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Measure travel time by using Bluetooth detectors on

freeway

Yubin Wang, Jos Vrancken and Paul Seidel

Abstract The common traffic sensors, such as loops,

cameras and radars, are very expensive and difficult to

maintain. Recently, Bluetooth detectors are hot alterna-

tive sensors with relative low cost to provide reliable traf-

fic information. In this paper, we will present travel time

measurements by using Bluetooth detectors on freeways

and an algorithm to calculate the average travel time.

The test has been done at the Dutch traffic management

system company Trinite Automation B.V. together withtraffic control center in Belgium. The average travel time

from the Bluetooth data has been roughly compared

with the loop detectors data. For both short routes and

long routes, results are rather good compared with the

Bluetooth test on urban road.

I. INTRODUCTION

In many countries, the road traffic is the

most important but also the most problematic

form of transportation. It includes problems like

frequent congestion, high pollution and extremely

high fatality rates in comparison with other

transportation forms. In most countries, it is

common to apply traffic management to improve

capacity of the road infrastructures and reduce

the negative effects of road traffic. Traffic data

collection from road side equipments is essential

for traffic management, more accurate traffic

information we know, better traffic control we

can perform. The common traffic data detectors

are loops, cameras with license plate recognition

and radars, however they have either high costsor high maintenance costs. GSM and GPS signals

are also good sources to collect traffic data,

Yubin Wang is with Trinite Automation B.V., Postbus 189, 1420

AD Uithoorn, The Netherlands and the Faculty of Technology,

Policy and Management, Delft University of Technology, 2600 GA

Delft, The Netherlands y.wang@trinite.nl

Jos Vrancken is with the Faculty of Technology, Policy and

Management, Delft University of Technology, 2600 GA Delft, The

Netherlands j.l.m.vrancken@tudelft.nl

Paul Seidel is with Trinite Automation B.V., Postbus 189, 1420

AD Uithoorn, The Netherlands p.seidel@trinite.nl

however the data is not sufficient to know traffic

state. Thus, recently a new type of detector

by using the bluetooth signal can contribute to

solving the high costs and the high maintenance

costs problems. Bluetooth detectors are attractive

alternative equipments to collect traffic data with

relatively low cost.

Elsewhere Bluetooth is also becoming very

popular in traffic research and in operational

systems, both for in-vehicle driver support and for

roadside traffic data collection [1], [2], [3]. The

data collected are mostly travel times and travel

time variance, but also dynamic OD matrices are

estimated on the basis of Bluetooth data.

There are many advantages in using bluetooth

detectors to measure traffic data.

High detect rate: Nowadays, almost everyone

has a mobile phone with a Bluetooth de-

vice. Each Bluetooth device has a worldwide

unique address; this address is detectable.

Approximately 20 to 30 percentage of road

users can be detected by using the bluetooth

detector, so that we can receive guaranteed,

accurate travel information.

Very cheap: A Bluetooth receiver only costs

about 10 Euro. We can plug it in a mini-

computer, then it can be used to collect traffic

data. Complete unit cost is much cheaper than

the loop detectors and cameras if deployed on

a large scale.

Relative low maintenance costs: The device is

very simple to install and it is also very easy

to replace.

Sufficient travel information of traffic state:

Travel time of individual road users are mea-

sured, thus the received traffic information is

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not fragmented and it can represent the real

traffic state.

There is a privacy problem with Bluetooth, but it

can be handled by storing data in an anonymous

way.

With so many advantages, Bluetooth detectors areused to measure travel time on freeway in stead

of cameras or induction loops. In this paper, we

will present travel time measurements by using

Bluetooth detectors and an algorithm to calculate

the average travel time based on the measurements.

The test in this paper was done at the Dutch

traffic management system company Trinite

Automation B.V. together with traffic control

center in Belgium.

I I . TEST IN BELGIUM

In order to verify the accuracy of Bluetooth

detectors for travel time measurements on freeway,

a test in Belgium was done from 26-08-2010 till

07-10-2010 on the freeway E313 (A13) between

Geel and Antwerp. Four Bluetooth detectors are

placed near ramps (See Figure 1). Figure 2 shows

how the Bluetooth detector looks like in this test.

Fig. 2. Bluetooth detector

Total distance between point A and point D

is around 36 km with a free flow travel time of 23

minutes. Total distance divided by the maximum

velocity is the free flow travel time. Table I shows

the distance and free flow travel time between

four points.

Detectors Distance (km) Free flow travel time (min)

A-B 22 14

B-C 5 3

C-D 9 9

TABLE I

DISTANCE AND FREE FLOW TRAVEL TIME

The work principle of the Bluetooth detectors has

been explained in many papers [1], [2], [3]. In the

paper, we just give a short summary. We receive

a list of detections with Bluetooth IDs for each

measurement point every minute. Then we try to

match the Ids at two measurement points. Once a

match is found, travel time for the road user can

be calculated. Figure 3 and Figure 4 show the

measured travel time for each road user by usingBluetooth detectors. For the long route, many

road users with high travel time are measured.

This is due to the fact that there are gas stations

in the middle of the route. Road users might stop

at the gas stations and continue their trips after

some while. There are not too many data with

high travel time for the short route, since there

are no gas stations or entry and exit points in the

middle of the short route.

Another interesting fact is that 3 to 10 roadusers are measured every minute for the long

route and 8 to 20 road users are measured every

minute for the short route. The reason is that there

is no way out for the short route, so the road

users detected at the starting point of the route

are also detected at the end of the route mostly.

However, the road users might exit in the middle

of the long route. Thus, the detect rate for the

short route is higher than the long routes.

III. ALGORITHM: AVERAGE TRAVEL TIMECALCULATION

As we discussed in the previous section, there

are many hits with high travel time which are

outliers. Thus, in order to calculate the correct

travel time, we need to remove the outliers. In

this paper, a simple algorithm is used to remove

outliers and a moving average algorithm is used

to calculate the average travel time.

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Fig. 1. Four Bluetooth detectors

00:00 06:00 12:00 18:00 00:000

1000

2000

3000

4000

5000

6000

Time

TravelTime(Second)

Bluetooth tracing

Measured travel time

Average travel time

Fig. 3. Travel time from point A to point B

Input detections are stored in an input table

until a configured max-time (for example 2 hours)

is reached. The configured max-time can boundthe measured travel time and remove outliers.

Output detections are looked up in the input table

at the moment they are received. If a match is

found, the difference in time is stored in a match

table. If the time is negative, it is not stored in

the match table. Every minute, the match table of

recent 10 minutes is analyzed. First, outliers are

further removed by a simple algorithm. All hits

that are more than 50 percent above the average

are removed form the table. As long as there

are values above this threshold, the average is

re-calculated and the high values will be removed.Then, the average travel time is calculated by

a moving average algorithm. The average travel

time for each minute is the mean value of the

recent 10 minutes measured travel time.

IV. RESULTS AND ANALYSIS

The average travel time is shown on the above

figures. From the figures, we can see that there

are the two lines on the long routes. It might be

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00:00 06:00 12:00 18:00 00:000

100

200

300

400

500

600

700

800

900

1000

Time

TravelTime(Second)

Bluetooth tracing

Measured travel time

Average travel time

Fig. 4. Travel time from point B to point C

Fig. 5. Loop detector data

due to the fact that there are two lanes on theroutes, cars on the left lane are driving faster than

cars on the right lane. For the long route, the

travel time goes higher to 1.5 times of free flow

travel time around 7:00 AM. For the short route,

the travel time goes higher to 4 times of free flow

travel time around 7:00 AM and around 11:30 AM.

While travel time is measured by Bluetooth

detectors, traffic information for the same period

is also collected by loop detectors. Figure 5shows the data from point A to point D. X axis

is the time, y axis is the space between point

A and point D. The color represents velocity.

However, it is difficult to calculate the realized

travel time by using loop the data. Thus, travel

time is calculated by using the length divided

by the average velocity. From figure 5, we can

see that the velocity drops dramatically around

7:00 AM for the long route and both around

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7:00 AM and 11:30 AM for the short route. The

traffic jam causes the long travel time around

7:00 AM for the long route and around 7:00

AM, 11:30 AM for the short route. The rest is

free flow. Roughly, travel time which measured

by Bluetooth detectors is close to the travel time

which measured by loop detectors.

There are some peaks for the average travel

time of the long route in the beginning and

the end of the day. The peaks are not correct

average travel time by comparing the travel time

which measured by loop detectors. The reason is

that there are too little Bluetooth measurements,

thus the outliers can not be removed properly.

However, there is no peak for the average travel

time of the short route in the beginning and

the end of the day. It is due to the fact thatthere are relative more hits per minute for the

short route, thus the outliers can be removed easily.

Compared with the Bluetooth test on urban

roads [4], travel time measured by Bluetooth

detectors is much reliable for both short routes

and long routes on a freeway. The reason is

that the distance between on-ramp and off-ramp

is relatively large. Thus, it is relatively easy

to remove the outliers, since the chance of

go-and-return cars within a short time is low.

V. CONCLUSIONS

In this paper, we present travel time measure-

ments by using Bluetooth detectors on freeways

and the algorithm to calculate the average travel

time, developed at Trinite Automation B.V. The

average travel time measured by Bluetooth detec-

tors, has been roughly compared with traffic time

which measured by the loop detectors. For both

the short route (5 km) and the long route (>10km),results are quite good compared with the Bluetooth

test on urban road. Thus, Bluetooth detectors are a

cheap and reliable alternative sensors on freeways.

VI. FUTURE WORKS

Since it is difficult to compare realized travel

time by using the loop data and Bluetooth

data, we will use GPS-based Floating Car data

to compare with Bluetooth data in the future.

A better filtering method will be used to

remove the outliers.

In the case there are too little hits, a different

approach will be used to calculate the average

travel time.

Reliability of the average travel time based on

number of hits will be considered. Predicted travel time based on the realized

travel time will be calcuated.

VII. ACKNOWLEDGMENTS

The research leading to these results has sup-

ported by Trinite Automation B.V. and received

funding from the European Communitys Seventh

Framework Programme (FP7/2007-2013) under

grant agreement no. INFSO-ICT-223844 (project

Con4Coord, a.k.a. C4C).

REFERENCES

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annual meeting of the Transport Research Board 2010, pages

117, January 2010.

[2] Ali Haghani, Masoud Hamedi, Kaveh Farokhi Sadabadi, Stan-

ley Young, and Philip Tarnoff. Freeway travel time ground

truth data collection using bluetooth sensors. In Proceedings

of the 89th annual meeting of the Transport Research Board

2010, pages 122, January 2010.

[3] Yegor Malinovskiy, Yao-Jan Wu, Yinhai Wang, and Un-Kun

Lee. Field experiments on bluetooth-based travel time data

collection. In Proceedings of the 89th annual meeting of the

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[4] Henk J. van Zuylen, Li Jie, and Lu Shoufeng. The use of

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