830 3262 -2 16 281277 296543 159149 83619 188778 10754 -1 1 159149 197314 143883 71398 109409 12848 19 23 204947 159149 182048 13624 119434 12293 22 2 189681 143883 182048 13391 106354 12193 7 23 250745 281277 143883 69750 169301 6069 12 0 113351 5992 220213 12670 036762 0765 3 9 143883 189681 113351 63983 118166 4413 16 10 182048 159149 120984 12067 149317 6307 24 13 235479 113351 189681 23794 16048 3054 11 2 113351 75186 151516 12333 693284 5109 18 0 159149 44654 21258 16590 468432 4891 19 18 243112 220213 159149 12275 170651 6788

Ave 6828 STD 3763









56






















57






















58























59

0

200

400

600

800

1000

-120

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


0

100

200

300

400

500

600

700

800

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10

0 0 5 10 15 20 25 30 35

RSSI

Distance


Sample Data

60

-100 0 100 200 300 400 500 600 700 800

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


Data














61






















62























63







64




C2BBD0 6262012 170306 C2BBD0 6262012 170310 C2BBD0 6262012 170315






65















group

66





Pei et al 2010)














DCU




67




















68












69















passes the DCU








70
















71




72










vehicle









73




74




75











76























77



the signal







78

















79


decrease

80














of accuracy







81


















and 64th Ave

82



DATA








83





times


RSSI DATA













84























85







Metho d




2 First Last (F+L)2

RSSI Method

1 128 135 131 125 124 004 011 007 001 2 225 148 206 149 149 036 001 017 000 3 212 212 212 203 203 009 009 009 000 4 249 136 212 139 135 114 001 037 004 5 151 301 226 251 253 102 008 027 002 6 238 134 206 137 133 105 001 033 004 7 141 259 220 229 229 048 030 009 000 8 258 245 251 239 240 018 005 011 001 9 158 256 227 247 246 048 010 019 001

10 341 202 252 156 154 147 008 058 002 11 151 143 147 142 140 011 003 007 002 12 142 140 141 134 134 008 006 007 000 13 246 233 239 241 240 006 007 001 001 14 202 140 151 138 136 026 004 015 002 15 203 203 203 156 153 010 010 010 003 16 234 229 231 226 225 009 004 006 001 17 144 148 146 139 138 006 010 008 001 18 246 248 247 243 242 004 006 005 001 19 155 205 200 153 154 001 011 006 001 20 258 304 301 303 303 005 001 002 000

AVG (sec) 2785 73 147 135 STDV (sec) 2988 64 1414 122

86










segments




Average RSSI Method


1 1955 (1312) 890 (597) 1145 (768) 265 (178) 2 1305 (876) 475 (319) 770 (517) 315 (211)


1 855 (629) 610 (449) 590 (434) 305 (224) 2 611 (449) 537 (395) 532 (391) 353 (259)


1 2785 (2193) 730 (575) 1470 (1157) 135 (106) 2 1568 (1235) 384 (303) 790 (622) 268 (211)


1 3310 (2348) 575 (408) 1600 (1135) 150 (106) 2 2358 (1672) 295 (209) 1216 (862) 295 (209)

Average

1 2226 (1620) 701 (507) 1201 (874) 214 (154) 2 1460 (1058) 423 (306) 827 (598) 308 (223)

All 1843 (1339) 562 (407) 1014 (736) 261 (188)

87






















poor accuracy

88






Volume



Samples















89











deployed


90


















passed the DCU unit

91

DCU 1 DCU 3 DCU 2

NW

Kin

g s B

lvd

Monroe Ave

Dutch Bros

Rogers Graff


X

X

150 ft 50 ft













92








DCUs (sec)



Address DCU1 ndash

DCU2

DCU2 ndash

DCU3

DCU1 ndash

DCU2

DCU2 ndash




93

D

23 BE6BEDC D 7 4 7 4 0 0

24 BE461DE4 25 3EECAF75 14 7 14 7 0 0


Max (seconds) 5 6


















94





95

5 CONCLUSIONS





















96


















platform





97



98

BIBLIOGRAPHY




























99









Philadelphia PA





PP 2834--2839




2007







on IEEE PP1--5




100



















297--303




227ndash238






101












program)





Proceedings




Vol9 No 2 pp 122--130




12 pp 101-127



102



UT-1105





WLAN





52(2)


Operational




74(4)26ndash29








September 2010)


ummpdf

103





2010-2014 2010 (accessed August 2010)




California Berkeley



PP 20--23








104

APPENDICES

105






















106























107






transmitting












likelihood

108




128 494

256 991

384 993

64 998

1024 999








mode remotely

109






















110











n=0for item in RSSI



n=n+1





111















112







print 8s8s (U[00] U[01])


113


VISUAL BASIC





End Structure


114


End Structure








End WhileEnd Using





115

NextEnd Using





NextEnd Using





NextEnd Using





116









End If





117


Test













End If



118


End If


Next











End If





119




















120


















121

End If










End If








122








End IfElse

counter += 1End If


Exit ForEnd If

NextNext

Next

End Sub



R2 R3 As Double





123




Next













124

2





R = (R1 + R2 + R3) 3


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

Next


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

2 _ + ((Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1) B1)) -





^ 2 _ + (Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1)) ^ 2


(Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1))) ^ 2 _

125

2



Next




B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

NextR = 1 - (SSE (18 3 - 2)) (SSTO (18 3 - 1))

Return Fit 3 18

End Function


End Sub


126


End Function



End Function





True)

End Sub



True)

End SubEnd Module


Data Collection

by

Amirali Saeedi

A DISSERTATION

submitted to




degree of





February 22 2013

APPROVED








ACKNOWLEDGEMENTS








TABLE OF CONTENTS

Page

1 INTRODUCTION 1















3 METHODOLOGY 34





Page









5 CONCLUSIONS 95

BIBLIOGRAPHY 98

APPENDICES 104




LIST OF FIGURES

Figure Page




















Figure Page






LIST OF TABLES

Table Page












DEDICATION


1 INTRODUCTION



















2






















3






the problems






functions










4






















DCU 1 DCU 2 DCU 3

5











6





















research

7











8


to the DCU


















9























10











11

2 LITERATURE REVIEW




operation






briefly discussed









12























13














Metric Metric





14

Metric Metric


















15






samples















16























17















18



Inductive Loop









measurements










Microwave Radar






stopped vehicles

19

(Continued)


Infrared

(Laser Radar)








Ultrasonic









Bluetooth



bull Non-intrusive




bull Spatial errors

20

(Continued)


Video Image

Processor




















21






















22
















(PBSampJ 2001)






23






















24






















25












2012)








26























27























28

















comparison






29








RELATIONSHIP














30












results










31









al 2008)




applications






regulation


32























33




running prediction







34

3 METHODOLOGY





















35






















36






reliability



Class I 300ft

Class II 33ft

Class III 3ft










37






















38






















39


















this study




40























41





1 119889














Equation 32




42






షరబమఱళ


















43











presented next











44



Grenander 1990)




















45

















equation





46








value





119909(119905 + 1) = 119909(119905) + 119907(119905 + 1)









47











ଶ









48













49









Appendix section B


MODEL








50



51














1 7 -4 22 -79 -86 -58

2 1 1 2 -52 -70 -66

3 8 11 21 -69 -69 -68

4 5 10 6 -61 -59 -70

5 4 2 11 -57 -73 -62

6 5 13 12 -68 -61 -71

7 7 -2 16 -72 -71 -62

8 1 -1 1 -60 -74 -63

52

9 9 19 23 -70 -59 -73

10 3 22 2 -81 -57 -86

11 8 7 23 -69 -73 -65

12 2 12 0 -60 -52 -72

13 4 3 9 -55 -73 -61

14 6 16 10 -64 -59 -70

15 6 24 13 -80 -62 -73

16 2 11 2 -63 -58 -78

17 3 18 0 -65 -44 -68

18 9 19 18 -77 -67 -72











53


0 20 40 60 80









50

0

-50

Error

(dis

tance) -100

-150

-200

-250-

-300-

Estimated Distance

Error

RSSI-



100

54


















55



Distance -4 22 235479 266011 128617 69664 169191 12086 1 2 105718 166782 166782 63365 633656 6876 11 21 227846 250745 113351 76476 178288 4614 10 6 98085 128617 13625 80814 753122 2454 2 11 174415 189681 90452 85775 157753 8128 13 12 174415 174415 128617 99999 132830 3262 -2 16 281277 296543 159149 83619 188778 10754 -1 1 159149 197314 143883 71398 109409 12848 19 23 204947 159149 182048 13624 119434 12293 22 2 189681 143883 182048 13391 106354 12193 7 23 250745 281277 143883 69750 169301 6069 12 0 113351 5992 220213 12670 036762 0765 3 9 143883 189681 113351 63983 118166 4413 16 10 182048 159149 120984 12067 149317 6307 24 13 235479 113351 189681 23794 16048 3054 11 2 113351 75186 151516 12333 693284 5109 18 0 159149 44654 21258 16590 468432 4891 19 18 243112 220213 159149 12275 170651 6788

Ave 6828 STD 3763









56






















57






















58























59

0

200

400

600

800

1000

-120

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


0

100

200

300

400

500

600

700

800

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10

0 0 5 10 15 20 25 30 35

RSSI

Distance


Sample Data

60

-100 0 100 200 300 400 500 600 700 800

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


Data














61






















62























63







64




C2BBD0 6262012 170306 C2BBD0 6262012 170310 C2BBD0 6262012 170315






65















group

66





Pei et al 2010)














DCU




67




















68












69















passes the DCU








70
















71




72










vehicle









73




74




75











76























77



the signal







78

















79


decrease

80














of accuracy







81


















and 64th Ave

82



DATA








83





times


RSSI DATA













84























85







Metho d




2 First Last (F+L)2

RSSI Method

1 128 135 131 125 124 004 011 007 001 2 225 148 206 149 149 036 001 017 000 3 212 212 212 203 203 009 009 009 000 4 249 136 212 139 135 114 001 037 004 5 151 301 226 251 253 102 008 027 002 6 238 134 206 137 133 105 001 033 004 7 141 259 220 229 229 048 030 009 000 8 258 245 251 239 240 018 005 011 001 9 158 256 227 247 246 048 010 019 001

10 341 202 252 156 154 147 008 058 002 11 151 143 147 142 140 011 003 007 002 12 142 140 141 134 134 008 006 007 000 13 246 233 239 241 240 006 007 001 001 14 202 140 151 138 136 026 004 015 002 15 203 203 203 156 153 010 010 010 003 16 234 229 231 226 225 009 004 006 001 17 144 148 146 139 138 006 010 008 001 18 246 248 247 243 242 004 006 005 001 19 155 205 200 153 154 001 011 006 001 20 258 304 301 303 303 005 001 002 000

AVG (sec) 2785 73 147 135 STDV (sec) 2988 64 1414 122

86










segments




Average RSSI Method


1 1955 (1312) 890 (597) 1145 (768) 265 (178) 2 1305 (876) 475 (319) 770 (517) 315 (211)


1 855 (629) 610 (449) 590 (434) 305 (224) 2 611 (449) 537 (395) 532 (391) 353 (259)


1 2785 (2193) 730 (575) 1470 (1157) 135 (106) 2 1568 (1235) 384 (303) 790 (622) 268 (211)


1 3310 (2348) 575 (408) 1600 (1135) 150 (106) 2 2358 (1672) 295 (209) 1216 (862) 295 (209)

Average

1 2226 (1620) 701 (507) 1201 (874) 214 (154) 2 1460 (1058) 423 (306) 827 (598) 308 (223)

All 1843 (1339) 562 (407) 1014 (736) 261 (188)

87






















poor accuracy

88






Volume



Samples















89











deployed


90


















passed the DCU unit

91

DCU 1 DCU 3 DCU 2

NW

Kin

g s B

lvd

Monroe Ave

Dutch Bros

Rogers Graff


X

X

150 ft 50 ft













92








DCUs (sec)



Address DCU1 ndash

DCU2

DCU2 ndash

DCU3

DCU1 ndash

DCU2

DCU2 ndash




93

D

23 BE6BEDC D 7 4 7 4 0 0

24 BE461DE4 25 3EECAF75 14 7 14 7 0 0


Max (seconds) 5 6


















94





95

5 CONCLUSIONS





















96


















platform





97



98

BIBLIOGRAPHY




























99









Philadelphia PA





PP 2834--2839




2007







on IEEE PP1--5




100



















297--303




227ndash238






101












program)





Proceedings




Vol9 No 2 pp 122--130




12 pp 101-127



102



UT-1105





WLAN





52(2)


Operational




74(4)26ndash29








September 2010)


ummpdf

103





2010-2014 2010 (accessed August 2010)




California Berkeley



PP 20--23








104

APPENDICES

105






















106























107






transmitting












likelihood

108




128 494

256 991

384 993

64 998

1024 999








mode remotely

109






















110











n=0for item in RSSI



n=n+1





111















112







print 8s8s (U[00] U[01])


113


VISUAL BASIC





End Structure


114


End Structure








End WhileEnd Using





115

NextEnd Using





NextEnd Using





NextEnd Using





116









End If





117


Test













End If



118


End If


Next











End If





119




















120


















121

End If










End If








122








End IfElse

counter += 1End If


Exit ForEnd If

NextNext

Next

End Sub



R2 R3 As Double





123




Next













124

2





R = (R1 + R2 + R3) 3


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

Next


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

2 _ + ((Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1) B1)) -





^ 2 _ + (Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1)) ^ 2


(Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1))) ^ 2 _

125

2



Next




B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

NextR = 1 - (SSE (18 3 - 2)) (SSTO (18 3 - 1))

Return Fit 3 18

End Function


End Sub


126


End Function



End Function





True)

End Sub



True)

End SubEnd Module


February 22 2013

APPROVED








ACKNOWLEDGEMENTS








TABLE OF CONTENTS

Page

1 INTRODUCTION 1















3 METHODOLOGY 34





Page









5 CONCLUSIONS 95

BIBLIOGRAPHY 98

APPENDICES 104




LIST OF FIGURES

Figure Page




















Figure Page






LIST OF TABLES

Table Page












DEDICATION


1 INTRODUCTION



















2






















3






the problems






functions










4






















DCU 1 DCU 2 DCU 3

5











6





















research

7











8


to the DCU


















9























10











11

2 LITERATURE REVIEW




operation






briefly discussed









12























13














Metric Metric





14

Metric Metric


















15






samples















16























17















18



Inductive Loop









measurements










Microwave Radar






stopped vehicles

19

(Continued)


Infrared

(Laser Radar)








Ultrasonic









Bluetooth



bull Non-intrusive




bull Spatial errors

20

(Continued)


Video Image

Processor




















21






















22
















(PBSampJ 2001)






23






















24






















25












2012)








26























27























28

















comparison






29








RELATIONSHIP














30












results










31









al 2008)




applications






regulation


32























33




running prediction







34

3 METHODOLOGY





















35






















36






reliability



Class I 300ft

Class II 33ft

Class III 3ft










37






















38






















39


















this study




40























41





1 119889














Equation 32




42






షరబమఱళ


















43











presented next











44



Grenander 1990)




















45

















equation





46








value





119909(119905 + 1) = 119909(119905) + 119907(119905 + 1)









47











ଶ









48













49









Appendix section B


MODEL








50



51














1 7 -4 22 -79 -86 -58

2 1 1 2 -52 -70 -66

3 8 11 21 -69 -69 -68

4 5 10 6 -61 -59 -70

5 4 2 11 -57 -73 -62

6 5 13 12 -68 -61 -71

7 7 -2 16 -72 -71 -62

8 1 -1 1 -60 -74 -63

52

9 9 19 23 -70 -59 -73

10 3 22 2 -81 -57 -86

11 8 7 23 -69 -73 -65

12 2 12 0 -60 -52 -72

13 4 3 9 -55 -73 -61

14 6 16 10 -64 -59 -70

15 6 24 13 -80 -62 -73

16 2 11 2 -63 -58 -78

17 3 18 0 -65 -44 -68

18 9 19 18 -77 -67 -72











53


0 20 40 60 80









50

0

-50

Error

(dis

tance) -100

-150

-200

-250-

-300-

Estimated Distance

Error

RSSI-



100

54


















55



Distance -4 22 235479 266011 128617 69664 169191 12086 1 2 105718 166782 166782 63365 633656 6876 11 21 227846 250745 113351 76476 178288 4614 10 6 98085 128617 13625 80814 753122 2454 2 11 174415 189681 90452 85775 157753 8128 13 12 174415 174415 128617 99999 132830 3262 -2 16 281277 296543 159149 83619 188778 10754 -1 1 159149 197314 143883 71398 109409 12848 19 23 204947 159149 182048 13624 119434 12293 22 2 189681 143883 182048 13391 106354 12193 7 23 250745 281277 143883 69750 169301 6069 12 0 113351 5992 220213 12670 036762 0765 3 9 143883 189681 113351 63983 118166 4413 16 10 182048 159149 120984 12067 149317 6307 24 13 235479 113351 189681 23794 16048 3054 11 2 113351 75186 151516 12333 693284 5109 18 0 159149 44654 21258 16590 468432 4891 19 18 243112 220213 159149 12275 170651 6788

Ave 6828 STD 3763









56






















57






















58























59

0

200

400

600

800

1000

-120

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


0

100

200

300

400

500

600

700

800

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10

0 0 5 10 15 20 25 30 35

RSSI

Distance


Sample Data

60

-100 0 100 200 300 400 500 600 700 800

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


Data














61






















62























63







64




C2BBD0 6262012 170306 C2BBD0 6262012 170310 C2BBD0 6262012 170315






65















group

66





Pei et al 2010)














DCU




67




















68












69















passes the DCU








70
















71




72










vehicle









73




74




75











76























77



the signal







78

















79


decrease

80














of accuracy







81


















and 64th Ave

82



DATA








83





times


RSSI DATA













84























85







Metho d




2 First Last (F+L)2

RSSI Method

1 128 135 131 125 124 004 011 007 001 2 225 148 206 149 149 036 001 017 000 3 212 212 212 203 203 009 009 009 000 4 249 136 212 139 135 114 001 037 004 5 151 301 226 251 253 102 008 027 002 6 238 134 206 137 133 105 001 033 004 7 141 259 220 229 229 048 030 009 000 8 258 245 251 239 240 018 005 011 001 9 158 256 227 247 246 048 010 019 001

10 341 202 252 156 154 147 008 058 002 11 151 143 147 142 140 011 003 007 002 12 142 140 141 134 134 008 006 007 000 13 246 233 239 241 240 006 007 001 001 14 202 140 151 138 136 026 004 015 002 15 203 203 203 156 153 010 010 010 003 16 234 229 231 226 225 009 004 006 001 17 144 148 146 139 138 006 010 008 001 18 246 248 247 243 242 004 006 005 001 19 155 205 200 153 154 001 011 006 001 20 258 304 301 303 303 005 001 002 000

AVG (sec) 2785 73 147 135 STDV (sec) 2988 64 1414 122

86










segments




Average RSSI Method


1 1955 (1312) 890 (597) 1145 (768) 265 (178) 2 1305 (876) 475 (319) 770 (517) 315 (211)


1 855 (629) 610 (449) 590 (434) 305 (224) 2 611 (449) 537 (395) 532 (391) 353 (259)


1 2785 (2193) 730 (575) 1470 (1157) 135 (106) 2 1568 (1235) 384 (303) 790 (622) 268 (211)


1 3310 (2348) 575 (408) 1600 (1135) 150 (106) 2 2358 (1672) 295 (209) 1216 (862) 295 (209)

Average

1 2226 (1620) 701 (507) 1201 (874) 214 (154) 2 1460 (1058) 423 (306) 827 (598) 308 (223)

All 1843 (1339) 562 (407) 1014 (736) 261 (188)

87






















poor accuracy

88






Volume



Samples















89











deployed


90


















passed the DCU unit

91

DCU 1 DCU 3 DCU 2

NW

Kin

g s B

lvd

Monroe Ave

Dutch Bros

Rogers Graff


X

X

150 ft 50 ft













92








DCUs (sec)



Address DCU1 ndash

DCU2

DCU2 ndash

DCU3

DCU1 ndash

DCU2

DCU2 ndash




93

D

23 BE6BEDC D 7 4 7 4 0 0

24 BE461DE4 25 3EECAF75 14 7 14 7 0 0


Max (seconds) 5 6


















94





95

5 CONCLUSIONS





















96


















platform





97



98

BIBLIOGRAPHY




























99









Philadelphia PA





PP 2834--2839




2007







on IEEE PP1--5




100



















297--303




227ndash238






101












program)





Proceedings




Vol9 No 2 pp 122--130




12 pp 101-127



102



UT-1105





WLAN





52(2)


Operational




74(4)26ndash29








September 2010)


ummpdf

103





2010-2014 2010 (accessed August 2010)




California Berkeley



PP 20--23








104

APPENDICES

105






















106























107






transmitting












likelihood

108




128 494

256 991

384 993

64 998

1024 999








mode remotely

109






















110











n=0for item in RSSI



n=n+1





111















112







print 8s8s (U[00] U[01])


113


VISUAL BASIC





End Structure


114


End Structure








End WhileEnd Using





115

NextEnd Using





NextEnd Using





NextEnd Using





116









End If





117


Test













End If



118


End If


Next











End If





119




















120


















121

End If










End If








122








End IfElse

counter += 1End If


Exit ForEnd If

NextNext

Next

End Sub



R2 R3 As Double





123




Next













124

2





R = (R1 + R2 + R3) 3


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

Next


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

2 _ + ((Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1) B1)) -





^ 2 _ + (Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1)) ^ 2


(Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1))) ^ 2 _

125

2



Next




B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

NextR = 1 - (SSE (18 3 - 2)) (SSTO (18 3 - 1))

Return Fit 3 18

End Function


End Sub


126


End Function



End Function





True)

End Sub



True)

End SubEnd Module

ACKNOWLEDGEMENTS








TABLE OF CONTENTS

Page

1 INTRODUCTION 1















3 METHODOLOGY 34





Page









5 CONCLUSIONS 95

BIBLIOGRAPHY 98

APPENDICES 104




LIST OF FIGURES

Figure Page




















Figure Page






LIST OF TABLES

Table Page












DEDICATION


1 INTRODUCTION



















2






















3






the problems






functions










4






















DCU 1 DCU 2 DCU 3

5











6





















research

7











8


to the DCU


















9























10











11

2 LITERATURE REVIEW




operation






briefly discussed









12























13














Metric Metric





14

Metric Metric


















15






samples















16























17















18



Inductive Loop









measurements










Microwave Radar






stopped vehicles

19

(Continued)


Infrared

(Laser Radar)








Ultrasonic









Bluetooth



bull Non-intrusive




bull Spatial errors

20

(Continued)


Video Image

Processor




















21






















22
















(PBSampJ 2001)






23






















24






















25












2012)








26























27























28

















comparison






29








RELATIONSHIP














30












results










31









al 2008)




applications






regulation


32























33




running prediction







34

3 METHODOLOGY





















35






















36






reliability



Class I 300ft

Class II 33ft

Class III 3ft










37






















38






















39


















this study




40























41





1 119889














Equation 32




42






షరబమఱళ


















43











presented next











44



Grenander 1990)




















45

















equation





46








value





119909(119905 + 1) = 119909(119905) + 119907(119905 + 1)









47











ଶ









48













49









Appendix section B


MODEL








50



51














1 7 -4 22 -79 -86 -58

2 1 1 2 -52 -70 -66

3 8 11 21 -69 -69 -68

4 5 10 6 -61 -59 -70

5 4 2 11 -57 -73 -62

6 5 13 12 -68 -61 -71

7 7 -2 16 -72 -71 -62

8 1 -1 1 -60 -74 -63

52

9 9 19 23 -70 -59 -73

10 3 22 2 -81 -57 -86

11 8 7 23 -69 -73 -65

12 2 12 0 -60 -52 -72

13 4 3 9 -55 -73 -61

14 6 16 10 -64 -59 -70

15 6 24 13 -80 -62 -73

16 2 11 2 -63 -58 -78

17 3 18 0 -65 -44 -68

18 9 19 18 -77 -67 -72











53


0 20 40 60 80









50

0

-50

Error

(dis

tance) -100

-150

-200

-250-

-300-

Estimated Distance

Error

RSSI-



100

54


















55



Distance -4 22 235479 266011 128617 69664 169191 12086 1 2 105718 166782 166782 63365 633656 6876 11 21 227846 250745 113351 76476 178288 4614 10 6 98085 128617 13625 80814 753122 2454 2 11 174415 189681 90452 85775 157753 8128 13 12 174415 174415 128617 99999 132830 3262 -2 16 281277 296543 159149 83619 188778 10754 -1 1 159149 197314 143883 71398 109409 12848 19 23 204947 159149 182048 13624 119434 12293 22 2 189681 143883 182048 13391 106354 12193 7 23 250745 281277 143883 69750 169301 6069 12 0 113351 5992 220213 12670 036762 0765 3 9 143883 189681 113351 63983 118166 4413 16 10 182048 159149 120984 12067 149317 6307 24 13 235479 113351 189681 23794 16048 3054 11 2 113351 75186 151516 12333 693284 5109 18 0 159149 44654 21258 16590 468432 4891 19 18 243112 220213 159149 12275 170651 6788

Ave 6828 STD 3763









56






















57






















58























59

0

200

400

600

800

1000

-120

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


0

100

200

300

400

500

600

700

800

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10

0 0 5 10 15 20 25 30 35

RSSI

Distance


Sample Data

60

-100 0 100 200 300 400 500 600 700 800

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


Data














61






















62























63







64




C2BBD0 6262012 170306 C2BBD0 6262012 170310 C2BBD0 6262012 170315






65















group

66





Pei et al 2010)














DCU




67




















68












69















passes the DCU








70
















71




72










vehicle









73




74




75











76























77



the signal







78

















79


decrease

80














of accuracy







81


















and 64th Ave

82



DATA








83





times


RSSI DATA













84























85







Metho d




2 First Last (F+L)2

RSSI Method

1 128 135 131 125 124 004 011 007 001 2 225 148 206 149 149 036 001 017 000 3 212 212 212 203 203 009 009 009 000 4 249 136 212 139 135 114 001 037 004 5 151 301 226 251 253 102 008 027 002 6 238 134 206 137 133 105 001 033 004 7 141 259 220 229 229 048 030 009 000 8 258 245 251 239 240 018 005 011 001 9 158 256 227 247 246 048 010 019 001

10 341 202 252 156 154 147 008 058 002 11 151 143 147 142 140 011 003 007 002 12 142 140 141 134 134 008 006 007 000 13 246 233 239 241 240 006 007 001 001 14 202 140 151 138 136 026 004 015 002 15 203 203 203 156 153 010 010 010 003 16 234 229 231 226 225 009 004 006 001 17 144 148 146 139 138 006 010 008 001 18 246 248 247 243 242 004 006 005 001 19 155 205 200 153 154 001 011 006 001 20 258 304 301 303 303 005 001 002 000

AVG (sec) 2785 73 147 135 STDV (sec) 2988 64 1414 122

86










segments




Average RSSI Method


1 1955 (1312) 890 (597) 1145 (768) 265 (178) 2 1305 (876) 475 (319) 770 (517) 315 (211)


1 855 (629) 610 (449) 590 (434) 305 (224) 2 611 (449) 537 (395) 532 (391) 353 (259)


1 2785 (2193) 730 (575) 1470 (1157) 135 (106) 2 1568 (1235) 384 (303) 790 (622) 268 (211)


1 3310 (2348) 575 (408) 1600 (1135) 150 (106) 2 2358 (1672) 295 (209) 1216 (862) 295 (209)

Average

1 2226 (1620) 701 (507) 1201 (874) 214 (154) 2 1460 (1058) 423 (306) 827 (598) 308 (223)

All 1843 (1339) 562 (407) 1014 (736) 261 (188)

87






















poor accuracy

88






Volume



Samples















89











deployed


90


















passed the DCU unit

91

DCU 1 DCU 3 DCU 2

NW

Kin

g s B

lvd

Monroe Ave

Dutch Bros

Rogers Graff


X

X

150 ft 50 ft













92








DCUs (sec)



Address DCU1 ndash

DCU2

DCU2 ndash

DCU3

DCU1 ndash

DCU2

DCU2 ndash




93

D

23 BE6BEDC D 7 4 7 4 0 0

24 BE461DE4 25 3EECAF75 14 7 14 7 0 0


Max (seconds) 5 6


















94





95

5 CONCLUSIONS





















96


















platform





97



98

BIBLIOGRAPHY




























99









Philadelphia PA





PP 2834--2839




2007







on IEEE PP1--5




100



















297--303




227ndash238






101












program)





Proceedings




Vol9 No 2 pp 122--130




12 pp 101-127



102



UT-1105





WLAN





52(2)


Operational




74(4)26ndash29








September 2010)


ummpdf

103





2010-2014 2010 (accessed August 2010)




California Berkeley



PP 20--23








104

APPENDICES

105






















106























107






transmitting












likelihood

108




128 494

256 991

384 993

64 998

1024 999








mode remotely

109






















110











n=0for item in RSSI



n=n+1





111















112







print 8s8s (U[00] U[01])


113


VISUAL BASIC





End Structure


114


End Structure








End WhileEnd Using





115

NextEnd Using





NextEnd Using





NextEnd Using





116









End If





117


Test













End If



118


End If


Next











End If





119




















120


















121

End If










End If








122








End IfElse

counter += 1End If


Exit ForEnd If

NextNext

Next

End Sub



R2 R3 As Double





123




Next













124

2





R = (R1 + R2 + R3) 3


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

Next


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

2 _ + ((Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1) B1)) -





^ 2 _ + (Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1)) ^ 2


(Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1))) ^ 2 _

125

2



Next




B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

NextR = 1 - (SSE (18 3 - 2)) (SSTO (18 3 - 1))

Return Fit 3 18

End Function


End Sub


126


End Function



End Function





True)

End Sub



True)

End SubEnd Module

TABLE OF CONTENTS

Page

1 INTRODUCTION 1















3 METHODOLOGY 34





Page









5 CONCLUSIONS 95

BIBLIOGRAPHY 98

APPENDICES 104




LIST OF FIGURES

Figure Page




















Figure Page






LIST OF TABLES

Table Page












DEDICATION


1 INTRODUCTION



















2






















3






the problems






functions










4






















DCU 1 DCU 2 DCU 3

5











6





















research

7











8


to the DCU


















9























10











11

2 LITERATURE REVIEW




operation






briefly discussed









12























13














Metric Metric





14

Metric Metric


















15






samples















16























17















18



Inductive Loop









measurements










Microwave Radar






stopped vehicles

19

(Continued)


Infrared

(Laser Radar)








Ultrasonic









Bluetooth



bull Non-intrusive




bull Spatial errors

20

(Continued)


Video Image

Processor




















21






















22
















(PBSampJ 2001)






23






















24






















25












2012)








26























27























28

















comparison






29








RELATIONSHIP














30












results










31









al 2008)




applications






regulation


32























33




running prediction







34

3 METHODOLOGY





















35






















36






reliability



Class I 300ft

Class II 33ft

Class III 3ft










37






















38






















39


















this study




40























41





1 119889














Equation 32




42






షరబమఱళ


















43











presented next











44



Grenander 1990)




















45

















equation





46








value





119909(119905 + 1) = 119909(119905) + 119907(119905 + 1)









47











ଶ









48













49









Appendix section B


MODEL








50



51














1 7 -4 22 -79 -86 -58

2 1 1 2 -52 -70 -66

3 8 11 21 -69 -69 -68

4 5 10 6 -61 -59 -70

5 4 2 11 -57 -73 -62

6 5 13 12 -68 -61 -71

7 7 -2 16 -72 -71 -62

8 1 -1 1 -60 -74 -63

52

9 9 19 23 -70 -59 -73

10 3 22 2 -81 -57 -86

11 8 7 23 -69 -73 -65

12 2 12 0 -60 -52 -72

13 4 3 9 -55 -73 -61

14 6 16 10 -64 -59 -70

15 6 24 13 -80 -62 -73

16 2 11 2 -63 -58 -78

17 3 18 0 -65 -44 -68

18 9 19 18 -77 -67 -72











53


0 20 40 60 80









50

0

-50

Error

(dis

tance) -100

-150

-200

-250-

-300-

Estimated Distance

Error

RSSI-



100

54


















55



Distance -4 22 235479 266011 128617 69664 169191 12086 1 2 105718 166782 166782 63365 633656 6876 11 21 227846 250745 113351 76476 178288 4614 10 6 98085 128617 13625 80814 753122 2454 2 11 174415 189681 90452 85775 157753 8128 13 12 174415 174415 128617 99999 132830 3262 -2 16 281277 296543 159149 83619 188778 10754 -1 1 159149 197314 143883 71398 109409 12848 19 23 204947 159149 182048 13624 119434 12293 22 2 189681 143883 182048 13391 106354 12193 7 23 250745 281277 143883 69750 169301 6069 12 0 113351 5992 220213 12670 036762 0765 3 9 143883 189681 113351 63983 118166 4413 16 10 182048 159149 120984 12067 149317 6307 24 13 235479 113351 189681 23794 16048 3054 11 2 113351 75186 151516 12333 693284 5109 18 0 159149 44654 21258 16590 468432 4891 19 18 243112 220213 159149 12275 170651 6788

Ave 6828 STD 3763









56






















57






















58























59

0

200

400

600

800

1000

-120

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


0

100

200

300

400

500

600

700

800

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10

0 0 5 10 15 20 25 30 35

RSSI

Distance


Sample Data

60

-100 0 100 200 300 400 500 600 700 800

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


Data














61






















62























63







64




C2BBD0 6262012 170306 C2BBD0 6262012 170310 C2BBD0 6262012 170315






65















group

66





Pei et al 2010)














DCU




67




















68












69















passes the DCU








70
















71




72










vehicle









73




74




75











76























77



the signal







78

















79


decrease

80














of accuracy







81


















and 64th Ave

82



DATA








83





times


RSSI DATA













84























85







Metho d




2 First Last (F+L)2

RSSI Method

1 128 135 131 125 124 004 011 007 001 2 225 148 206 149 149 036 001 017 000 3 212 212 212 203 203 009 009 009 000 4 249 136 212 139 135 114 001 037 004 5 151 301 226 251 253 102 008 027 002 6 238 134 206 137 133 105 001 033 004 7 141 259 220 229 229 048 030 009 000 8 258 245 251 239 240 018 005 011 001 9 158 256 227 247 246 048 010 019 001

10 341 202 252 156 154 147 008 058 002 11 151 143 147 142 140 011 003 007 002 12 142 140 141 134 134 008 006 007 000 13 246 233 239 241 240 006 007 001 001 14 202 140 151 138 136 026 004 015 002 15 203 203 203 156 153 010 010 010 003 16 234 229 231 226 225 009 004 006 001 17 144 148 146 139 138 006 010 008 001 18 246 248 247 243 242 004 006 005 001 19 155 205 200 153 154 001 011 006 001 20 258 304 301 303 303 005 001 002 000

AVG (sec) 2785 73 147 135 STDV (sec) 2988 64 1414 122

86










segments




Average RSSI Method


1 1955 (1312) 890 (597) 1145 (768) 265 (178) 2 1305 (876) 475 (319) 770 (517) 315 (211)


1 855 (629) 610 (449) 590 (434) 305 (224) 2 611 (449) 537 (395) 532 (391) 353 (259)


1 2785 (2193) 730 (575) 1470 (1157) 135 (106) 2 1568 (1235) 384 (303) 790 (622) 268 (211)


1 3310 (2348) 575 (408) 1600 (1135) 150 (106) 2 2358 (1672) 295 (209) 1216 (862) 295 (209)

Average

1 2226 (1620) 701 (507) 1201 (874) 214 (154) 2 1460 (1058) 423 (306) 827 (598) 308 (223)

All 1843 (1339) 562 (407) 1014 (736) 261 (188)

87






















poor accuracy

88






Volume



Samples















89











deployed


90


















passed the DCU unit

91

DCU 1 DCU 3 DCU 2

NW

Kin

g s B

lvd

Monroe Ave

Dutch Bros

Rogers Graff


X

X

150 ft 50 ft













92








DCUs (sec)



Address DCU1 ndash

DCU2

DCU2 ndash

DCU3

DCU1 ndash

DCU2

DCU2 ndash




93

D

23 BE6BEDC D 7 4 7 4 0 0

24 BE461DE4 25 3EECAF75 14 7 14 7 0 0


Max (seconds) 5 6


















94





95

5 CONCLUSIONS





















96


















platform





97



98

BIBLIOGRAPHY




























99









Philadelphia PA





PP 2834--2839




2007







on IEEE PP1--5




100



















297--303




227ndash238






101












program)





Proceedings




Vol9 No 2 pp 122--130




12 pp 101-127



102



UT-1105





WLAN





52(2)


Operational




74(4)26ndash29








September 2010)


ummpdf

103





2010-2014 2010 (accessed August 2010)




California Berkeley



PP 20--23








104

APPENDICES

105






















106























107






transmitting












likelihood

108




128 494

256 991

384 993

64 998

1024 999








mode remotely

109






















110











n=0for item in RSSI



n=n+1





111















112







print 8s8s (U[00] U[01])


113


VISUAL BASIC





End Structure


114


End Structure








End WhileEnd Using





115

NextEnd Using





NextEnd Using





NextEnd Using





116









End If





117


Test













End If



118


End If


Next











End If





119




















120


















121

End If










End If








122








End IfElse

counter += 1End If


Exit ForEnd If

NextNext

Next

End Sub



R2 R3 As Double





123




Next













124

2





R = (R1 + R2 + R3) 3


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

Next


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

2 _ + ((Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1) B1)) -





^ 2 _ + (Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1)) ^ 2


(Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1))) ^ 2 _

125

2



Next




B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

NextR = 1 - (SSE (18 3 - 2)) (SSTO (18 3 - 1))

Return Fit 3 18

End Function


End Sub


126


End Function



End Function





True)

End Sub



True)

End SubEnd Module


Page









5 CONCLUSIONS 95

BIBLIOGRAPHY 98

APPENDICES 104




LIST OF FIGURES

Figure Page




















Figure Page






LIST OF TABLES

Table Page












DEDICATION


1 INTRODUCTION



















2






















3






the problems






functions










4






















DCU 1 DCU 2 DCU 3

5











6





















research

7











8


to the DCU


















9























10











11

2 LITERATURE REVIEW




operation






briefly discussed









12























13














Metric Metric





14

Metric Metric


















15






samples















16























17















18



Inductive Loop









measurements










Microwave Radar






stopped vehicles

19

(Continued)


Infrared

(Laser Radar)








Ultrasonic









Bluetooth



bull Non-intrusive




bull Spatial errors

20

(Continued)


Video Image

Processor




















21






















22
















(PBSampJ 2001)






23






















24






















25












2012)








26























27























28

















comparison






29








RELATIONSHIP














30












results










31









al 2008)




applications






regulation


32























33




running prediction







34

3 METHODOLOGY





















35






















36






reliability



Class I 300ft

Class II 33ft

Class III 3ft










37






















38






















39


















this study




40























41





1 119889














Equation 32




42






షరబమఱళ


















43











presented next











44



Grenander 1990)




















45

















equation





46








value





119909(119905 + 1) = 119909(119905) + 119907(119905 + 1)









47











ଶ









48













49









Appendix section B


MODEL








50



51














1 7 -4 22 -79 -86 -58

2 1 1 2 -52 -70 -66

3 8 11 21 -69 -69 -68

4 5 10 6 -61 -59 -70

5 4 2 11 -57 -73 -62

6 5 13 12 -68 -61 -71

7 7 -2 16 -72 -71 -62

8 1 -1 1 -60 -74 -63

52

9 9 19 23 -70 -59 -73

10 3 22 2 -81 -57 -86

11 8 7 23 -69 -73 -65

12 2 12 0 -60 -52 -72

13 4 3 9 -55 -73 -61

14 6 16 10 -64 -59 -70

15 6 24 13 -80 -62 -73

16 2 11 2 -63 -58 -78

17 3 18 0 -65 -44 -68

18 9 19 18 -77 -67 -72











53


0 20 40 60 80









50

0

-50

Error

(dis

tance) -100

-150

-200

-250-

-300-

Estimated Distance

Error

RSSI-



100

54


















55



Distance -4 22 235479 266011 128617 69664 169191 12086 1 2 105718 166782 166782 63365 633656 6876 11 21 227846 250745 113351 76476 178288 4614 10 6 98085 128617 13625 80814 753122 2454 2 11 174415 189681 90452 85775 157753 8128 13 12 174415 174415 128617 99999 132830 3262 -2 16 281277 296543 159149 83619 188778 10754 -1 1 159149 197314 143883 71398 109409 12848 19 23 204947 159149 182048 13624 119434 12293 22 2 189681 143883 182048 13391 106354 12193 7 23 250745 281277 143883 69750 169301 6069 12 0 113351 5992 220213 12670 036762 0765 3 9 143883 189681 113351 63983 118166 4413 16 10 182048 159149 120984 12067 149317 6307 24 13 235479 113351 189681 23794 16048 3054 11 2 113351 75186 151516 12333 693284 5109 18 0 159149 44654 21258 16590 468432 4891 19 18 243112 220213 159149 12275 170651 6788

Ave 6828 STD 3763









56






















57






















58























59

0

200

400

600

800

1000

-120

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


0

100

200

300

400

500

600

700

800

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10

0 0 5 10 15 20 25 30 35

RSSI

Distance


Sample Data

60

-100 0 100 200 300 400 500 600 700 800

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


Data














61






















62























63







64




C2BBD0 6262012 170306 C2BBD0 6262012 170310 C2BBD0 6262012 170315






65















group

66





Pei et al 2010)














DCU




67




















68












69















passes the DCU








70
















71




72










vehicle









73




74




75











76























77



the signal







78

















79


decrease

80














of accuracy







81


















and 64th Ave

82



DATA








83





times


RSSI DATA













84























85







Metho d




2 First Last (F+L)2

RSSI Method

1 128 135 131 125 124 004 011 007 001 2 225 148 206 149 149 036 001 017 000 3 212 212 212 203 203 009 009 009 000 4 249 136 212 139 135 114 001 037 004 5 151 301 226 251 253 102 008 027 002 6 238 134 206 137 133 105 001 033 004 7 141 259 220 229 229 048 030 009 000 8 258 245 251 239 240 018 005 011 001 9 158 256 227 247 246 048 010 019 001

10 341 202 252 156 154 147 008 058 002 11 151 143 147 142 140 011 003 007 002 12 142 140 141 134 134 008 006 007 000 13 246 233 239 241 240 006 007 001 001 14 202 140 151 138 136 026 004 015 002 15 203 203 203 156 153 010 010 010 003 16 234 229 231 226 225 009 004 006 001 17 144 148 146 139 138 006 010 008 001 18 246 248 247 243 242 004 006 005 001 19 155 205 200 153 154 001 011 006 001 20 258 304 301 303 303 005 001 002 000

AVG (sec) 2785 73 147 135 STDV (sec) 2988 64 1414 122

86










segments




Average RSSI Method


1 1955 (1312) 890 (597) 1145 (768) 265 (178) 2 1305 (876) 475 (319) 770 (517) 315 (211)


1 855 (629) 610 (449) 590 (434) 305 (224) 2 611 (449) 537 (395) 532 (391) 353 (259)


1 2785 (2193) 730 (575) 1470 (1157) 135 (106) 2 1568 (1235) 384 (303) 790 (622) 268 (211)


1 3310 (2348) 575 (408) 1600 (1135) 150 (106) 2 2358 (1672) 295 (209) 1216 (862) 295 (209)

Average

1 2226 (1620) 701 (507) 1201 (874) 214 (154) 2 1460 (1058) 423 (306) 827 (598) 308 (223)

All 1843 (1339) 562 (407) 1014 (736) 261 (188)

87






















poor accuracy

88






Volume



Samples















89











deployed


90


















passed the DCU unit

91

DCU 1 DCU 3 DCU 2

NW

Kin

g s B

lvd

Monroe Ave

Dutch Bros

Rogers Graff


X

X

150 ft 50 ft













92








DCUs (sec)



Address DCU1 ndash

DCU2

DCU2 ndash

DCU3

DCU1 ndash

DCU2

DCU2 ndash




93

D

23 BE6BEDC D 7 4 7 4 0 0

24 BE461DE4 25 3EECAF75 14 7 14 7 0 0


Max (seconds) 5 6


















94





95

5 CONCLUSIONS





















96


















platform





97



98

BIBLIOGRAPHY




























99









Philadelphia PA





PP 2834--2839




2007







on IEEE PP1--5




100



















297--303




227ndash238






101












program)





Proceedings




Vol9 No 2 pp 122--130




12 pp 101-127



102



UT-1105





WLAN





52(2)


Operational




74(4)26ndash29








September 2010)


ummpdf

103





2010-2014 2010 (accessed August 2010)




California Berkeley



PP 20--23








104

APPENDICES

105






















106























107






transmitting












likelihood

108




128 494

256 991

384 993

64 998

1024 999








mode remotely

109






















110











n=0for item in RSSI



n=n+1





111















112







print 8s8s (U[00] U[01])


113


VISUAL BASIC





End Structure


114


End Structure








End WhileEnd Using





115

NextEnd Using





NextEnd Using





NextEnd Using





116









End If





117


Test













End If



118


End If


Next











End If





119




















120


















121

End If










End If








122








End IfElse

counter += 1End If


Exit ForEnd If

NextNext

Next

End Sub



R2 R3 As Double





123




Next













124

2





R = (R1 + R2 + R3) 3


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

Next


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

2 _ + ((Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1) B1)) -





^ 2 _ + (Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1)) ^ 2


(Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1))) ^ 2 _

125

2



Next




B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

NextR = 1 - (SSE (18 3 - 2)) (SSTO (18 3 - 1))

Return Fit 3 18

End Function


End Sub


126


End Function



End Function





True)

End Sub



True)

End SubEnd Module

LIST OF FIGURES

Figure Page




















Figure Page






LIST OF TABLES

Table Page












DEDICATION


1 INTRODUCTION



















2






















3






the problems






functions










4






















DCU 1 DCU 2 DCU 3

5











6





















research

7











8


to the DCU


















9























10











11

2 LITERATURE REVIEW




operation






briefly discussed









12























13














Metric Metric





14

Metric Metric


















15






samples















16























17















18



Inductive Loop









measurements










Microwave Radar






stopped vehicles

19

(Continued)


Infrared

(Laser Radar)








Ultrasonic









Bluetooth



bull Non-intrusive




bull Spatial errors

20

(Continued)


Video Image

Processor




















21






















22
















(PBSampJ 2001)






23






















24






















25












2012)








26























27























28

















comparison






29








RELATIONSHIP














30












results










31









al 2008)




applications






regulation


32























33




running prediction







34

3 METHODOLOGY





















35






















36






reliability



Class I 300ft

Class II 33ft

Class III 3ft










37






















38






















39


















this study




40























41





1 119889














Equation 32




42






షరబమఱళ


















43











presented next











44



Grenander 1990)




















45

















equation





46








value





119909(119905 + 1) = 119909(119905) + 119907(119905 + 1)









47











ଶ









48













49









Appendix section B


MODEL








50



51














1 7 -4 22 -79 -86 -58

2 1 1 2 -52 -70 -66

3 8 11 21 -69 -69 -68

4 5 10 6 -61 -59 -70

5 4 2 11 -57 -73 -62

6 5 13 12 -68 -61 -71

7 7 -2 16 -72 -71 -62

8 1 -1 1 -60 -74 -63

52

9 9 19 23 -70 -59 -73

10 3 22 2 -81 -57 -86

11 8 7 23 -69 -73 -65

12 2 12 0 -60 -52 -72

13 4 3 9 -55 -73 -61

14 6 16 10 -64 -59 -70

15 6 24 13 -80 -62 -73

16 2 11 2 -63 -58 -78

17 3 18 0 -65 -44 -68

18 9 19 18 -77 -67 -72











53


0 20 40 60 80









50

0

-50

Error

(dis

tance) -100

-150

-200

-250-

-300-

Estimated Distance

Error

RSSI-



100

54


















55



Distance -4 22 235479 266011 128617 69664 169191 12086 1 2 105718 166782 166782 63365 633656 6876 11 21 227846 250745 113351 76476 178288 4614 10 6 98085 128617 13625 80814 753122 2454 2 11 174415 189681 90452 85775 157753 8128 13 12 174415 174415 128617 99999 132830 3262 -2 16 281277 296543 159149 83619 188778 10754 -1 1 159149 197314 143883 71398 109409 12848 19 23 204947 159149 182048 13624 119434 12293 22 2 189681 143883 182048 13391 106354 12193 7 23 250745 281277 143883 69750 169301 6069 12 0 113351 5992 220213 12670 036762 0765 3 9 143883 189681 113351 63983 118166 4413 16 10 182048 159149 120984 12067 149317 6307 24 13 235479 113351 189681 23794 16048 3054 11 2 113351 75186 151516 12333 693284 5109 18 0 159149 44654 21258 16590 468432 4891 19 18 243112 220213 159149 12275 170651 6788

Ave 6828 STD 3763









56






















57






















58























59

0

200

400

600

800

1000

-120

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


0

100

200

300

400

500

600

700

800

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10

0 0 5 10 15 20 25 30 35

RSSI

Distance


Sample Data

60

-100 0 100 200 300 400 500 600 700 800

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


Data














61






















62























63







64




C2BBD0 6262012 170306 C2BBD0 6262012 170310 C2BBD0 6262012 170315






65















group

66





Pei et al 2010)














DCU




67




















68












69















passes the DCU








70
















71




72










vehicle









73




74




75











76























77



the signal







78

















79


decrease

80














of accuracy







81


















and 64th Ave

82



DATA








83





times


RSSI DATA













84























85







Metho d




2 First Last (F+L)2

RSSI Method

1 128 135 131 125 124 004 011 007 001 2 225 148 206 149 149 036 001 017 000 3 212 212 212 203 203 009 009 009 000 4 249 136 212 139 135 114 001 037 004 5 151 301 226 251 253 102 008 027 002 6 238 134 206 137 133 105 001 033 004 7 141 259 220 229 229 048 030 009 000 8 258 245 251 239 240 018 005 011 001 9 158 256 227 247 246 048 010 019 001

10 341 202 252 156 154 147 008 058 002 11 151 143 147 142 140 011 003 007 002 12 142 140 141 134 134 008 006 007 000 13 246 233 239 241 240 006 007 001 001 14 202 140 151 138 136 026 004 015 002 15 203 203 203 156 153 010 010 010 003 16 234 229 231 226 225 009 004 006 001 17 144 148 146 139 138 006 010 008 001 18 246 248 247 243 242 004 006 005 001 19 155 205 200 153 154 001 011 006 001 20 258 304 301 303 303 005 001 002 000

AVG (sec) 2785 73 147 135 STDV (sec) 2988 64 1414 122

86










segments




Average RSSI Method


1 1955 (1312) 890 (597) 1145 (768) 265 (178) 2 1305 (876) 475 (319) 770 (517) 315 (211)


1 855 (629) 610 (449) 590 (434) 305 (224) 2 611 (449) 537 (395) 532 (391) 353 (259)


1 2785 (2193) 730 (575) 1470 (1157) 135 (106) 2 1568 (1235) 384 (303) 790 (622) 268 (211)


1 3310 (2348) 575 (408) 1600 (1135) 150 (106) 2 2358 (1672) 295 (209) 1216 (862) 295 (209)

Average

1 2226 (1620) 701 (507) 1201 (874) 214 (154) 2 1460 (1058) 423 (306) 827 (598) 308 (223)

All 1843 (1339) 562 (407) 1014 (736) 261 (188)

87






















poor accuracy

88






Volume



Samples















89











deployed


90


















passed the DCU unit

91

DCU 1 DCU 3 DCU 2

NW

Kin

g s B

lvd

Monroe Ave

Dutch Bros

Rogers Graff


X

X

150 ft 50 ft













92








DCUs (sec)



Address DCU1 ndash

DCU2

DCU2 ndash

DCU3

DCU1 ndash

DCU2

DCU2 ndash




93

D

23 BE6BEDC D 7 4 7 4 0 0

24 BE461DE4 25 3EECAF75 14 7 14 7 0 0


Max (seconds) 5 6


















94





95

5 CONCLUSIONS





















96


















platform





97



98

BIBLIOGRAPHY




























99









Philadelphia PA





PP 2834--2839




2007







on IEEE PP1--5




100



















297--303




227ndash238






101












program)





Proceedings




Vol9 No 2 pp 122--130




12 pp 101-127



102



UT-1105





WLAN





52(2)


Operational




74(4)26ndash29








September 2010)


ummpdf

103





2010-2014 2010 (accessed August 2010)




California Berkeley



PP 20--23








104

APPENDICES

105






















106























107






transmitting












likelihood

108




128 494

256 991

384 993

64 998

1024 999








mode remotely

109






















110











n=0for item in RSSI



n=n+1





111















112







print 8s8s (U[00] U[01])


113


VISUAL BASIC





End Structure


114


End Structure








End WhileEnd Using





115

NextEnd Using





NextEnd Using





NextEnd Using





116









End If





117


Test













End If



118


End If


Next











End If





119




















120


















121

End If










End If








122








End IfElse

counter += 1End If


Exit ForEnd If

NextNext

Next

End Sub



R2 R3 As Double





123




Next













124

2





R = (R1 + R2 + R3) 3


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

Next


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

2 _ + ((Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1) B1)) -





^ 2 _ + (Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1)) ^ 2


(Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1))) ^ 2 _

125

2



Next




B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

NextR = 1 - (SSE (18 3 - 2)) (SSTO (18 3 - 1))

Return Fit 3 18

End Function


End Sub


126


End Function



End Function





True)

End Sub



True)

End SubEnd Module


Figure Page






LIST OF TABLES

Table Page












DEDICATION


1 INTRODUCTION



















2






















3






the problems






functions










4






















DCU 1 DCU 2 DCU 3

5











6





















research

7











8


to the DCU


















9























10











11

2 LITERATURE REVIEW




operation






briefly discussed









12























13














Metric Metric





14

Metric Metric


















15






samples















16























17















18



Inductive Loop









measurements










Microwave Radar






stopped vehicles

19

(Continued)


Infrared

(Laser Radar)








Ultrasonic









Bluetooth



bull Non-intrusive




bull Spatial errors

20

(Continued)


Video Image

Processor




















21






















22
















(PBSampJ 2001)






23






















24






















25












2012)








26























27























28

















comparison






29








RELATIONSHIP














30












results










31









al 2008)




applications






regulation


32























33




running prediction







34

3 METHODOLOGY





















35






















36






reliability



Class I 300ft

Class II 33ft

Class III 3ft










37






















38






















39


















this study




40























41





1 119889














Equation 32




42






షరబమఱళ


















43











presented next











44



Grenander 1990)




















45

















equation





46








value





119909(119905 + 1) = 119909(119905) + 119907(119905 + 1)









47











ଶ









48













49









Appendix section B


MODEL








50



51














1 7 -4 22 -79 -86 -58

2 1 1 2 -52 -70 -66

3 8 11 21 -69 -69 -68

4 5 10 6 -61 -59 -70

5 4 2 11 -57 -73 -62

6 5 13 12 -68 -61 -71

7 7 -2 16 -72 -71 -62

8 1 -1 1 -60 -74 -63

52

9 9 19 23 -70 -59 -73

10 3 22 2 -81 -57 -86

11 8 7 23 -69 -73 -65

12 2 12 0 -60 -52 -72

13 4 3 9 -55 -73 -61

14 6 16 10 -64 -59 -70

15 6 24 13 -80 -62 -73

16 2 11 2 -63 -58 -78

17 3 18 0 -65 -44 -68

18 9 19 18 -77 -67 -72











53


0 20 40 60 80









50

0

-50

Error

(dis

tance) -100

-150

-200

-250-

-300-

Estimated Distance

Error

RSSI-



100

54


















55



Distance -4 22 235479 266011 128617 69664 169191 12086 1 2 105718 166782 166782 63365 633656 6876 11 21 227846 250745 113351 76476 178288 4614 10 6 98085 128617 13625 80814 753122 2454 2 11 174415 189681 90452 85775 157753 8128 13 12 174415 174415 128617 99999 132830 3262 -2 16 281277 296543 159149 83619 188778 10754 -1 1 159149 197314 143883 71398 109409 12848 19 23 204947 159149 182048 13624 119434 12293 22 2 189681 143883 182048 13391 106354 12193 7 23 250745 281277 143883 69750 169301 6069 12 0 113351 5992 220213 12670 036762 0765 3 9 143883 189681 113351 63983 118166 4413 16 10 182048 159149 120984 12067 149317 6307 24 13 235479 113351 189681 23794 16048 3054 11 2 113351 75186 151516 12333 693284 5109 18 0 159149 44654 21258 16590 468432 4891 19 18 243112 220213 159149 12275 170651 6788

Ave 6828 STD 3763









56






















57






















58























59

0

200

400

600

800

1000

-120

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


0

100

200

300

400

500

600

700

800

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10

0 0 5 10 15 20 25 30 35

RSSI

Distance


Sample Data

60

-100 0 100 200 300 400 500 600 700 800

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


Data














61






















62























63







64




C2BBD0 6262012 170306 C2BBD0 6262012 170310 C2BBD0 6262012 170315






65















group

66





Pei et al 2010)














DCU




67




















68












69















passes the DCU








70
















71




72










vehicle









73




74




75











76























77



the signal







78

















79


decrease

80














of accuracy







81


















and 64th Ave

82



DATA








83





times


RSSI DATA













84























85







Metho d




2 First Last (F+L)2

RSSI Method

1 128 135 131 125 124 004 011 007 001 2 225 148 206 149 149 036 001 017 000 3 212 212 212 203 203 009 009 009 000 4 249 136 212 139 135 114 001 037 004 5 151 301 226 251 253 102 008 027 002 6 238 134 206 137 133 105 001 033 004 7 141 259 220 229 229 048 030 009 000 8 258 245 251 239 240 018 005 011 001 9 158 256 227 247 246 048 010 019 001

10 341 202 252 156 154 147 008 058 002 11 151 143 147 142 140 011 003 007 002 12 142 140 141 134 134 008 006 007 000 13 246 233 239 241 240 006 007 001 001 14 202 140 151 138 136 026 004 015 002 15 203 203 203 156 153 010 010 010 003 16 234 229 231 226 225 009 004 006 001 17 144 148 146 139 138 006 010 008 001 18 246 248 247 243 242 004 006 005 001 19 155 205 200 153 154 001 011 006 001 20 258 304 301 303 303 005 001 002 000

AVG (sec) 2785 73 147 135 STDV (sec) 2988 64 1414 122

86










segments




Average RSSI Method


1 1955 (1312) 890 (597) 1145 (768) 265 (178) 2 1305 (876) 475 (319) 770 (517) 315 (211)


1 855 (629) 610 (449) 590 (434) 305 (224) 2 611 (449) 537 (395) 532 (391) 353 (259)


1 2785 (2193) 730 (575) 1470 (1157) 135 (106) 2 1568 (1235) 384 (303) 790 (622) 268 (211)


1 3310 (2348) 575 (408) 1600 (1135) 150 (106) 2 2358 (1672) 295 (209) 1216 (862) 295 (209)

Average

1 2226 (1620) 701 (507) 1201 (874) 214 (154) 2 1460 (1058) 423 (306) 827 (598) 308 (223)

All 1843 (1339) 562 (407) 1014 (736) 261 (188)

87






















poor accuracy

88






Volume



Samples















89











deployed


90


















passed the DCU unit

91

DCU 1 DCU 3 DCU 2

NW

Kin

g s B

lvd

Monroe Ave

Dutch Bros

Rogers Graff


X

X

150 ft 50 ft













92








DCUs (sec)



Address DCU1 ndash

DCU2

DCU2 ndash

DCU3

DCU1 ndash

DCU2

DCU2 ndash




93

D

23 BE6BEDC D 7 4 7 4 0 0

24 BE461DE4 25 3EECAF75 14 7 14 7 0 0


Max (seconds) 5 6


















94





95

5 CONCLUSIONS





















96


















platform





97



98

BIBLIOGRAPHY




























99









Philadelphia PA





PP 2834--2839




2007







on IEEE PP1--5




100



















297--303




227ndash238






101












program)





Proceedings




Vol9 No 2 pp 122--130




12 pp 101-127



102



UT-1105





WLAN





52(2)


Operational




74(4)26ndash29








September 2010)


ummpdf

103





2010-2014 2010 (accessed August 2010)




California Berkeley



PP 20--23








104

APPENDICES

105






















106























107






transmitting












likelihood

108




128 494

256 991

384 993

64 998

1024 999








mode remotely

109






















110











n=0for item in RSSI



n=n+1





111















112







print 8s8s (U[00] U[01])


113


VISUAL BASIC





End Structure


114


End Structure








End WhileEnd Using





115

NextEnd Using





NextEnd Using





NextEnd Using





116









End If





117


Test













End If



118


End If


Next











End If





119




















120


















121

End If










End If








122








End IfElse

counter += 1End If


Exit ForEnd If

NextNext

Next

End Sub



R2 R3 As Double





123




Next













124

2





R = (R1 + R2 + R3) 3


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

Next


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

2 _ + ((Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1) B1)) -





^ 2 _ + (Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1)) ^ 2


(Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1))) ^ 2 _

125

2



Next




B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

NextR = 1 - (SSE (18 3 - 2)) (SSTO (18 3 - 1))

Return Fit 3 18

End Function


End Sub


126


End Function



End Function





True)

End Sub



True)

End SubEnd Module

LIST OF TABLES

Table Page












DEDICATION


1 INTRODUCTION



















2






















3






the problems






functions










4






















DCU 1 DCU 2 DCU 3

5











6





















research

7











8


to the DCU


















9























10











11

2 LITERATURE REVIEW




operation






briefly discussed









12























13














Metric Metric





14

Metric Metric


















15






samples















16























17















18



Inductive Loop









measurements










Microwave Radar






stopped vehicles

19

(Continued)


Infrared

(Laser Radar)








Ultrasonic









Bluetooth



bull Non-intrusive




bull Spatial errors

20

(Continued)


Video Image

Processor




















21






















22
















(PBSampJ 2001)






23






















24






















25












2012)








26























27























28

















comparison






29








RELATIONSHIP














30












results










31









al 2008)




applications






regulation


32























33




running prediction







34

3 METHODOLOGY





















35






















36






reliability



Class I 300ft

Class II 33ft

Class III 3ft










37






















38






















39


















this study




40























41





1 119889














Equation 32




42






షరబమఱళ


















43











presented next











44



Grenander 1990)




















45

















equation





46








value





119909(119905 + 1) = 119909(119905) + 119907(119905 + 1)









47











ଶ









48













49









Appendix section B


MODEL








50



51














1 7 -4 22 -79 -86 -58

2 1 1 2 -52 -70 -66

3 8 11 21 -69 -69 -68

4 5 10 6 -61 -59 -70

5 4 2 11 -57 -73 -62

6 5 13 12 -68 -61 -71

7 7 -2 16 -72 -71 -62

8 1 -1 1 -60 -74 -63

52

9 9 19 23 -70 -59 -73

10 3 22 2 -81 -57 -86

11 8 7 23 -69 -73 -65

12 2 12 0 -60 -52 -72

13 4 3 9 -55 -73 -61

14 6 16 10 -64 -59 -70

15 6 24 13 -80 -62 -73

16 2 11 2 -63 -58 -78

17 3 18 0 -65 -44 -68

18 9 19 18 -77 -67 -72











53


0 20 40 60 80









50

0

-50

Error

(dis

tance) -100

-150

-200

-250-

-300-

Estimated Distance

Error

RSSI-



100

54


















55



Distance -4 22 235479 266011 128617 69664 169191 12086 1 2 105718 166782 166782 63365 633656 6876 11 21 227846 250745 113351 76476 178288 4614 10 6 98085 128617 13625 80814 753122 2454 2 11 174415 189681 90452 85775 157753 8128 13 12 174415 174415 128617 99999 132830 3262 -2 16 281277 296543 159149 83619 188778 10754 -1 1 159149 197314 143883 71398 109409 12848 19 23 204947 159149 182048 13624 119434 12293 22 2 189681 143883 182048 13391 106354 12193 7 23 250745 281277 143883 69750 169301 6069 12 0 113351 5992 220213 12670 036762 0765 3 9 143883 189681 113351 63983 118166 4413 16 10 182048 159149 120984 12067 149317 6307 24 13 235479 113351 189681 23794 16048 3054 11 2 113351 75186 151516 12333 693284 5109 18 0 159149 44654 21258 16590 468432 4891 19 18 243112 220213 159149 12275 170651 6788

Ave 6828 STD 3763









56






















57






















58























59

0

200

400

600

800

1000

-120

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


0

100

200

300

400

500

600

700

800

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10

0 0 5 10 15 20 25 30 35

RSSI

Distance


Sample Data

60

-100 0 100 200 300 400 500 600 700 800

-100

-80

-60

-40

-20

0 0 5 10 15 20 25 30 35

RSSI

Distance


Data














61






















62























63







64




C2BBD0 6262012 170306 C2BBD0 6262012 170310 C2BBD0 6262012 170315






65















group

66





Pei et al 2010)














DCU




67




















68












69















passes the DCU








70
















71




72










vehicle









73




74




75











76























77



the signal







78

















79


decrease

80














of accuracy







81


















and 64th Ave

82



DATA








83





times


RSSI DATA













84























85







Metho d




2 First Last (F+L)2

RSSI Method

1 128 135 131 125 124 004 011 007 001 2 225 148 206 149 149 036 001 017 000 3 212 212 212 203 203 009 009 009 000 4 249 136 212 139 135 114 001 037 004 5 151 301 226 251 253 102 008 027 002 6 238 134 206 137 133 105 001 033 004 7 141 259 220 229 229 048 030 009 000 8 258 245 251 239 240 018 005 011 001 9 158 256 227 247 246 048 010 019 001

10 341 202 252 156 154 147 008 058 002 11 151 143 147 142 140 011 003 007 002 12 142 140 141 134 134 008 006 007 000 13 246 233 239 241 240 006 007 001 001 14 202 140 151 138 136 026 004 015 002 15 203 203 203 156 153 010 010 010 003 16 234 229 231 226 225 009 004 006 001 17 144 148 146 139 138 006 010 008 001 18 246 248 247 243 242 004 006 005 001 19 155 205 200 153 154 001 011 006 001 20 258 304 301 303 303 005 001 002 000

AVG (sec) 2785 73 147 135 STDV (sec) 2988 64 1414 122

86










segments




Average RSSI Method


1 1955 (1312) 890 (597) 1145 (768) 265 (178) 2 1305 (876) 475 (319) 770 (517) 315 (211)


1 855 (629) 610 (449) 590 (434) 305 (224) 2 611 (449) 537 (395) 532 (391) 353 (259)


1 2785 (2193) 730 (575) 1470 (1157) 135 (106) 2 1568 (1235) 384 (303) 790 (622) 268 (211)


1 3310 (2348) 575 (408) 1600 (1135) 150 (106) 2 2358 (1672) 295 (209) 1216 (862) 295 (209)

Average

1 2226 (1620) 701 (507) 1201 (874) 214 (154) 2 1460 (1058) 423 (306) 827 (598) 308 (223)

All 1843 (1339) 562 (407) 1014 (736) 261 (188)

87






















poor accuracy

88






Volume



Samples















89











deployed


90


















passed the DCU unit

91

DCU 1 DCU 3 DCU 2

NW

Kin

g s B

lvd

Monroe Ave

Dutch Bros

Rogers Graff


X

X

150 ft 50 ft













92








DCUs (sec)



Address DCU1 ndash

DCU2

DCU2 ndash

DCU3

DCU1 ndash

DCU2

DCU2 ndash




93

D

23 BE6BEDC D 7 4 7 4 0 0

24 BE461DE4 25 3EECAF75 14 7 14 7 0 0


Max (seconds) 5 6


















94





95

5 CONCLUSIONS





















96


















platform





97



98

BIBLIOGRAPHY




























99









Philadelphia PA





PP 2834--2839




2007







on IEEE PP1--5




100



















297--303




227ndash238






101












program)





Proceedings




Vol9 No 2 pp 122--130




12 pp 101-127



102



UT-1105





WLAN





52(2)


Operational




74(4)26ndash29








September 2010)


ummpdf

103





2010-2014 2010 (accessed August 2010)




California Berkeley



PP 20--23








104

APPENDICES

105






















106























107






transmitting












likelihood

108




128 494

256 991

384 993

64 998

1024 999








mode remotely

109






















110











n=0for item in RSSI



n=n+1





111















112







print 8s8s (U[00] U[01])


113


VISUAL BASIC





End Structure


114


End Structure








End WhileEnd Using





115

NextEnd Using





NextEnd Using





NextEnd Using





116









End If





117


Test













End If



118


End If


Next











End If





119




















120


















121

End If










End If








122








End IfElse

counter += 1End If


Exit ForEnd If

NextNext

Next

End Sub



R2 R3 As Double





123




Next













124

2





R = (R1 + R2 + R3) 3


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

Next


B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

2 _ + ((Data(i 1 0) - 10 ^ ((Data(i 0 0) - A1) B1)) -





^ 2 _ + (Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1)) ^ 2


(Data(i 1 1) - B1 10 ^ (Data(i 0 1) - A1))) ^ 2 _

125

2



Next




B1)) ^ 2 _ + (Data(i 1 1) - 10 ^ ((Data(i 0 1) - A1) B1)) ^

2 _ + (Data(i 1 2) - 10 ^ ((Data(i 0 2) - A1) B1)) ^

NextR = 1 - (SSE (18 3 - 2)) (SSTO (18 3 - 1))

Return Fit 3 18

End Function


End Sub


126


End Function



End Function





True)

End Sub



True)

End SubEnd Module

Utilizing Wireless-based Data Collection Units for Automated Vehicle Movement Data Collection

Documents

Transcript of Utilizing Wireless-based Data Collection Units for Automated Vehicle Movement Data Collection