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Development of VISSIM Base Model for Northern
Virginia (NOVA) Freeway System
By:
Srividya Santhanam
Byungkyu (Brian) Park
Research Report No. UVACTS-13-0-124
June 2008
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A Research Project Report
For the Northern Region Operations
Virginia Department of Transportation (VDOT)
Srividya Santhanam
Department of Civil Engineering`
Email: [email protected]
Dr. Byungkyu (Brian) Park
Department of Civil Engineering
Email: [email protected]
Center for Transportation Studiesat the University of Virginia produces outstanding transportationprofessionals, innovative research results and provides important public service. The Center for
Transportation Studies is committed to academic excellence, multi-disciplinary research and to developing
state-of-the-art facilities. Through a partnership with the Virginia Department of Transportations (VDOT)Research Council (VTRC), CTS faculty hold joint appointments, VTRC research scientists teach
specialized courses, and graduate student work is supported through a Graduate Research Assistantship
Program. CTS receives substantial financial support from two federal University Transportation Center
Grants: the Mid-Atlantic Universities Transportation Center (MAUTC), and through the National ITS
Implementation ResearchCenter (ITS Center). Other related research activities of the faculty includefunding through FHWA, NSF, US Department of Transportation, VDOT, other governmental agencies and
private companies.
Disclaimer:The contents of this report reflect the views of the authors, who are responsible for the factsand the accuracy of the information presented herein. This document is disseminated under the
sponsorship of the Department of Transportation, University Transportation Centers Program, intheinterest of information exchange. The U.S. Government assumes no liability for the contents or use
thereof.
CTS Website Center for Transportation Studieshttp://cts.virginia.edu University of Virginia
351 McCormick Road, P.O. Box 400742
Charlottesville, VA 22904-4742434.924.6362
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1. Report No. UVACTS-13-0-124 2. Government Accession No. 3. Recipients Catalog No.
4. Title and Subtitle 5. Report Date
Development of VISSIM Model for Northern Virginia (NOVA) Freeway System June 2008
6. Performing Organization Code
7. Author(s) 8. Performing Organization Report No.
Srividya Santhanam and Byungkyu (Brian) Park UVACTS-13-0-124
9. Performing Organization and Address 10. Work Unit No. (TRAIS)
Center for Transportation Studies
University of Virginia 11. Contract or Grant No.
PO Box 400742
Charlottesville, VA 22904-7472
12. Sponsoring Agencies' Name and Address 13. Type of Report and Period Covered
Northern Region Operations
Virginia Department of Transportation
Final Report
14. Sponsoring Agency Code
15. Supplementary Notes
16. Abstract
This project aims at providing the Northern Region Operations (NRO) staff with a microscopic traffic simulation model for
major Northern Virginia (NOVA) Freeway system using VISSIM. The network includes 4 Interstate Highways (I-66, I-95, I-395, and
I-495) and a State Highway 267. This report provides details on the tasks of network building, O-D estimation and model calibration.
The O-D matrices were estimated on the basis of traffic counts obtained from video cameras, sensors, and average annual daily traffic
using QUEENSOD method. Latin Hypercube experimental design approach was used for the calibration. The project deliverables to
the Northern Region Operations include calibrated network for the NOVA freeway system in VISSIM program along with O-D tables
and base measures of effectiveness.
17 Key Words 18. Distribution Statement
Microscopic traffic simulation model, O-D Estimation, Calibration, LatinHypercube experimental design, Measures of Effectiveness (MOE)
No restrictions. This document is available to thepublic.
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Abstract
This project aims at providing the Northern Region Operations (NRO) staff with a
microscopic traffic simulation model for major Northern Virginia (NOVA) Freeway
system using VISSIM. The network includes 4 Interstate Highways (I-66, I-95, I-395,
and I-495) and a State Highway 267. This report provides details on the tasks of network
building, O-D estimation and model calibration. The O-D matrices were estimated on the
basis of traffic counts obtained from video cameras, sensors, and average annual daily
traffic using QUEENSOD method. Latin Hypercube experimental design approach was
used for the calibration. The project deliverables to the Northern Region Operations
include calibrated network for the NOVA freeway system in VISSIM program along with
O-D tables and base measures of effectiveness.
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Table of Contents
Abstract.............................................................................................................................. ivTable of Contents................................................................................................................ v
List of Figures .................................................................................................................... vi
List of Tables .................................................................................................................... vii
Introduction......................................................................................................................... 1
Purpose and Scope.......................................................................................................... 2
Organization of the Report.............................................................................................. 2
Chapter 1: Network Coding................................................................................................ 3
1.1 Parking Lots and Nodes............................................................................................ 31.2 Vehicle Types and Traffic Composition................................................................... 41.3 HOV Lanes and Link/Connector Costs .................................................................... 5
1.4 Toll Roads and Stop Signs........................................................................................ 8
1.5 Driving Behavior Parameters Based on Link Type .................................................. 9
Chapter 2: O-D Estimation ............................................................................................... 11
2.1 Data Collection ....................................................................................................... 11
2.1.1 Data from Detectors......................................................................................... 11
2.1.3 Extrapolation of Missing Data ......................................................................... 21
2.2 O-D Estimation Using QUEENSOD...................................................................... 242.3 Measures of Effectiveness Data Collection......................................................... 27
Chapter 3: Simulation Model Calibration......................................................................... 28
3.1 Introduction............................................................................................................. 283.2 Latin Hypercube Sampling ..................................................................................... 29
3.3 Experimental Design Results.................................................................................. 30
3.3.1 Simulation Model Setup .................................................................................. 313.3.2 Initial Evaluation.............................................................................................. 31
3.3.3 Initial Calibration Latin Hypercube Design (LHD) for Driving Behavior... 363.3.3.1 Experimental Design Latin Hypercube Sampling ................................. 38
3.3.3.2 Multiple Runs............................................................................................ 38
3.3.3.3 Parameter Set Selection ............................................................................ 38
Chapter 5: Measures of Effectiveness from the Calibrated Model................................... 47
5.1 Counts and Speeds.................................................................................................. 47
5.2 Travel Time and Delays.......................................................................................... 485.3 Density .................................................................................................................... 49
Conclusions and Recommendations ................................................................................. 51References......................................................................................................................... 52
Appendix A....................................................................................................................... 53
Appendix B ....................................................................................................................... 81
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List of Figures
Figure 2.Northern Virginia (NOVA) Freeway network (source: Google map) ................. 1
Figure 3. I-66 EB Mainline Flow Profiles ........................................................................ 21
Figure 4. I-66 EB On Ramp Flow Profiles ....................................................................... 22
Figure 5. I-66 EB Off Ramp Flow Profiles....................................................................... 22Figure 6. Flow Chart for Calibration Using Latin Hypercube Experimental Design....... 28
Figure 7.Two-dimensional representation of a LHS of size 5 for X1 and X2 ................... 30
Figure 8. I-66EB Network ................................................................................................ 31
Figure 9. Simulated TT Vs Observed TT (6:00-6:30 AM)-Default Parameters............... 32Figure 10. Simulated TT Vs Observed TT (6:30-7:00 AM)-Default Parameters............. 33
Figure 11. Simulated TT Vs Observed TT (7:30-8:00 AM)-Default Parameters............. 33
Figure 12. Simulated Counts Vs Detector Counts for Station 2-Default Parameters....... 34
Figure 13. Simulated Speed Vs Detector Speed for Station 2-Default Parameters.......... 34
Figure 14. Simulated Counts Vs Detector Counts for Station 10-Default Parameters..... 35Figure 15. Simulated Speed Vs Detector Speed for Station 10-Default Parameters........ 35
Figure 16.Simulated TT Vs Sample Case......................................................................... 39
Figure 17. Simulated TT Vs CC1 ..................................................................................... 40
Figure 18. Simulated TT Vs Observed TT (6:00-6:30 AM)-C98 (LHD_1)..................... 41
Figure 19. Simulated TT Vs Observed TT (6:30-7:00 AM)-C98 (LHD_1)..................... 42
Figure 20. Simulated TT Vs Observed TT (7:30-8:00 AM)-C98 (LHD_1)..................... 42Figure 21. Simulated Counts Vs Detector Counts for Station 2 C98 (LHD_1) ............ 43
Figure 22. Simulated Speed Vs Detector Speed for Station 2 C98 (LHD_1)................ 43Figure 23. Simulated Counts Vs Detector Counts for Station 10 C98 (LHD_1) .......... 44Figure 24. Simulated Speed Vs Detector Speed for Station 10 C98 (LHD_1).............. 44
Figure 1.A print screen of Step 1 in usage of Lane Closure.exe ...................................... 81
Figure 2.A print screen of Steps 4-7 in Usage of Lane Closure.exe................................. 83
Figure 3.A print screen of Functioning of Lane Closure.exe ........................................... 84
Figure 4.A print screen of RSZ Data Reduction.exe ........................................................ 85
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List of Tables
Table 1.Cost Coefficients for each Traffic Composition.................................................... 7Table 2.Connector Costs..................................................................................................... 7
Table 3. HOV Schedule for Corridors in NOVA Network ................................................ 8
Table 4.Speed and Deceleration values used for lanes on SH 267..................................... 9Table 5.Detector Data Quality Summery by Interstate..................................................... 12
Table 6. I-66 EB Volume Data from Detectors ................................................................ 16Table 7. I-66 EB Volume Data from Videos .................................................................... 16
Table 8. I-66 WB Volume Data from Detectors............................................................... 17
Table 9. I-66 WB Volume Data from Videos................................................................... 17
Table 10. I-95 NB Volume Data from Detectors.............................................................. 18
Table 11. I-95 NB Volume Data from Videos.................................................................. 18
Table 12. I-395 NB Volume Data from Detectors............................................................ 19
Table 13. I-395 NB Volume Data from Videos................................................................ 19
Table 14. I-95 SB Volume Data from Detectors .............................................................. 20Table 15. I-395 SB Volume Data from Detectors ............................................................ 20
Table 16. Extrapolation of Missing Data.......................................................................... 23
Table 17. Details of OD Matrices for each Freeway ........................................................ 25
Table 18. Relative Error for Simulations using Default Parameters ................................ 36
Table 19.Relative Error for Simulations using C98 and Default Parameters................... 40Table 20. C98 and Default Parameters ............................................................................. 45
Table 21. Stations defined for Simulated Counts and Speeds .......................................... 48
Table 22. Travel Time Sections Defined in the Network ................................................. 49Table 23. Link IDs/Locations for Density ........................................................................ 50
Table 1.Simulated Counts for I-66 EB ............................................................................. 53
Table 2.Simulated Speeds for I-66 EB ............................................................................. 54
Table 3.Simulated Travel Times for I-66 EB ................................................................... 55
Table 4.Simulated Densities for I-66 EB.......................................................................... 57
Table 5.Simulated Delays for I-66 EB.............................................................................. 58
Table 6.Simulated Counts for I-66 WB............................................................................ 59
Table 7.Simulated Speeds for I-66 WB............................................................................ 61Table 8.Simulated Travel times for I-66 WB ................................................................... 62
Table 9.Simulated Densities for I-66 WB......................................................................... 63
Table 10.Simulated Delays for I-66 WB .......................................................................... 65
Table 11.Simulated Counts for I-95 and I-395 NB........................................................... 66Table 12.Simulated Speeds for I-95 and I-395 NB........................................................... 67
Table 13.Simulated Travel Times for I-95 and I-395 NB ................................................ 68
Table 14.Simulated Densities for I-95 and I-395 NB....................................................... 70
Table 15.Simulated Delays for I-95 and I-395 NB........................................................... 71
Table 16.Simulated Counts for I-95 and I-395 SB ........................................................... 73Table 17.Simulated Speeds for I-95 and I-395 SB........................................................... 74
Table 18.Simulated Travel times for I-95 and I-395 SB .................................................. 75
Table 19.Simulated Densities for I-95 and I-395 SB........................................................ 77
Table 20.Simulated Delays for I-95 and I-395 SB ........................................................... 79
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Final Report
Development of VISSIM Base Model for Northern Virginia (NOVA)Freeway System
June 2008
Dr. Byungkyu Brian Park and Srividya Santhanam
Traffic Operations Laboratory
Center for Transportation Studies
University of Virginia
IntroductionThe purpose of this project is to provide Northern Region Operations (NRO) staff
with a microscopic traffic simulation model for major Northern Virginia (NOVA)
Freeway network using VISSIM. These freeways include four Interstate Highways (I-66,
I-495, I-395, and I-95) and a State Highway (SH 267). This network shown in Figure 1 is
referred to NOVA network in the remainder of the report.
Figure 1.Northern Virginia (NOVA) Freeway network (source: Google map)
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Purpose and Scope
The project deliverables to the Northern Region Operations (NRO) staff are
calibrated network for the NOVA freeway system in VISSIM program along with O-D
tables and base measures of effectiveness. The main tasks involved in completing the
project were: (1) network building, (2) OD estimation and (3) simulation model
calibration.
Organization of the Report
The following chapters of this report describe the details on the important aspects
of each task. The first chapter explains the network coding efforts in VISSIM and some
important characteristics of the NOVA network. The second chapter presents the data
collection and the O-D estimation efforts that were carried out for the project. The third
chapter focuses on the procedure adopted for the calibration of the VISSIM freeway
network model. The base measures of effectiveness for each of the corridors of the
NOVA network have been tabulated in the Appendix A. Furthermore some guidelines for
the usage of applications developed for the model have also been presented in the
Appendix B.
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Chapter 1: Network Coding
Building an accurate VISSIM model from scratch requires scaled maps showing
the real network in detail. In order to replicate the modeled infrastructure network to
scale, aerial photos (which served as base maps) were obtained and used as background
images and VISSIM network was traced exactly according to the scaled maps.
Dynamic Assignment (DA) module was chosen to model the route choice
behavior of drivers for the freeways. As such, the specification of Origin Destination
matrices are needed for input flows. To define travel demand using an OD matrix, the
area to be simulated is divided into sub-areas called zones and to model the points
where vehicles actually enter or leave the road network, a network element called
parking lot is created. To reduce the complexity of the network, parts of the network
where paths could diverge are defined using network element node.
The following sections list a few important aspects of the network.
1.1 Parking Lots and Nodes
a) There are two types of Parking Lots that can be used in DA module of VISSIM
Abstract Parking Lot and Zone Connector. Abstract parking lots are used if the
road network is detailed enough to represent actual parking lot. The vehicles
approaching abstract parking lot slow down until they come to a stop at the
middle of the parking lot. Their capacity is limited to 700 vehicles per hour per
lane. When Zone Connectors are used, entering vehicles do not slow down and
are just removed from the network as they reach the middle of the parking lot.
Thus the entry capacity of a zone connector is not restricted and this type is
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appropriate to model origin and destination points where traffic enters or exits the
network without using real parking. Hence Zone Connectors were used to model
the entry and exit points for the NOVA network.
b) As mentioned earlier, nodes are created where paths diverge or merge. It is
imperative to include the connectors at diverging and merging paths of the
network within a single node.
c) From the information given by the users definition of nodes, VISSIM builds an
abstract network graph consisting of edges (which distinguish them from the
links of the basic VISSIM network) when the Dynamic Assignment is started.
The edges are the basic building blocks of the routing search. For all the edges
travel times and costs are computed from the simulation and they are used in the
route choice model.
1.2 Vehicle Types and Traffic Composition
In VISSIM, vehicles that share common vehicle performance attributes are added
into a single group, categorizing vehicle population into vehicle types. Each vehicle type
is defined with several attributes such as vehicle model, minimum and maximum
acceleration/deceleration, weight, length, etc. Based on the characteristics of each
corridor of the NOVA network, 3 main vehicle types were created GP, HOV and HGV.
The GP type represented vehicles with a single occupant and those that used the General
Purpose lanes on the Interstate Highways I-66, I-95 and I-395 of the NOVA network. The
HOV type represented vehicles with 2 or more occupants on I-66; vehicles with 3 or
more occupants on I-95 and I-395; thus making them eligible to use the HOV lane on the
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corresponding corridors of the NOVA network (the HOV lanes are being operated during
the peak hours). The HOV lane schedule for each corridor is listed in
Table 3. The specifications and characteristics for these two types were identical
to those of the default CAR type in VISSIM. The HGV type represented the heavy
vehicles on the network with vehicle type characteristics of default HGV type in
VISSIM.
In VISSIM, the vehicle mix of each network entrance flow for the network is
defined by traffic composition. For example, three traffic compositions were defined for
the I-66 EB network: GP for general purpose lane use (100 % GP type), HOV for those
eligible to use HOV lane (100% HOV type) and HV for general purpose lane use (100 %
HGV type).
1.3 HOV Lanes and Link/Connector Costs
Some interchanges on I-66 of the NOVA network contain auxiliary lanes and it
was observed during test simulations that vehicles chose the auxiliary lanes instead of the
mainline path causing congestion in the nearby areas and eventually reducing the
vehicular speeds. This undesired behavior during simulation was rectified by adding costs
to the links containing the auxiliary lanes. Travel time, travel distance, and financial cost
(e.g., tolls) are the factors that influence route choice in VISSIM. The General Cost for
all edges is computed as a weighted sum:
General Cost = * travel time + * travel distance + * financial cost (1)
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The coefficients , , can be defined by the user for a particular vehicle class.
Thus by adding financial cost on appropriate links, the route choice of the vehicles can be
altered as desired during the simulation.
As noted, the Outside Beltway section of I-66 features a High Occupancy Vehicle
(HOV) lane (not barrier separated, open to HOV 2+ vehicles during the peak hours) and
Hard Shoulders (open to all vehicles during peak hours). Simulation of this feature
requires lane closure/open by time of day (simulation time) which VISSIM does not
currently provide directly. Taking advantage of the VISSIM COM Interface Module, a
separate application was developed in Visual C++ to implement this Lane Closure
Facility. HOV only restrictions were enforced by creating separate vehicle type for the
HOV eligible vehicles, and by closing the HOV only lanes to all non-HOV types.
Just closing relevant lanes alone did not necessarily encourage HOV eligible
vehicles to use the HOV lane. The DA in VISSIM needs to identify the HOV and
General Purpose (GP) lanes as two separate routes in order to assign a realistic proportion
of HOV eligible vehicles on the HOV lane/route. Since I-66 EB network is characterized
by HOV lanes that are not barrier separated from GP lanes, the only way to make
VISSIM identify it as separate path/route was by defining separate connectors and adding
appropriate costs for these connectors. Hence separate connectors were defined between
the upstream and downstream links consisting of the HOV lane while separate connectors
were defined for the same links consisting of the GP lanes.
A separate link cost coefficient was assigned to each vehicle type. Thus, based on the
general cost computed using the cost coefficients and the link/connector costs assigned in
the network, vehicle route choice became available in VISSIM. During the verification
process undesired lane change behavior by HOV eligible vehicles was often observed. As
vehicles moved from upstream link to downstream links, it was observed that the HOV
eligible vehicles used the GP lane connector rather than using the HOV lane connector,
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causing congestion at several nodes. To correct for this undesired vehicle movement,
costs were introduced to GP lane connectors. Assigning cost coefficient to HOV
vehicle type alone (assigning 0= to all other vehicle types) made the GP connector
costs applicable to only HOV vehicles. Thus, magnitudes of the cost coefficients and
connector costs were chosen in a manner that made the path with the HOV lane as the
minimum-cost route available to HOV-type vehicles. The magnitudes of the costs and the
coefficient values assigned in the model were solely based on watching animations for
avoiding unrealistic lane change behavior by HOV eligible vehicles. Based on watching
animations, assigning 500= for HOV vehicle type was suitable for realistic lane
change behavior. Table 1 lists the cost coefficients associated with each trafficcomposition and
Table 2 summarizes the connector costs defined in the network.
Table 1.Cost Coefficients for each Traffic Composition
Traffic Composition/ CostCoefficients
GP HV HOV
1 1 1
0 0 0 0 0 500
Table 2.Connector Costs
Connector Type Associated Costs
GP Lane Connector 5/ mile
HOV Lane Connector 0/ mile
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For this study, it is assumed that the cost coefficient values and connector costs
were well calibrated based on watching animations. Thus, these local parameters were
not considered during the entire network model calibration.
For I-95 and I-395 which feature Reversible High Occupancy (RHOV) lanes,
similar strategy of closing and opening links and lanes was used. In this case HOV 3+
vehicles were eligible to use the HOV lanes during the peak hours for each direction (NB
during morning peak, and SB during evening peak).
Table 3 lists the HOV schedule for the freeways in consideration.
Table 3. HOV Schedule for Corridors in NOVA Network
Freeway/Direction HOV Eligibility Hours of Operation
I-66 EB (Outside Beltway) HOV 2+ 5:30 9:30
I-66 EB (Inside Beltway) Only HOV 2+ 6:30 9:00
I-66 WB (Outside Beltway) HOV 2+ 15:00 19:00
I-66 WB (Inside Beltway) Only HOV 2+ 15:30 18:30
I-95 and I-395 NB HOV 3+ 6:00 9:00
I-95 and I-395 SB HOV 3+ 15:30 18:00
1.4 Toll Roads and Stop Signs
SH 267 consists of toll roads and toll free roads. Most of the toll booths are
located on the on- and off-ramps while a few of them are located on the main lines. Stop
signs have been defined in the network to imitate the toll booths that have cash/credit
card service as they prevent multiple vehicle entrance at the same time. Dwell times for
these stop signs follow a normal distribution N (8, 1) for vehicles using Cash lanes. That
is, the time distribution has a mean of 8 seconds and standard deviation of 1. For vehicles
using Smart tag lanes, the stop signs follow a normal distribution N (3, 1). Furthermore,
reduced speed areas were defined on certain lanes of SH 267. Table 4 gives the details of
the speeds and deceleration values used for these lanes.
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Table 4.Speed and Deceleration values used for lanes on SH 267
Name of the RoadNumber of Dedicated
Smart Card lanes
Minimum
Speed (mph)
Maximum
Speed (mph)
Deceleratio
n (ft/s2)
DTR 7 30 40 6.562
Dulles Greenway 4 7 15 6.562
It must be noted that the speed and deceleration values have been assumed based
on the information available at the Toll road websites.
1.5 Driving Behavior Parameters Based on Link Type
In VISSIM, driving behavior is associated to each link by its link type. Though it
is possible to define a different driving behavior parameter set for each vehicle type
within the same link, it has been assumed in this project that rather than vehicle type,
these parameters are associated to the vehicle position in the network. For example, it
was observed that some on- ramps on the NOVA network had heavy demand during the
morning peaks and in reality the mainline vehicles on the right most lanes in the
acceleration/merge link tend to slow down and yield to the vehicles from the on ramp.
Thus, driving behavior of vehicles from the on-ramp may be different from those on the
mainline near the merge section.
In order to generate this behavior the on-ramp and acceleration links were defined
as separate type enabling to define and control driving behavior parameters in a manner
that would make the drivers from the on-ramps more aggressive than the ones on the
right most lane on the mainline. Some factors that could be adjusted to increase/decrease
aggressiveness of drivers are the -1 ft/s2 per distance reductions factor, accepted
deceleration, safety distance reduction factor (SDRF) which are available in VISSIM
under the Lane Change parameters of the Driving Behavior section. Based on assistance
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from PTV America staff and test runs, it was observed that altering SDRF values
influenced the aggressiveness of the drivers, at least increased aggressiveness. By
defining different link types for the on-ramp acceleration lanes and lowering the SDRF
values for these links (to around 0.2 against the default value of 0.6), it was possible to
generate realistic merge behavior on the freeway.
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Chapter 2: O-D Estimation
One of key elements in the microscopic simulation modeling process is to
estimate OD table. OD tables can be either obtained from conventional household
surveys or estimated using sensor counts from freeway mainlines and on- and off- ramps.
Given that the cost of collecting OD information from field is so expensive, the OD
tables for this project were estimated using sensor counts. However, since most of
available sensors were located on mainline freeway, there was a need to collect on- and
off-ramp volumes, especially where no sensors are available. Once the mainline and ramp
volume counts were obtained, QUEENSOD method was used in the estimation of OD
tables. For the NOVA project, it was required to look into a 15 hour time period (5 AM to
8 PM), the OD demands were estimated by each hour and a total of 15 OD tables were
developed for each freeway and direction.
2.1 Data Collection
The link volumes required to carry out the OD estimation for all the freeways in
the NOVA region were collected through the three main sources Detector information
through the STL Database; Video images accessed at http://vds.trafficland.com and
AADT data accessed from the VDOT website.
2.1.1 Data from Detectors
Mainline volume counts for the freeway were extracted from the STL database. In
order to do this, it was necessary to know the detector locations and their quality. Initial
queries were made to retrieve the location information of the detectors such as Interstate
Direction, Type of Link (Mainline/Off Ramp/On Ramp), and Milepost. For the purpose
of identifying good detectors, real time screening tests value were used [1, 2]. Real time
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screening tests values are characteristic to each detector based on the volume and speed
data that the detector reports, giving a measure of whether the data reported by the
detector is reasonable or not. Table 5 summarizes the detectors that were identified as
good ones based on the initial queries.
Table 5.Detector Data Quality Summery by Interstate
Interstate Direction No of Reliable Mainline detectors/No of detectors available
EB 100/190I 66
WB 108/198
NB 72/139
SB 72/133I 95
Rhov 54/75
NB 4/59I 395
SB 4/57
As mentioned earlier, some of the ramp volume counts were collected from the
field through video images transmitted by the CCTV cameras administered and
controlled by the Northern Region Operations. These efforts are explained in the sections
that follow.
Once the location and quality of the detectors were known, the average values of
the six weekday volumes were retrieved from the database. The six weekdays used were
Tue Thurs over two weeks (i.e., May 8-10, 2007 and May 15-17, 2007). By identifying
the detectors available on these freeways, specifying the corresponding detector IDs and
time periods, simple queries in Oracle-SQL Plus were used to retrieve the required
detector information and traffic volume counts.
A sample query that was used to retrieve data from the STL database is as follows
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spool C:\vidya\May\Stn1\496.txt
select t.time_string ||' '|| min(detectorid)||' '|| sum(volume) ||' '||
round(avg(volume),2) ||' '|| count (volume)
from nova.detector_flow df, timex t, calendar c
where
t.time_key between 500 and 2000
and df.detectorid = 496
and df.time_key = t.time_key
and df.calendar_key = c.calendar_key
and c.datex in ('8-May-2007','9-May-2007','10-May-2007','15-May-2007','16-
May-2007','17-May-2007')
and screening_tests not like '%0%'
and screening_tests not like '%9%'
group by t.time_string
/
spool close;
A list of the detectors with their IDs and corresponding locations that have been used for
the OD Estimation task of this project has been provided in Table 6, Table 8, Table 10,
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Table 12,
Table 14 and
Table 15.
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2.1.2 Data from Video Images
Volume data for the on/off ramps at locations characterized by low qualify
detector data was collected by accessing video images at http://vds.trafficland.com. These
videos were recorded and saved using Camtasia software. In order to take advantage of
the CCTV Cameras, time was first spent to identify the camera locations, camera IDs,
and to list the ones that would be helpful in the data collection. For this purpose two visits
were made to the Northern Region Operations TMC in Arlington to obtain detailed
information about the camera locations and the ramps that can be used in the data
collection.
The process of getting the CCTV cameras fixed at the desired locations and
capturing the images for the required time periods was a challenging task as it demanded
uninterrupted video capture and good image quality. Data collection was not possible on
several days in the months of Feb and March 2007 due to very poor video transmission
from most of the cameras. This issue was resolved by using the upgraded version of the
trafficland website (accessed at http://vds.trafficland.com) which was available from
the second week of April 2007. The upgraded system made streamlined videos available
for the cameras located in NOVA region. The volume counts from the video images were
reduced manually which was a time consuming task. Table 7, Table 9,
Table 11 and Table 13 list the locations and the time periods for which the
volume counts have been reduced from the CCTV cameras.
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Table 6. I-66 EB Volume Data from Detectors
LocationLinkID
Type ofLink
StationNo Detector ID
Rte 29 - U 25 ML Stn 1 21-27; 37-43; 53-59; 69 -75; 101-107
Rte 29 - A 27 ML Stn 2 117-123
Rte 29 - D 29 ML Stn 3 126 -132
Rte 28 - A 31 ML Stn 4 144 -150
Rte 28 - D 33 ML Stn 5 155 -163; 173 179; 189 - 195
Rte 7100 - A 37 ML Stn 6 205-211; 715-721
Rte 7100 - D 45 ML Stn 7 235-241
Rte 50 - A 47 ML Stn 8 263-271
Rte 50 - D 51 ML Stn 10 302-308
Rte 243 - U 60 ML Stn 11 338-344
Rte 243 - A 61 ML Stn 12 617-623
Rte 243 - D 73 ML Stn 14 413-419; 421-427
Rte 7 - A 83 ML Stn 19 451-453
Sycamore - A 91 ML Stn22 455-457
G mason - A 95 ML Stn 23 466-468
Table 7. I-66 EB Volume Data from Videos
Location LinkID Type ofLink RampNo Time Period of data from videos
Rte 29 - D 28 On R 2 7:00 - 18:00
Rte 28 - U 30 Off R 3 9:00 - 15:00
Rte 28 - D 32 On R 4 9:00 - 15:00
Rte 50 - D 50 On R 11 6:00 - 8:00; 10:00 - 13:00; 14:00 - 15:00
Rte 123 - U 52 Off R 12 6:00 - 15:00; 16:00 - 18:00
Beltway - U 74 Off R 22 9:00 - 17:00
Beltway - A 76 Off R 23 9:00 - 13:00
Beltway - A 78 Off R 24 11:00 - 13:00
Rte 7 - D 84 On R 27 6:00 - 14:00; 15:00 - 18:00
Rte 110 - A 106 On R 37 6:00 - 18:00
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Table 8. I-66 WB Volume Data from Detectors
LocationLinkID
Type ofLink
StationNo Detector ID
Before Rte 7 163 ML Stn 2 509-510
Rte 7 - U 174 On Det 1 538
Rte 7 - D 176 Off Det 2 537
Rte 243 - U 187 ML Stn 8 570-576
Rte 243 - D 199 ML Stn 12 625-631
Rte 123 - U 203 ML Stn 13 346-352
Rte 50 - D 212 Off Det 3 687
Rte 50 - D 213 ML Stn 18 679-681
Rte 50 - D 214 On Det 4 688
Rte 50 - D 215 ML Stn 19 243-251
Rte 7100 - D 221 Off Det 5 710
Rte 7100 - D 222 Aux Det 6 711
Rte 28 - U 225 ML Stn 23 702-708; 725-731
Rte 28 - U 226 ML Stn 24 197-203
Rte 28 - D 229 Off Det 741
Rte 28 - D 230 ML Stn 26 733-739
Rte 29 - U 232 ML Stn 27 134-142
Rte 29 - U 233 Off Det 7 751
Rte 29 - U 234 ML Stn 28 743-749
Rte 29 - U 235 On Det 8 762
Rte 29 - A 236 ML Stn 29 754-760
Rte 29 - D 237 On Det 9 752
Rte 29 - D 242 ML Stn 32 109-115
Table 9. I-66 WB Volume Data from Videos
Location Link IDType of
LinkRamp
No Time Period of data from videos
TR Bridge Entry 151 ML M 1 5:00 - 20:00TR Bridge - A 152 Off R 2 5:00 - 20:00
TR Bridge - D 153 ML M 3 5:00 - 20:00
Spout run - D 160 On R 4 9:00-12:00; 14:00 - 18:00
N Sycamore - U 165 ML M 5 10:00 - 18:00
N Sycamore - D 166 Off R 6 10:00 - 18:00
Rte 7100 - U 217 Off R 7 6:00 - 10:00; 12:00 - 18:00
Rte 234 - U 243 Off R 8 6:00 - 13:00
Rte 234 - D 247 On R 9 9:00 - 13:00
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Table 10. I-95 NB Volume Data from Detectors
Location Link IDType of
Link Station No Detector IDBeginning of
RHOV 1005Rhov ML
Stn 1 828
Rte 234 - U 859 ML Stn 2 813-815
Rte 234 - A 860 ML Stn 3 816-818
Rte 234 - A 861 Off Stn 4 1489
Rte 234 - D 862 ML Stn 5 1483-1485
Rte 234 - D 864 ML Stn 6 831
Rte 234 - D 865 On Stn 7 837
Rte 784 - U 867 ML Stn 8 840-841
Rte 3000 - U 887 ML Stn 9 893-895Rte 3000 - U 888 Off Stn 10 905
Rte 3000 - D 896 On Stn 11 914
Rte 123 - U 897 ML Stn 12 927-931
Rte 123 - A 899 ML Stn 13 943-944
Rte 123 - A 900 On Stn 14 946
Rte 611 - D 904 ML Stn 15 962-964
Rte 7100 - U 911 Off Stn 16 1032
Rte 7100 - A 912 ML Stn 17 1028-1030
Rte 7100 - A 913 On Stn 18 1033
Rte 7100 - D 916 ML Stn 19 1034-1036
Rte 7100 - D 917 On Stn 20 1038
Rte 644 - U 1019 Rhov Off Stn 21 1057Rte 644 - U 1020 ML Stn 22 1329-1335
Table 11. I-95 NB Volume Data from Videos
Location Link IDType of
LinkRamp
No Time Period of data from videos
Rte 642 - D 909 On R 26 7:00 - 8:00
Rte 644 - U 919,920 Off R 31 6:00 - 7:00
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Table 12. I-395 NB Volume Data from Detectors
Location Link IDType of
Link Station No Detector ID
Rte 648 - A 940 ML Stn 1 1147-1149
Rte 648 - D 941 On Stn 2 1182
Rte 236 - D 946 ML Stn 3 1161-1165
Rte 236 - D 947 On Stn 4 1816
Rte 420 - U 949 Off Stn 5 1586
Rte 420 - A 950 ML Stn 6 1181-1183
Rte 420 - D 951 On Stn 7 1820
Rte 7 - A 954 ML Stn 8 1184-1185
Rte 7 - D 957 On Stn 9 1829
Rte 402 - A 960 ML Stn 10 1200-1203
Rte 402 - D 965 On Stn 11 1833
Rte 120 - A 968 ML Stn 12 1204-1207
Rte 110 - U 1038 Rhov ML Stn 13 1617-1618
Rte 110 - U 1037 Rhov On Stn 14 1619
Table 13. I-395 NB Volume Data from Videos
Location Link IDType of
LinkRamp
No Time Period of data from videos
From I-495 934,935 On R 0 5:00 - 20:00
Rte 648 - A 939 Off R 2 5:00 - 7:00; 8:00 - 18:00Rte 120 - U 967 Off R 15 5:00 - 20:00
Rte 27 - U 978 Off R 19 5:00 - 10:00
Rte 27 - A 976 ML M 19-20 5:00 - 10:00
Rte 27 - A 1034 Rhov ML R 102 5:00 - 10:00
Rte 27 - D 972 Off R 20 7:00 - 13:00
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Table 14. I-95 SB Volume Data from Detectors
Location Link IDType of
Link Station No Detector ID
Rte 644 - D 1416 Rhov Stn 1 1328
To Rte 617 1204 ML Stn 2 1063-1066
Rte 7100 - U 1205 ML Stn 3 1047-1050
Rte 7100 - A 1208 On Stn 4 1355
Rte 611 - U 1218 ML Stn 5 1405-1407
Rte 611 - U 1219 Off Stn 6 968
Rte 611 - A 1220 ML Stn 7 965-967
Rte 611 - D 1221 On Stn 8 1422
Rte 123 - U 1223 Off Stn 9 952
Rte 123 - U 1224 ML Stn 10 953-957
Rte 123 - A 1225 On Stn 11 1423Rte 123 - D 1226 ML Stn 12 1424-1426
Rte 123 - D 1227 On Stn 13 1427
Rte 123 - D 1228 ML Stn 14 933-937
Rte 3000 - U 1229 Off Stn 15 915
Rte 3000 - U 1230 ML Stn 16 910-912
Rte 3000 - D 1233 On Stn 17 1430
Rte 3000 - D 1234 ML Stn 18 896-899
Opitz Blvd - U 1237 Off Stn 19 889-890
Opitz Blvd - A 1245 ML Stn 20 883-885
Rte 784 - D 1246 ML Stn 21 865-866, 1570
Rte 234 - U 1440 Rhov On Stn 22 870
Rte 234 - U 1247 ML Stn 23 843-845Rte 234 - U 1249 Off Stn 24 836
Rte 234 - U 1250 ML Stn 25 833-835
Rte 234 - D 1252 ML Stn 26 1486-1488
Rte 234 - D 1253 On Stn 27 1490
End of Rhov 1443 Rhov Stn 28 829
Table 15. I-395 SB Volume Data from Detectors
Location Link IDType of
Link Station No Detector ID
Rte 7 - A 1159 ML Stn 1 1260-1263
Rte 420 - D 1164 On Stn 2 1882
Rte 236 - D 1172 On Stn 3 1889
Rte 236 - D 1416 Rhov Stn 4 1153
Rte 648 - A 1179 ML Stn 5 1283-1287
Rte 648 - D 1183 ML Stn 6 1293-1295- A: At the Interchange
- U: Upstream of the Interchange
- D: Downstream of the Interchange
Link ID: Used in the QueensOD estimation input to assign links
Station No and Ramp No: Used for denoting a location
Detector ID: IDs as per the STL database
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2.1.3 Extrapolation of Missing Data
The data could be retrieved from the database to cover the time period 5 AM to 8
PM. The data collected from the detectors and video images in several cases did not
cover the entire time period. For these cases, volume counts were extrapolated from short
term counts to the entire time period by using the flow profiles of mainline, on- and off-
ramp volumes of those that covered the entire period. Figure 2, Figure 3 and Figure 4
show the flow profiles for the mainline, on-ramp and off-ramp of I-66 EB respectively.
0
1000
2000
3000
4000
5000
6000
7000
8000
0 2 4 6 8 10 12 14 16
Hour of the Day (5:00 AM - 8:00 PM)
Vehiclesp
erHour
Stn 1
Stn 3
Stn 6
Stn 7
Stn 5
Figure 2. I-66 EB Mainline Flow Profiles
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0
500
1000
1500
2000
2500
3000
0 2 4 6 8 10 12 14 16
Hour of the day (5:00 AM - 8:00 PM)
Vehiclesperhour
On 4
On 2
On 7
On 8
On 11
On 13
Figure 3. I-66 EB On Ramp Flow Profiles
0
200
400
600
800
1000
1200
1400
1600
1800
0 2 4 6 8 10 12 14 16
Hour of the Day (5:00 AM to 8:00 PM)
VehiclesperHour
Off 1
Off 3
Off 9
Off 10
Off 14
Figure 4. I-66 EB Off Ramp Flow Profiles
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Since the sections were fairly long, the variation in the flow profiles could be
attributed to the locations and the respective nature of demands. The obtained profiles
were categorized into sections. Thus, the freeway was divided into sections and the flow
profiles corresponding to a particular section of the freeway were used for the
extrapolation.
For example, the video counts for On-ramp 2 did not cover the time period 5:00
AM 7:00 AM (Figure 4). The flow profile of the On-ramp 4 (closest to On-ramp 2) was
used to extrapolate the missing data for the time period 5:00 AM 7:00 AM.
Table 16. Extrapolation of Missing Data
On-Ramp 2 (initial) missing missing 872On-Ramp 4 1483 1716 1605
Flow Profile of On 4 (based on 3rd time period) 0.923988 1.069159 1
On-Ramp 2 (Extrapolated) 806 932 872
The extrapolated values were further adjusted to balance the flows based on the
available mainline counts upstream and downstream of the corresponding ramps. Missing
data and extrapolated data for this particular case is shown in Table 16. Similar
extrapolation was done to obtain missing counts on the other sections.
The data collected through the detectors and videos did not cover all the links of
the NOVA network. To overcome these, AADT data published for year 2006 (accessible
at http://www.virginiadot.org/info/ct-TrafficCounts-2006.asp ) were used to supplement
and give an estimation of the volumes of other links. Once an estimate of the total
volume say x for a particular link was obtained for the 15 hour period from the AADT
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data, the flow profiles were used to distribute this x for each hour within the 15 hour
time period.
2.2 O-D Estimation Using QUEENSOD
Van Aerde et al. [3] proposed the QUEENSOD model for generating dynamic
synthetic O-D matrices and also tested it on a 35 km section of Highway 401 in Toronto
and the Santa Monica Smart Corridor in Los Angeles. Many of the techniques developed
for estimating O-D matrices from link flow counts are based on very similar but slightly
different mathematical/statistical properties of the final matrix obtained. In many
practical applications, it is unclear as to how significant these mathematical/theoretical
intricacies are in view of the amount and quality of data that must be used as inputs.
The QUEENSOD model aims at finding an O-D matrix that minimizes the
discrepancies between estimated and observed link flows. For static O-Ds this approach
may be mathematically expressed as
( )aav
vvFa
,min (2)
subject to
10, =a
ij
ij
a
ijija ppTv
where
( )aa vvF , = function of the general error measurement between av and av ,
av = observed link flow in linka
av = estimated link flow in linka
ijT = estimated trips leaving zone i to zonej
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a
ijp = proportion of trips from zone i to zonej traveling through linka,
i = origin,
j = destination, and
a = link identifier
The QUEENSOD model starts with a seed matrix (uniform or historic matrix) and
uses this seed matrix to estimate the link flows. Based on quantitative comparisons
between observed and estimated link flows, adjustments are made on the seed O-D
matrix. This involves adjusting the seed O-D matrix by calculating error correction
factors between the actual link volumes and estimated link volumes. The adjusted seed
O-D matrix is updated in the subsequent iterations to obtain new error correction factors
until the error between the actual link volumes and estimated link volumes are
minimized. A more detailed description of this model can be found in [4]. Hourly OD
matrices for each vehicle type (GP, HOV, HGV) were generated. Table 17 summarizes
some details on the OD Estimation for each freeway.
Table 17. Details of OD Matrices for each Freeway
Interstate Direction Vehicle Types included in OD matrix
I 66 EB GP, HOV 2+, HVs
I 66 WB GP, HOV 2+, HVsI 95 and I - 395 NB GP, HOV 3+, HVs
I 95 and I - 395 SB GP, HOV 3+, HVs
I 495 EB/WB GP, HVs
SH 267 EB/WB Cash (GP), E-Z Pass (HOV)GP: General Purpose
HOV: High Occupancy Vehicle
HVs: Heavy Vehicles
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Estimation using QUEENSOD required preparing several input files which are
explained as follows.
Feasible OD Pairs This input file consists of the feasible OD pairs for a particular
corridor and each of the pair was assigned a unique number to be referenced by the
QUEENSOD during the estimation process
Minimum Path Trees This input file is related to the sequence of links that make up the
path for a particular OD pair. For example, if the path tree for a particular OD pair is as
follows.
6601-6606: 25 26 27 28
This denotes a path for the OD pair 6601 6606 and vehicles will take the path
consisting of the link sequence 25, 26, 27 and 28. Since the NOVA network did not have
multiple paths, it was fairly easy to come up with the path trees and these were done
manually.
Link Volume This input file consists of volume data for each link that will be used in
the OD estimation process by identifying the possible OD pair and the link sequence or
path tree for the same.
Once the required input files for the corridor was prepared, QUEENSOD
algorithm coded in MATLAB was used to come up with the corresponding OD tables.
QUEENSOD is an iterative method that tries to balance the link volumes across the paths
for the various OD pairs. The number of iterations used during the OD estimation process
was 100.
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2.3 Measures of Effectiveness Data Collection
Field travel time data collected in January 9 through 11, 2007 and May 25
through 30, 2007 were prepared for sections on I-66, I-95 and I-395 of the NOVA
network and used for the calibration purpose. Detector counts and speeds for the
corresponding days in January and May 2007 were retrieved from the STL database and
used in an effort to look at multiple MOEs during the calibration process.
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Chapter 3: Simulation Model Calibration
3.1 IntroductionThis chapter explains the application and results of the experimental design
approach using Latin Hypercube Sampling (LHS) method for calibration of a portion of
I-66 Eastbound of the NOVA network modeled in VISSIM. Figure 5 depicts the steps
that were performed in an effort to obtain most suitable combination of driving behavior
parameters for the developed model using experimental design procedure.
Figure 5. Flow Chart for Calibration Using Latin Hypercube Experimental Design
Simulation Model Setup
Initial Evaluation
Experimental Design(LHD)
Adjust Key ParameterRanges (such as SDRF)
Evaluation of the CalibratedParameter Set
(Carry out morereplications with selected
parameter set)
End
Unsatisfied
Satisfied
Yes
No
Satisfied
Parameter SelectionBased on Multiple MOEs(TT, Counts and Speeds)
SatisfactoryMOEs?
Unsatisfied
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3.2 Latin Hypercube Sampling
Latin Hypercube Sampling (LHS) [5] is a statistical method that is used to
generate a distribution of plausible collections of parameter values from a
multidimensional distribution. A simple method to generate n combination sets of k
variables is to use random sampling. An alternate method to generate them would be to
use stratified sampling, which can characterize the population of each variable equally
well as random sampling with a smaller sample size.
In stratified sampling of a single variable X1, the distribution of X1 is divided into
m segments. The distribution of n samples over these segments is proportional to the
probabilities of X1 falling in the segments. Each sample is drawn from its segment by
simple random sampling and maximum stratification takes place when the number of
segments m equals the number of samples n required.
For a multi variable case (generating n combination sets of k variables X1,
X2Xk), an efficient sample is the one that is maximally stratified for all the variables
simultaneously. That is, the range of each variable is divided into n non-overlapping
intervals on the basis of equal probability, and one value from each interval is randomly
selected. The n values obtained for X1 are randomly paired with n values of X2. These n
pairs are then combined in a random manner with n values of X3 to form n triplets and so
on. Thus, nk-tuplets can be formed in this fashion (Latin Hypercube Samples). These
samples can be thought of as forming an input matrix of order (n x k) where the
combination set in the ith
row containing values for k input variables can be used as input
of ith run of a computer model.
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A Two-Dimensional representation of One Possible Latin Hypercube Sample of
Size 5 for X1 and X2 is shown in Figure 6.
Figure 6.Two-dimensional representation of a LHS of size 5 for X1 and X2
3.3 Experimental Design Results
As a part of the project, the site and time period chosen for the initial calibration
efforts for the NOVA network is I-66 Eastbound from Rte 15, Gainesville to I-495
Beltway (just after Rte 243) from 5:00 AM to 8:00 AM. This is a 23.5 mile section of the
freeway that is heavily congested in the morning. During the remainder of this chapter,
this portion of I-66 Eastbound has been referred to as I-66 EB network. The network
alignment for I-66EB network is shown in Figure 7.
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Figure 7. I-66EB Network
3.3.1 Simulation Model Setup
The 23.5 mile section of I-66 EB freeway was coded in VISSIM (a part of the
NOVA network coded in VISSIM), and the traffic data for the time period 5:00 AM
8:00 AM (O-D estimates using QUEENSOD method) were input into the model.
3.3.2 Initial Evaluation
In order to test if the default values for the driver behavior parameters in VISSIM
were sufficient to generate field conditions, 25 replications were conducted for the
network. The travel time simulation outputs based on 5 replications have been shown in
Figure 8, Figure 9 and Figure 10. Since the probe vehicles that were used to collect the
travel time from the field traveled on General Purpose (GP) lanes of the I-66 EB network,
the simulated travel times corresponding to GP vehicle type were used in the analysis.
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The default parameters did not represent the field conditions, especially with the
3rd time period TT. Moreover, volume counts were low through out the simulation period.
Comparison of volume counts and speeds from simulations and detectors have been
presented in Figure 11, Figure 12, Figure 13 and Figure 14 for two stations namely
station 2 and station 10. Thus it was necessary to conduct the other steps proposed in the
calibration procedure. It must be noted that the first 15 minutes of the simulation was
considered as warm up time and MOEs were collected from 5:15 AM onwards, for every
15 minutes.
0
100
200
300
400
500
600
700
800
550 600 650 700 750 800 850 900
Travel Time (seconds)
Frequenc
y
Figure 8. Simulated TT Vs Observed TT (6:00-6:30 AM)-Default Parameters
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0
50
100
150
200
250
300
550 600 650 700 750 800 850 900 950 1000 1050 1100 1150 1200 1250
Travel Time (seconds)
Frequency
Figure 9. Simulated TT Vs Observed TT (6:30-7:00 AM)-Default Parameters
0
50
100
150
200
250
550
600
650
700
750
800
850
900
950
1000
1050
1100
1150
1200
1250
1300
1350
1400
1450
1500
1550
1600
1650
Travel Time (seconds)
Frequency
Figure 10. Simulated TT Vs Observed TT (7:30-8:00 AM)-Default Parameters
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0
200
400
600
800
1000
1200
1400
1600
1800
2000
2700 3600 4500 5400 6300 7200 8100 9000 9900 10800
Simulation time in seconds (5:30-8:00 AM)
Numberofvehicles Default_1
Default_2
Default_3
Default_4
Default_5
May 25th Det data
Jan 10th Det Data
Figure 11. Simulated Counts Vs Detector Counts for Station 2-Default Parameters
0
10
20
30
40
50
60
70
80
900 1800 2700 3600 4500 5400 6300 7200 8100 9000 9900 10800
Simulation time in seconds (5:30-8:00 AM)
Speedinmph
Default_1
Default_2
Default_3Default_4
Default_5
May 25th Det speed
Jan 10th Det Data
Figure 12. Simulated Speed Vs Detector Speed for Station 2-Default Parameters
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0
200
400
600
800
1000
1200
1400
1600
1800
2700 3600 4500 5400 6300 7200 8100 9000 9900 10800
Simulation time in seconds (5:30-8:00 AM)
Numberofvehicles Default_1
Default_2
Default_3
Default_4
Default_5
May 25thDet data
Jan 10th Det Data
Figure 13. Simulated Counts Vs Detector Counts for Station 10-Default Parameters
0
10
20
30
40
50
60
70
900 1800 2700 3600 4500 5400 6300 7200 8100 9000 9900 10800
Simulation time in seconds (5:30-8:00 AM)
Speedinmph
Default_1
Default_2
Default_3
Default_4Default_5
May 25th det speed
Jan 10th Det Data
Figure 14. Simulated Speed Vs Detector Speed for Station 10-Default Parameters
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Relative errors (based on 25 replications from simulation for default parameters) for
travel time speeds and counts have been summarized in Table 18.
Table 18. Relative Error for Simulations using Default Parameters
Relative Error Default
TT Error (3rd Time period) 1.35
Counts Error (Stn 2) 0.205
Speed Error (Stn 2) 0.36
Counts Error (Stn10) 0.156
Speed Error (Stn 10) 0.188
3.3.3 Initial Calibration Latin Hypercube Design (LHD) for Driving Behavior
The initial set of parameters that were identified as relevant to the performance of
the simulation model with their acceptable ranges are as follows:
1) Waiting time before diffusion (seconds): 30-90
2) Min. Headway (front/rear, meters): 0.1-0.9
3) Max. Deceleration Own vehicle Freeway link (m/s2): -5.00 ~ -1.00
4) Reduction Rate (meters per 1m/s2) Own vehicle Freeway link: 20-80
5) Accepted Deceleration (m/s2) Own vehicle Freeway link: -3.0 ~ -0.2
6) Max. Deceleration Trailing vehicle Freeway link (m/s2): -5.00 ~ -1.00
7) Reduction Rate (meters per 1m/s2) Trailing vehicle Freeway link: 20-80
8) Accepted Deceleration (m/s2) Trailing vehicle Freeway link: -3.0 ~ -0.2
9) Number of observed preceding vehicles: 1 5
10) Maximum look ahead distance (meters): 200 300
11) CC0: average standstill distance (meters): 1.0 2.0
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12) CC1: headway at a certain speed (seconds): 0.5 3.0
13) CC2: longitudinal oscillation (meters): 0 ~ 15.0
14) CC3: start of deceleration process (seconds): -30.0 0
15) CC4: minimal closing v (m/s): -1.0 ~ 0
16) CC5: minimal opening v (m/s): 0.0 ~ 1.0
17) CC6: dv/dx (10-4
rad/s): 0.0 ~ 20.0
18) CC7: car following activities b (m/s2): 0.0 ~ 1.0
19) CC8: acceleration behavior when starting (m/s2): 1.0 ~ 8.0
20) CC9: acceleration behavior at v ~ 80 km/h (m/s2): 0.5 ~ 3.0
21) Speed Index 1 (mph): 1 ~ 3 (55 ~ 65, 65 ~ 72.5, 65 ~ 75)
22) Speed Index 2 (mph): 1 ~ 3 (60 ~ 70, 65 ~ 72.5, 65 ~ 75)
23) Speed Index 1 (mph): 1 ~ 2 (65 ~ 75, 65 ~ 72.5)
24) Max. Deceleration Own vehicle Ramp Merge Link (m/s2): -5.00 ~ -1.00
25) Reduction Rate (meters per 1m/s2) Own vehicle Ramp Merge Link: 20-80
26) Accepted Deceleration (m/s2) Own vehicle Ramp Merge Link: -3.0 ~ -0.2
27) Max. Deceleration Trailing vehicle (m/s2) Ramp Merge Link: -5.00 ~ -1.00
28) Reduction Rate (meters per 1m/s2) Trailing vehicle Ramp Merge Link: 20-80
29) Accepted Deceleration (m/s2) Trailing vehicle Ramp Merge Link: -3.0 ~ -0.2
Parameters 21, 22 and 23 set desired speed distributions along the I-66 EB
network based on their locations where three different posted speed limits are present.
These were indexed for the convenience of experimental design. Thus, speed index 1 and
2 have 3 options to define the desired speed distribution on the freeway where the posted
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speed limits are 55 mph and 60 mph respectively. Speed index 3 has two options to
define the desired speed distribution where the posted speed limit is 65 mph.
The initial ranges for the lane change and car following parameters were obtained
from [6]. Definitions of these parameters and their functioning in VISSIM can be
obtained from [7]. As mentioned earlier, the Safety Distance Reduction Factor (SDRF)
defined for the links plays an important role in altering the aggressiveness of the drivers
especially near the ramp merge areas. Based on the initial test runs for the I-66 EB
network, the SDRF values for the freeway link and ramp merge link were fixed to 0.6 and
0.2 respectively during the experimental design.
3.3.3.1 Experimental Design Latin Hypercube Sampling
300 combination sets/samples of the 29 parameters were generated using Latin
Hypercube Sampling coded in MATLAB.
3.3.3.2 Multiple Runs
Five replications for each of the 300 cases were conducted in VISSIM, for a total
of 1,500 runs. For these runs, the aggregated travel times for specific sections were
collected. From the five replications for each case, the weighted average travel time
(based on the number of vehicles) for each case was calculated for three different time
periods: 6:00 AM 6:30 AM, 6:30 AM 7:00 AM, 7:30 AM 8:00 AM. Simulated
counts and speeds for station 2 and station 10 were compared with those from the
detector.
3.3.3.3 Parameter Set Selection
The weighted average travel time for GP vehicles (based on number of vehicles
passing the section) over the 5 replications for each parameter set was calculated. The
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absolute error of this weighted average against the field travel time for each of the 3 time
periods was calculated. Similarly, for volume counts and speeds the average of absolute
error (between 6:00-8:00) over the 5 replications was calculated. Normalization for the
three measures was done by taking logarithm of these absolute errors. Thus, sum of the
logarithms of the error in travel time for each time period; logarithm of the average
absolute error in counts and for each station was determined for comparison.
Based on looking at the errors for all the MOEs together, the sample case C98
was selected from the LHD samples. An X-Y plot of the weighted average travel time of
GP vehicles against the Sample case is shown in Figure 15.
0
500
1000
1500
2000
2500
0 50 100 150 200 250 300
Sample Case
SimulatedTT(WeightedAveragefor5:00-8:00AM)
Figure 15.Simulated TT Vs Sample Case
C98
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Figure 16 shows the variation of the simulated TT with the key parameter CC1
which has a significant impact on the capacity and hence the performance of a network.
0
0.5
1
1.5
2
2.5
3
3.5
0 500 1000 1500 2000 2500
Simulated TT in seconds (Weighted Average for 3 hour time period)
CC1
(seconds)
Figure 16. Simulated TT Vs CC1
25 replications for sample case C98 were carried out and the relative errors
between the simulated and field measures have been summarized in Table 19.
Table 19.Relative Error for Simulations using C98 and Default Parameters
Relative Error C98 Default t-test (p value)
TT Error (3rd timeperiod)
0.247 1.35 7.4 * 10 -27
Counts Error (Stn 2 ) 0.119 0.205 1.4 * 10 -20
Speed Error (Stn 2) 0.15 0.36 2 * 10 -15
Counts Error (Stn 10) 0.09 0.156 8 * 10 -25
Speed Error (Stn 10) 0.09 0.188 5 * 10 -7
C98
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It must be noted that the t-test (p value) only show that the distributions of the
measures obtained for C98 and default parameters are significantly different. However,
the t-test results do not prove that C98 is a better parameter set for the model. By
comparing the distributions of the measures obtained from simulation (using sample case
C98) with those obtained from the field, it was found that the sample case C98 was far
better than the default parameters. Based on the 5 replications for sample case C98,
Figure 17, Figure 18 and Figure 19 depict the travel time distribution from simulations
and field observations. Figure 20, Figure 21, Figure 22 and Figure 23 depict the count
and speed data from simulations and field.
0
100
200
300
400
500
600
700
800
550 600 650 700 750 800 850 900
Travel Time (seconds)
Frequency
Figure 17. Simulated TT Vs Observed TT (6:00-6:30 AM)-C98 (LHD_1)
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0
50
100
150
200
250
300
350
400
550 600 650 700 750 800 850 900 950 1000 1050 1100 1150 1200 1250
Travel Time (seconds)
Frequency
Figure 18. Simulated TT Vs Observed TT (6:30-7:00 AM)-C98 (LHD_1)
0
50
100
150
200
250
300
350
400
550 600 650 700 750 800 850 900 950 1000 1050 1100 1150 1200 1250
Travel Time (seconds)
Frequency
Figure 19. Simulated TT Vs Observed TT (7:30-8:00 AM)-C98 (LHD_1)
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0
200
400
600
800
1000
1200
1400
1600
1800
2000
2700 3600 4500 5400 6300 7200 8100 9000 9900 10800
Simulation time in seconds (5:30-8:00 AM)
Numberofvehicles C98_1
C98_2
C98_3
C98_4
C98_5
May 25th Det data
Jan 10th Det Data
Figure 20. Simulated Counts Vs Detector Counts for Station 2 C98 (LHD_1)
0
10
20
30
40
50
60
70
80
900 1800 2700 3600 4500 5400 6300 7200 8100 9000 9900 10800
Simulation time in seconds (5:30-8:00 AM)
Speedinmph
C98_1
C98_2
C98_3C98_4
C98_5
May 25th Det speed
Jan 10th Det Data
Figure 21. Simulated Speed Vs Detector Speed for Station 2 C98 (LHD_1)
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0
200
400
600
800
1000
1200
1400
1600
1800
2700 3600 4500 5400 6300 7200 8100 9000 9900 10800
Simulation time in seconds (5:30-8:00 AM)
Numberofvehicles C98_1
C98_2
C98_3
C98_4
C98_5
May 25thDet data
Jan 10th Det Data
Figure 22. Simulated Counts Vs Detector Counts for Station 10 C98 (LHD_1)
0
10
20
30
40
50
60
70
900 1800 2700 3600 4500 5400 6300 7200 8100 9000 9900 10800
Simulation time in seconds (5:30-8:00 AM)
Speedinmph
C98_1
C98_2
C98_3
C98_4C98_5
May 25th det speed
Jan 10th Det Data
Figure 23. Simulated Speed Vs Detector Speed for Station 10 C98 (LHD_1)
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Table 20 draws a comparison of the parameter set selected in the LHD with those
of the default values.
Table 20. C98 and Default Parameters
LHS C98 DefaultDiffusion 56.9 60
Min Headway 0.355 0.5
Max Decel -Own (F) -2.38 -4
Reduction Rate-Own(F) 46.7 200
Accepted Decel-Own
(F) -0.275 -1
Max Decel-T (F) -4.62 -3
Reduction Rate-T (F) 74.5 200
Accepted Decel-T (F) -1.194 -0.5
#obs veh 4 2
Max Lookahead 253.5 250
CC0 1.768 1.5
CC1 0.729 0.9
CC2 5.225 4CC3 -16.25 -8
CC4 -0.01167 -0.35
CC5 0.01167 0.35
CC6 4.9 11.44
CC7 0.948 0.25
CC8 3.718 3.5
CC9 2.3375 1.5DS1 3 -
DS2 2 -
DS3 1 -
SDRF (F) 0.6 0.6
Max Decel-Own (M) -1.5 -4
Reduction Rate-Own
(M) 30.5 200
Accepted Decel-Own
(M) -0.7579 -1
Max Decel-T (M) -2.2733 -3
Reduction Rate-T (M) 56.3 200
Accepted Decel-T (M) -0.6433 -0.5
SDRF (M) 0.2 0.6
F Freeway Section, M Ramp Merge Section
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It was found that the driving behavior parameters from sample C98 generated
field conditions with respect to travel times for I-66WB, I-95 and I-395 as well. Since no
field measures were available for I-495 and SH267, animations of the simulations using
C98 as driving behavior parameter set were watched. Thus, this particular combination
set was chosen as calibrated set for the NOVA network.
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Chapter 5: Measures of Effectiveness from the Calibrated Model
In order to obtain base measures of effectiveness for each corridor of the NOVA
network, 25 replications with the calibrated driving behavior parameter set were made.
The following lists the measures that were obtained from the simulation runs.
5.1 Counts and Speeds
For each corridor, simulated counts and speeds were obtained for every 15
minutes and for certain stations that were defined in the VISSIM station. These stations
have been listed in Table 1. The speed values obtained from the simulations are measured
in mph.
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Table 21. Stations defined for Simulated Counts and Speeds
Station Number Route/Direction Location
1 I-66 EB Rte 234
2 I-66 EB Rte 50
3 I-66 EB Beltway (I-495)
4 I-66 EB TR Bridge
5 I-66 WB TR Bridge
6 I-66 WB Beltway (I-495)
7 I-66 WB Rte 50
8 I-66 WB Rte 234
9 I-95/I-395 NB Rte 619/Triangle
10 I-95/I-395 NB Dale City
11 I-95/I-395 NB I-495 Interchange12 I-95/I-395 NB Rte 27
13 I-95/I-395 SB Rte 27
14 I-95/I-395 SB I-495 Interchange
15 I-95/I-395 SB Dale City
16 I-95/I-395 SB Rte 619/Triangle
17 I-495 EB/SB Rte 191
18 I-495 EB/SB I-66 (Beltway)
19 I-495 EB/SB I-95/I-395
20 I-495 EB/SB Rte 1
21 I-495 WB/NB Rte 1
22 I-495 WB/NB I-95/I-39523 I-495 WB/NB I-66 (Beltway)
24 I-495 WB/NB Rte 191
25 SH 267 EB Rte 7
26 SH 267 EB Dulles Airport Access Rd (DAAR)
27 SH 267 EB I-66
28 SH 267 WB I-66
29 SH 267 WB Dulles Airport Access Rd (DAAR)
30 SH 267 WB Rte 7
5.2 Travel Time and Delays
Based on the travel time sections defined, VISSIM calculates the time taken (in
seconds) by vehicles to cross the particular section for the time interval specified. The
travel time sections defined for each corridor are listed in Table 22. Based on the travel
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time sections defined in VISSIM, the delays (in seconds) for every chosen time interval is
calculated.
Table 22. Travel Time Sections Defined in the Network
Travel Time Section Number Route/Direction Location
1 I-66 EB Rte 234 to Rte 50
2 I-66 EB Rte 50 to Beltway
3 I-66 EB Beltway to TR Bridge
4 I-66 WB TR Bridge to Beltway
5 I-66 WB Beltway to Rte 50
6 I-66 WB Rte 50 to Rte 234
7 I-95/I-395 NB Rte 619 to Dale City
8 I-95/I-395 NB Dale City to I-495
9 I-95/I-395 NB I-495 to Rte 27
10 I-95/I-395 SB Rte 27 to I-495
11 I-95/I-395 SB I-495 to Dale City
12 I-95/I-395 SB Dale City to Rte 619
13 I-495 EB/SB Rte 191 to I-66
14 I-495 EB/SB I-66 to I-95/I-395
15 I-495 EB/SB I-95/I-395 to Rte 1
16 I-495 WB/NB Rte 1 to I-95/I-395
17 I-495 WB/NB I-95/I-395 to I-6618 I-495 WB/NB I-66 to Rte 191
19 SH 267 EB Rte 7 to DAAR
20 SH 267 EB DAAR to I-66
21 SH 267 WB I-66 to DAAR
22 SH 267 WB DAAR to Rte 7
5.3 Density
Density for certain links were obtained on each corridor. The location and
corresponding link IDs for which densities were collected have been shown in
Table 23.
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Table 23. Link IDs/Locations for Density
Link ID Route/Direction Location
2005 I-66 EB Rte 234
1601 I-66 EB Rte 50
1517 I-66 EB Beltway (I-495)
1326 I-66 EB TR Bridge
1325 I-66 WB TR Bridge
1513 I-66 WB Beltway (I-495)
1605 I-66 WB Rte 50
1687 I-66 WB Rte 234
1504 I-95/I-395 NB Rte 619/Triangle1383 I-95/I-395 NB Dale City
944 I-95/I-395 NB I-495 Interchange
1948 I-95/I-395 NB Rte 27
1959 I-95/I-395 SB Rte 27
945 I-95/I-395 SB I-495 Interchange
1390 I-95/I-395 SB Dale City
1501 I-95/I-395 SB Rte 619/Triangle
732 I-495 EB/SB Rte 191
84 I-495 EB/SB I-66 (Beltway)
900 I-495 EB/SB I-95/I-395
1871 I-495 EB/SB Rte 11851 I-495 WB/NB Rte 1
905 I-495 WB/NB I-95/I-395
61 I-495 WB/NB I-66 (Beltway)
742 I-495 WB/NB Rte 191
714 SH 267 EB Rte 7
477 SH 267 EB Dulles Airport Access Rd (DAAR)
133 SH 267 EB I-66
134 SH 267 WB I-66
446 SH 267 WB Dulles Airport Access Rd (DAAR)
713 SH 267 WB Rte 7
25 replications for each corridor were made with the calibrated parameter set for
the 15 hour time period (5AM to 8PM) and the measures (counts, speeds, travel times,
delays and densities) were obtained for every 15 minutes. The average over these
replications for each corridor was calculated and has been presented as base measures of
effectiveness in Appendix A.
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Chapter 6: Conclusions and Recommendations
The development of microscopic based traffic simulation model for the NOVA
Freeway system using VISSIM involved three main tasks: network coding, data
collection and O-D estimation and calibration. During the development of the model a lot
of time was spent on data collection and calibration efforts. Based on project study, the
following conclusions were made
(1) Default parameters in VISSIM did not simulate realistic behavior for the network
considered.
(2) Use of travel time alone as a measure for evaluating parameter sets from the
experimental design led to the selection of model parameters that did not generate
realistic outputs for other measures such as counts and speeds.
(3) The Safety distance reduction Factor (SDRF) that influenced the aggressiveness of the
drivers on the acceleration links near the on-ramps played a crucial role in generating
realistic ramp merge behavior. Since the network considered in this study consisted of
several on-ramps with high volumes, this parameter became crucial for the networks
performance.
Based on this study, the application of experimental design approach using Latin
Hypercube Sampling is recommended for the calibration of microscopic simulation
models. Use of multiple measures for evaluating the parameter sets from the
experimental design is also recommended.
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References
[1] Real-time screening tests recommended for the NOVA detector data, STL DatabaseDocuments
[2] Turner, S., Margiotta, R., and Lomax, T.Monitoring Urban Freeways in 2003:
Current Conditions and Trends from Archived Operations Data. December 2004.
[3] Van Aerde, M., B. Hellinga and G. MacKinnon. QUEENSOD: A Method for
Estimating Time Varying Origin-Destination Demands For Freeway
Corridores/Networks. Presented at Annual TRB Meeting, Washington D.C., January
1993.
[4] Park, B. B. and I,Yun. Development of ITS Evaluation Test-Bed Using Microscopic
Simulation City of Hampton Case Study. Research Report No. UVACTS-15-0-45,
2003.
[5] Wyss, D. G. and K. H. Jorgensen.,A Users Guide to LHS: Sandias Latin hypercubeSampling Software. Sandia National Laboratories, Albuquerque, 1998.
[6] VISSIM User Manualversion 4.30. PTV Planung Transport Verkehr AG,
Karlsruhe, Germany, 2007.
[7] Park, B. and J. Won,Microscopic Simulation Model Calibration and Validation
Handbook, Virginia Transportation Research Council, June 2006.
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Appendix A
MOEs for I-66 EB Table 24.Simulated Counts for I-66 EB
1 2 3 4Time Period/
Station Number Mean StDev Mean StDev Mean StDev Mean StDev
5:15 1205.14 9.5 1119.86 11.21 962 18.33 311.79 5.43
5:30 1326.86 70.57 1237.43 44.55 1057.07 49.75 578.36 10.55
5:45 1196.36 93.38 1059.79 38.34 1014.21 50.77 592.07 12.82
6:00 1177.21 91.11 1087.36 79.32 1028.86 43.75 584.64 13.9
6:15 1277.93 79.77 1199 71.34 1220.5 63.08 674.79 26.79
6:30 1237.21 88.18 1409.29 59.9 1426.36 69.03 649.29 31.76
6:45 1217.57 88.46 1460.79 96.38 1336.79 90.69 648.57 30.28
7:00 1202.71 69.93 1532.5 73.89 1346.86 94.64 633 26.69
7:15 1279.29 77.73 1422.79 80.91 1416.29 97.17 635 19.28
7:30 1256.36 99.62 1381.71 59.09 1359.79 90.79 589.07 20.687:45 1230 91.52 1331.57 49.21 1363.43 95.15 551.14 20.69
8:00 1263.93 84.92 1314.57 94.48 1335.43 90.87 538.57 26.4
8:15 1123.21 87.74 1372.36 79.91 1328.71 77.16 561.71 21.59
8:30 1214.21 124.67 1329.21 74.42 1220.07 63.75 684.79 33.02
8:45 1260.57 86.25 1338.07 29.02 1300.29 72.09 718.71 16.87
9:00 1269.5 79.33 1302.86 32.07 1251.93 51.34 724.57 20.71
9:15 1149.21 63.22 1331.57 29.58 1250.79 45.31 723.29 37.97
9:30 1182.43 82.35 1308.64 48.17 1259.36 45.06 706.64 26.52
9:45 1218.64 99.04 1292.86 58.53 1200.71 39.11 703.71 35.1
10:00 1261.29 94.02 1152.29 40.78 968.43 47.95 734.71 26.21
10:15 999.2 9.5 1073.4 9.5 1211.12 18.33 414.84 5.43
10:30 1028.3 70.57 1168.4 70.57 1427.43 49.75 689.94 10.55
10:45 1069.2 93.38 1184.65 93.38 869.46 50.77 682.63 12.82
11:00 963.4 91.11 1074.33 91.11 846.88 43.75 709.8 13.9
11:15 1037.5 79.77 1179.5 79.77 969.98 63.08 647.4 26.79
11:30 925.6 88.18 1225.65 88.18 717.54 69.03 677.43 31.76
11:45 915.4 88.46 1092.6 88.46 869.98 90.69 703.2 30.28
12:00 886.6 69.93 1160.3 69.93 966.5 94.64 674.25 26.69
12:15 920.33 77.73 1100.6 77.73 727.43 97.17 692.74 19.28
12:30 872.6 99.62 1067.2 99.62 970.65 90.79 643.93 20.68
12:45 876.6 91.52 1003.1 91.52 821.54 95.15 695.94 20.69
13:00 804.7 84.92 1093.2 84.92 808.21 90.87 605.62 26.4
13:15 871.7 87.74 1067.5 87.74 1053.76 77.16 659.54 21.59
13:30 806.4 124.67 1039.6 124.67 1276.53 63.75 619.8 33.02
13:45 830.7 86.25 1041.1 86.25 1177.02 72.09 600.5 16.8714:00 899.6 79.33 1057.65 79.33 895.81 51.34 602.4 20.71
14:15 848.3 63.22 1076.7 63.22 1097.3 45.31 657.2 37.97
14:30 825.5 82.35 1079.9 82.35 1117.6 45.06 665.8 26.52
14:45 956.3 99.04 1066.4 99.04 1267.5 39.11 579.23 35.1
15:00 863.6 94.02 1157.6 94.02 1320.9 47.95 609.76 26.21
15:15 912.2 9.5 1106.22 9.5 1255.3 18.33 626.45 5.43
15:30 881.6 70.57 1039.67 70.57 1241.2 49.75 593.92 10.55
15:45 799.2 93.38 1048.6 93.38 1335.4 50.77 596.64 12.82
16:00 902.24 91.11 1052.21 91.11 1211.33 43.75 639.56 13.9
16:15 859.5 79.77 1078.65 79.77 1299.44 63.08 644.76 26.79
16:30 892.6 88.18 1096.32 88.18 1387.32 69.03 698.93 31.76
16:45 865.6 88.46 1116.6 88.46 1435.71 90.69 587.53 30.28
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17:00 865.2 69.93 1042.3 69.93 1283.51 94.64 597.92
17:15 914.5 88.46 1107.6 70.57 1281.42 90.69 617.64 9.5
17:30 870.5 69.93 1062.77 93.38 1450.64 94.64 609.83 70.57
17:45 965.1 77.73 1121.8 91.11 1413.32 97.17 623.84 93.3818:00 837.6 99.62 1071.23 79.77 1277.84 90.79 662.55 91.11
18:15 918.4 91.52 1116.6 88.18 1134.64 95.15 639.74 79.77
18:30 818.6 84.92 1008.7 88.46 1142.84 90.87 629.84 88.18
18:45 792.55 87.74 1011.8 69.93 1218.99 77.16 561.64 88.46
19:00 811.78 124.67 1030.2 77.73 1086.94 63.75 590.34 69.93
19:15 765.65 86.25 908.8 99.62 1214.94 72.09 593.77 77.73
19:30 625.65 79.33 854.9 91.52 1149.64 51.34 609.83 99.62
19:45 664.67 63.22 844.2 84.92 1282.83 45.31 632.53 91.52
20:00 642.76 82.35 843.65 70.57 1304.92 45.06 588.66 84.92
Table 25.Simulated Speeds for I-66 EB
1 2 3 4Time Period/
Station Number Mean StDev Mean StDev Mean StDev Mean StDev
5:15 65.94 1.08 62.47 1.65 57.99 1.09 47.30 0.06
5:30 60.94 2.81 43.98 6.28 56.79 1.45 46.16 1.03
5:45 62.38 3.22 14.18 1.01 57.56 1.35 46.56 1.33
6:00 62.11 3.85 13.99 1.36 57.29 1.48 46.24 2.59
6:15 60.53 3.81 15.65 1.26 44.01 1.41 46.66 1.18
6:30 61.44 3.22 19.44 1.36 36.84 3.45 45.51 3.03
6:45 60.76 3.56 21.69 1.94 29.19 9.82 45.89 2.44
7:00 60.96 3.04 26.53 4.31 25.46 9.13 46.11 1.51
7:15 57.26 8.71 32.22 10.74 24.69 7.14 45.09 2.84
7:30 58.62 9.81 38.66 6.34 21.86 6.87 23.81 23.827:45 60.54 3.49 36.63 8.63 19.56 4.29 46.41 2.12
8:00 59.34 3.01 26.31 8.62 18.47 2.09 46.65 1.35
8:15 62.36 3.52 24.1 5.05 17.53 1.54 45.80 2.74
8:30 62.29 2.34 35.32 8.25 15.88 1.01 44.97 2.21
8:45 61.24 2.48 47.66 6.87 18.07 2.02 42.94 3.20
9:00 61.13 2.6 45.39 5.54 16.72 1.23 42.26 2.80
9:15 62.78 1.25 41.17 7.56 16.59 1.22 43.23 2.11
9:30 63.16 1.87 41.85 8.6 16.86 1.19 41.24 3.37
9:45 62.56 1.87 42.36 10.58 15.59 1.08 41.14 3.73
10:00 62.93 1.69 30.49 15.81 12.05 0.89 42.98 3.04
10:15 68.1 1.48 63.1 1.36 45.6 4.29 47.2 2.44
10:30 67 1.41 37.1 1.26 37.5 2.09 46.9 1.51
10:45 67.2 3.45 17.3 1.36 10.2 1.54 47.1 2.8411:00 68 9.82 16.4 1.94 10.9 1.01 47 23.82
11:15 67.1 9.13 18.8 4.31 11.3 2.02 47 2.12
11:30 67.6 7.14 18.5 10.74 8.4 1.23 47 1.35
11:45 67.8 6.87 43.7 6.34 10.8 1.22 46.9 2.74
12:00 67.9 4.29 61 8.63 12.4 1.19 47.1 2.21
12:15 67.5 2.09 59.5 8.62 8.9 1.08 47.1 3.20
12:30 68.1 1.54 64.6 5.05 11.6 0.89 47 2.80
12:45 68 1.01 64.9 8.25 9.9 4.29 47 2.11
13:00 68.3 2.02 61.1 6.87 10.8 2.09 47.1 3.37
13:15 68.2 1.23 60.5 5.54 13.5 1.54 47.1 3.73
13:30 67.9 1.22 65.3 7.56 17.2 1.01 47 3.04
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13:45 67.4 1.19 63.8 8.6 14.4 2.02 47 2.44
14:00 67.6 1.08 64.4 4.29 10.5 4.29 47.1 1.08
14:15 67.5 0.89 60.4 2.09 13.1 2.09 47 2.81
14:30 67.7 1.48 65.2 1.54 13.8 1.54 47.1 3.2214:45 67.9 1.41 65 1.01 17.9 1.01 47.2 3.85
15:00 68.3 3.45 46.6 2.02 18.5 2.02 47.1 3.81
15:15 67.8 9.82 62.2 1.23 17.6 1.23 47.1 3.22
15:30 68.2 9.13 65 1.22 16.9 1.22 47.1 3.56
15:45 68.3 7.14 63.6 1.19 17.7 1.19 46.9 3.04
16:00 67.9 6.87 65.8 1.08 16 1.08 46.9 8.71
16:15 68.1 4.29 65.2 0.89 18.5 0.89 46.9 9.81
16:30 68 2.09 64 4.29 18.2 4.29 47.1 3.49
16:45 68 4.29 63.6 2.09 19.7 2.09 47 3.01
17:00 68.3 2.09 61.6 3.22 17 1.54 47.2 3.52
17:15 68.1 1.54 65 3.8
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