Proceedings of 10th ITS World Congress, Madrid, 17-21, November, 2003

A METHODOLOGY TO IDENTIFY TRAFFIC
CONDITION USING INTELLIGENT PROBE
VEHICLES
Majid Sarvi 1),Ryota Horiguchi 2), Masao Kuwahara 3) ,Yukiharu Shimizu 4),
Akinori Sato 5), Yasuhiro Sugisaki 6)1) Ph.D., i-Transport Lab. Co., Ltd., 2-12-404, Ageba-cho, Shinjuku-ku, Tokyo, 162-0824, Japan,
Email: majid.sarvi@eng.monash.edu.au, Tel/Fax: +81-3-5261-3077
2) Ph.D., i-Transport Lab. Co., Ltd., 2-12-404, Ageba-cho, Shinjuku-ku, Tokyo, 162-0824,Japan, Email: horiguchi@i-transportlab.jp, Tel/Fax: +81-3-5261-3077
3) Professor, Kuwahara Lab., Institute of Industrial science, University of Tokyo, 4-6-1 Komaba,Meguro-ku, Tokyo 153-8505, Japan Tel: +81-3-5452-6418, Fax: +81-3-5452-6420,
kuwahara@nishi.iis.u-tokyo.ac.jp
4) NEC Corporation, 7-1, Shiba 5-chome, Minato-ku, Tokyo 108-8001, Japan
Tel:+81-3-3798-0364, Email: y-shimizu@dc.jp.nec.com
5) NEC Corporation, 7-1, Shiba 5-chome, Minato-ku, Tokyo 108-8001, Japan
Tel:+81-3-3798-0364, Email: a-satou@cw.jp.nec.com6) NEC Soft Ltd., 1-18-6, Shinkiba, Koto-ku, Tokyo 136-8608, Japan
Tel:+81-3-5569-3333, Email: sugisaki@mxs.nes.nec.co.jp

ABSTRACTThe traffic conditions of the arterial routes in addition to the whole network are vital monitoring
criteria for the traffic management center in order to efficiently guide and administer the trafficnetworks. The main objective of this work is to establish a general methodology to identify
different traffic conditions in the traffic network using intelligent probe vehicles. For thispurpose we utilized Internet Protocol probe cars (IPCar) in Yokohama and Nagoya cities.
Yokohama and Nagoya IPCar projects use different type of vehicles consisting of taxis and buses.

INTRODUCTION
The main objective of this work is to establish a general methodology to identify the different
traffic conditions in the network using intelligent probe vehicles. The traffic conditions of thearterial routes as well as the whole network are vital monitoring criteria for traffic management
centers in order to efficiently guide and administer the traffic networks. There have been large
ranges of traffic monitoring equipments being utilized to observe traffic situation. Several

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Proceedings of 10th ITS World Congress, Madrid, 17-21, November, 2003

intelligent vehicle-highway system demonstration projects are currently assessing the feasibilityof using probe vehicles to collect real-time traffic data for advanced traffic management andinformation systems. However, most of the developed probe algorithm might be suffered fromthe cost of data transmission due to the richness of the probe information or the high frequencyof data acquisition. There could be some efficient strategies to minimize the transmission cost if
one be able to identify traffic conditions such as ‘congested’ or ‘free-flow’ from the time-space
trajectory of probe vehicles. For instance, we may select only the ‘congested’ record to betransmitted from a probe vehicle to the data center, when one is interested to observe the traffic
congestion conditions.
The aim of this project is to explore feasible real time application of Internet Protocol probe cars
(IPCar) date either as a stand alone data source or with other data sources such as detector counts
and automatic vehicle identification (AVI) travel time. The initial focus of this work is toestablish a general methodology to identify the different traffic conditions in the traffic network
using intelligent probe vehicles and would later extend it to provide other information such as
traffic volume and consequently emission level. Traffic conditions heritably have dynamicnature. Therefore, this study utilized the extended arterial routes and links in order to capture
traffic condition alterations and more specifically shockwave generations and dispersions under
different traffic conditions. For this purpose sets of data capturing a wide range of informationwere collected using IPCars in Yokohama and Nagoya cities. A sophisticated and innovative
method has been developed and employed to cleanse the IPCar data and searches for trip ends.
Then this data is used together with the shape of vehicles trajectories to classify and distinguishdifferent traffic condition patterns.

IPCAR SYSTEMThe Yokohama IPCar project uses 179 vehicles consisting of 140 taxis and 39 buses. The
experiment ran for 11 days in December 2001 while the Nagoya IPCar consisting of 47 taxis ranfor two days. The IPCar system is equipped with a GPS and a data logger. GPS collects
position data at regular intervals. However, the IPCar system does not store the vehicle positionat regular intervals. Instead it logs the state of events as either short stop (SS) or short trip (ST)(see Figure 1). The definition of a short stop is when the vehicle speed drops below 3 km/h.
When the vehicle speed increases above 3 km/h, the event is considered as a short trip. In otherwords, instead of a time based data logging, the equipment is an event based data logging. Soevery time the event changes from SS to ST and vice versa, the GPS position and time stamp arerecorded plus the event flag (eg. SS or ST). This approach reduces the amount of data stored andtherefore less data transmission, without sacrificing the quality of the data (Horiguchi, 2002).
Note that there is a maximum time limit of 30 seconds for a ST event in case of Yokohama
IPCar and a maximum distance of 300 meters in case of Nagoya IPCar. Therefore, if a probevehicle is moving for 2 minutes, four consecutive ST events will be recorded. However, there isno time limit for SS event. These event records are temporary buffered in the on-board
equipment and transmitted by the polling request from the IPCar data centre. The interval oftransmission is normally 1-5 minutes. In addition, the IPCar system records other parallel events2

Proceedings of 10th ITS World Congress, Madrid, 17-21, November, 2003

information such as the status of the left and right blinkers, the hazard light and the parking brake(see Figure 1).
There are instances where data are not recorded ie. contains gaps. This could be due tocommunication or GPS errors. GPS errors might occur when a probe vehicle passes under an
infrastructure such as tunnel, or when in the vicinity of elevated structures, the so called “urbancanyon”. Gap could also occur when the engine is switched off because no data will be recorded.

Short trip (ST) and Short
stop (SS) events

SSST
ShortTripSS

ST( )

SS
Short
Stop( )GAPST

SSTimeLeft Blinker

Right Blinker
Parking Brake

Hazard Light

Figure 1 Data collected by the IP Car system

DATA CLEANSING
Before the probe data can be employed to distinguish traffic patterns, the data needs to becleansed because probe data is a continuous trajectory (see Figure 2) and also there are gaps inthe data. Therefore, the data cleansing process for the OD analysis is to cut the “continuous”
trajectories into trip ends by detecting the following events.
••
•
•
•
•

Gap with parking brake event,Long gap,
Gap with unrealistic speed,Long stop,
Short stop with hazard light, andU-turn.

The data cleansing process starts by considering gaps in the data in step 1 to step 3. It thensearches for stops which are trip ends in steps 4 to 6. Details of each step are explained inreference 2.
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Proceedings of 10th ITS World Congress, Madrid, 17-21, November, 2003

Figure 2 Continuous trajectory of one probe vehicle including gaps

EVENT ANALYSISGeneral Concept
As discussed in preceding sections, the outcome of the data cleansing for each IPCar is a seriesof origin to destination trip which could happen during free or alternatively congested flow. On
the other hand each trip itself consists of a sequence of ST and SS. Therefore, it would be very
valuable to investigate the sequence of these ST and SS, in order to distinguish different patternsof these sequences under diverse traffic conditions.
A typical trajectory of an IPCar trip which consists of several sequences of ST and SS isdemonstrated in Figure 3. It shows that for instance, a long ST with relatively high speed might
follow by a short SS which is in a magnitude of a red time at a typical intersection. Conversely, along SS follow by a short ST could be a representative of proceed of an IPCar in the congestedflow. However, merely considering the speed of ST may lead to unrealistic interpretation oftraffic flow condition due to the fact that the average observed speed in urban area is less than
60km/hr. Therefore, the following three characteristics are selected in order to more accurately
distinguish different traffic regimes.

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Proceedings of 10th ITS World Congress, Madrid, 17-21, November, 2003

1. ST duration in second
2. SS duration in second
3. Adjusted speed (Average speed over the period of one sequence of ST and SS).
This average speed is calculated by dividing the distance during a ST to the total time passed
during that ST and its subsequent SS as it is demonstrated in Figure 3 by blue and pink arrows.
Distancecongested flow region

Sequence of ST+SSSS

STF ree flow region

TimeFigure 3 A typical time space trajectory of an IPCar

Traffic Pattern Classification
In this research, an extensive observation was carried out in one of the main arterial at Nagoya
city (Fushimi-Sakae arterial) in order to have a better understanding of the event sequence.
According to this observation periods of the day that have congested or free flows are established.
Consequently, the sequence analysis of IPCars is divided to congestion and non-congestion
periods and the result is shown in Figure 4. As expected it exhibits that the duration of ST islonger during non-congested periods and the maximum speed of IPCars is less than 70km/hr. In
addition, high speed takes place with short durations. Nevertheless, sequence of ST and SS
would be a much better representative of the traffic conditions as discussed earlier. Figures 5and 6 demonstrate the subsequent ST and SS of IPCar number 12 at Nagoya for an aggregatedperiod of one day divided for congested and non-congested time respectively. Each point onthese contours shows the density or the probability that one ST and its following SS with specificcharacteristic occurs. Additionally, Figure 5 clearly illustrates two completely distinguishedpatterns. One with SS less than 40 second and the other with SS longer than 40 second and ST
speed of higher magnitude. On the other hand Figure 6 suggests that longer SS and shorter ST
distance (distance travelled by an IPCar during ST) happens during congested flow while shorter
SS duration and longer ST distance occurs during free flow. These various patterns evidently
demonstrate different driving characteristics resulted from diverse traffic conditions. To have5Proceedings of 10th ITS World Congress, Madrid, 17-21, November, 2003

enhanced understanding and to clarify and classify these various traffic patterns further analysis
has been carried out utilizing more aggregated IPCar data for Yokohama and Nagoya. Some of
the results are shown in Figures 7-a and 7-b.

Fushimi-sakae arterial roadNagoya city
6050

Free time40

congested time

30201000100200300

ST duration (sec)Figure 4 Preliminary analysis of IPCars in Nagoya

Spd72
Spd64

Frequency

Spd56

Spd40
Spd32
Spd24
Spd16

ST speed (km/hr)

Spd48

70-80
60-70
50-60
40-50
30-40
20-30
10-20
0-10

180160

140

120

10080

604020

Spd80

ST speed (km/hr)

70

Spd0

SS duration (sec)Figure 5 Contour of IPCar number 12 for one day, aggregated data6

Proceedings of 10th ITS World Congress, Madrid, 17-21, November, 2003

Congestion time

45-50
D320

40-45
35-40

D240

180160140120100806040

200D160

ST distance(m)

30-35
25-30

D80

20-25
15-20

D0

10-15
5-100-5

SS duration (sec)-5-0Free time
45-50
40-45

D320

35-40

D240
D160

180160

140

120

10080

60

40

200D80
D0

ST distance (m)

30-35
25-30
20-25
15-20
10-15
5-10
0-5

SS duration (sec)-5-0Figure 6 Contour of IPCar number 12during one day for congested and free flow conditions

Figures 7-a and 7-b show five IPCars aggregated data running in Yokohama. Moreover, thisanalysis is represented in different time of the day to capture potential diversity among morningpeak, off peak and afternoon peak traffic regimes.

7Proceedings of 10th ITS World Congress, Madrid, 17-21, November, 2003

5 cars, yokohama, 10:00 - 14:00
Ssp80
Ssp70

Ssp50
Ssp40
Ssp30
Ssp20

ST->SS average speed (km/hr)

Ssp60

40-44

Ssp10
0

1020

30

40

50

60

70

80

90

100

110

120

36-40
32-36
28-32
24-28
20-24
16-20
12-16
8-12
4-8
0-4

Ssp0
130

-4-0

SS duration (sec)Figure 7-a Contour of five IPCars during 10:00 am to 2:00pm in Yokohama
5 cars, yokohama, 22:00 - 3:00
Ssp80
44-48Ssp70

Ssp50
Ssp40
Ssp30
Ssp20

ST->SS average speed (km/hr)

Ssp60

Ssp10

40-44
36-40
32-36
28-32
24-28
20-24
16-20
12-16
8-12
4-80

102030

40

50

60

70

80

90

100

110120Ssp0
130

0-4-4-0

SS duration (sec)Figure 7-b Contour of five IPCars during 10:00 pm to 3:00am in Yokohama

These contours together with those data acquired from trial in Nagoya led us to establish thefollowing five traffic patterns which might happen under various traffic conditions. Figure 8
schematically summarized these five patterns:i.

ii.

A1 with the adjusted speed of less than 20km/hr and grater than 2/3D (D is the STdistance in meter) and SS duration less than 30 sec. A representative of this pattern would
be the pink area in Figure 7-a.
A2 with the adjusted speed of less than 20km/hr and less than 2/3D (D is the ST distancein meter) and SS duration less than 30 sec. This pattern is frequently identified during
8Proceedings of 10th ITS World Congress, Madrid, 17-21, November, 2003

congested traffic conditions. A representative of this pattern would be the yellow area inFigure 7-a.
iii.
B1 with adjusted speed less than 20km/hr and SS grater than 30sec and less than 60 sec.
This pattern also mostly occurs in traffic congestions. A representative of this pattern
would be the light green and blue area in Figure 7-a.
iv. B2 with adjusted speed less than 20km/hr and SS grater than 60sec. This patternfrequently observed during congestion, however, less often it is observed under free flow
condition as well. A representative of this pattern would be the grey area in Figure 7-a.
v. C with adjusted speed bigger than 20km/hr. This pattern solely represents the free flowcondition.
According to the above classification, A1 and A2 represent short distance travel, for instance theadvancement of an IPCar in stop and going movements during congestion at a traffic queue
whereas B1 and B2 represent long distance travel with the same adjusted speed. Nevertheless, atree analysis has been carried out in consideration of Figure 5 in order to better clarify which
pattern correctly represents congested and free flow conditions. In this analysis each IPCar
trajectory is mapped to a series of pattern A or B. Pattern A consists of a pair of ST and SS with
the SS duration less than 50sec and Pattern B with SS longer than 50 sec (e.g.
AABBABAABBAAA…). It is performed in three depth level as shown in Figure 9.
STàSS sequence adjusted speed (km/hr)

C
20 km/hr
A1

B1

A2

B2

30 sec60 sec SS duration (sec)Figure 8 Summary of five distinguished traffic patterns
Depth 1

Depth 3Depth 2AABA
A (SS<50sec)
BBAAB
A

B (SS>50sec)

Th e pu rpose of thisanalysis is to figure out
if there is any relation
between specific patternsuch as AAB or ABB andtraffic situation.

BB

Figure 9 Frequency analysis using tree probability in three depths

9Proceedings of 10th ITS World Congress, Madrid, 17-21, November, 2003

The expected output of this tree analysis would guide to the probability of event sequences. For
instance what would be the probability of AA or AB (e.g. P(AA) > P(AB)). Results are shown in
Figure 10. It showed that P(AA) > P(AB), P(AAA) > P(AAB), P(ABA) > P(ABB).

Frequency

100

two days
data

80
60
two daysdata

40
200AAABB
BAABABBBABBBAAAAAB
ABAPercentage

1204035302520151050ABBABB

Pattern

PatternFigure 10 Frequency analysis using tree probability analysis

In addition, each IPCar trajectory map can be compared with five distinguished patterns (i.e. A1,A2, B1, B2, and C). Figure 11 illustrates the couple of the trajectories of IPCar number 1082 atNagoya. The left hand side figure shows that the two sequence of pattern C, which represents afree flow regime, is changed to pattern B2 and then A2 and A1 by reduction of IPCar speed andtransition to congested traffic condition. In contrast, the right hand side figure is therepresentative of a fairly free flow condition which observed at 1:00am. This analysis based on
the extensive investigation on the IPCars trajectories of Nagoya and Yokohama confirmed thatfive recognized patterns are fairly capable of identifying the traffic condition and changing
between traffic regimes. Further application of pattern analysis has been completed to minimize
the amount of memory and time needed to transfer data to centre from an IPCar onboard
equipments. Nonetheless, is not explained here due to space limitation.
Car 1082, time: 1am
6000

5000

5000

Distance (m)Distance (m)car 1082, Time: 8pm
6000

4000
Traj
A1

3000

A2
B1

2000

A1A23000

B12000

B2C

B21000

Traj

4000

1000

C0000300

600

Time (sec)

900

1200

200

400

600

800

Time (sec)Figure 11 Trajectory analysis against mapped patterns

1010001200

Proceedings of 10th ITS World Congress, Madrid, 17-21, November, 2003

CONCLUSIONSIn this study a general methodology to identify different traffic conditions in the traffic network
utilizing intelligent probe vehicles was introduced. For this purpose IPCars running in
Yokohama and Nagoya cities were employed. A sophisticated and innovative method wasdeveloped and employed to cleanse the IPCar data and searches for trip ends. This data then wasused with the shape of vehicles trajectories to classify and distinguish different traffic condition
patterns. Five discrete patterns namely A1, A2, B1, B2 and C are introduced based on ST and SS
contours and probability analysis. Further careful analysis of mapped trajectory of IPcar together
with the proposed five patterns showed that these patterns can be applied to identify different
traffic pattern during an IPCar trip. This analysis and patterns can be further utilized toward theminimization of data transactions between IPCar and the data center.

REFERENCES
Horiguchi R. (2002). The Advantage of Event-Periodic Data Recording for Probe Vehicle
System, Proceedings of Infrastructure Planning, Vol. 26, CD-ROM, November. 2002 (inJapanese).
Chung E., Sarvi M., Murakami Y., Horiguchi R., Kuwahara M. (2003). Cleansing of probe cardata to determine trip OD. Proceedings of the 21 st ARRB international conference, Cairns,Australia.

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