23rd ITS World Congress, Melbourne, Australia, 10–14 October 2016

Paper number ITS-0327

Study of resting behavior on inter-urban expressways using
ETC 2.0 probe data
Shoichi Hirai1*, Jian Xing1 , Shin-ichiro Kai2, Ryota Horiguchi2 , Nobuhiro Uno3
1. Nippon Expressway Research Institute Co., Ltd., Japan
1-4-1 Tadao, Machida, Tokyo 194-8508 Japan
Tel:+81-42-791-1679, E-mail: s.hirai.aa@ri-nexco.co.jp
2. i-Transport Lab Co. Ltd., Japan
3. Graduate School of Engineering, Kyoto University, Japan

Abstract
Resting is a part of long drive to keep both drivers and vehicles relax and safe. The Nippon
Expressway Companies (NEXCO) which operate the inter-urban expressways in Japan
provide with two types of resting place, service areas (SA) and parking areas (PA). As for theoperator, NEXCO is interested in the smooth operation and the fully utilization of SA/PAs
through the informative guidance taking account of the drivers’ resting behavior. However,there is less knowledge on the drivers’ resting behavior during long drive and further
investigation on this topic is expected. In order to explore the resting behavior on inter-urban
expressways, we have been using ETC 2.0 probe data to extract individual resting behavior bytracking successive travel data on expressways. In this paper, we describe the procedure forthe extraction of resting behavior from ETC 2.0 probe data, followed by the preliminary
analysis of the resting behavior.

Keywords:
ETC 2.0 probe data, service area and parking area, resting behavior
Introduction
So far, we have been developing a mesoscopic traffic simulator which covers wholeinter-urban expressway network in Japan[1]. With recent developments, the network topology
is getting more complex, and traffic prediction based on statistical analysis is becoming
harder and harder since it does not take into account the drivers’ behavioral changes on the
network. Looking at the issue from the seeds and needs perspective, there would be needs toStudy of resting behavior on inter-urban expressways using ETC2.0 probe data

develop a network traffic simulator dealing with traffic flow dynamics and driver behaviors.
From the seeds side, road operators are now motivated to utilize Electronic Toll Collection(ETC) data collected every time a vehicle passes an ETC tollgate located at interchanges,
since the penetration of ETC is almost 90% of expressway users nowadays. Fully using ETC
data is expected to improve the accuracy of time-dependent O-D matrix which is mandatory
in simulation studies.
Another expectation for the ETC data utilization can be found in the demand side modeling,which is also mandatory in network context. Some studies on route choice behavior have beenachieved mainly by the expressway companies [2][3]. However, there would be other types of
behavior which has to be considered.
The resting behavior of expressway users might be one of those that need to be considered. As
the trip distances on inter-urban expressways are relatively longer than those on urban
expressways or surface streets, many drivers may have chance to take one or more rests
during their trips. From the view point of the customer satisfaction for expressway users,
smooth operation of a service area is one of major concerns of road companies. If the resting
behavior is properly modeled in the express network simulator, it will be valuable to estimate
the congestion status of a service area and may help to evaluate the effects of various
countermeasures, such as enhancement of parking lots, congestion information provision of aservice area, recommendation on future resting plan via car navigation, etc.
However, few studies[4] are found on this topic and mostly they are based on a surveillancedata at a certain service area or on a questionnaire survey. It seems insufficient through those
approaches to analyze the resting behavior relating with trip context and traffic conditions.
In this research, we aim to evaluate traffic measures regarding the use of SA/PA afterincorporating resting behavior model in the traffic simulator. In our preceding research, we
had analyzed the resting behavior on expressways using the ETC trip data and vehicle
detector data. Nowadays, almost 90% cars on the expressways in Japan equip ETC on-board
unit. However, because ETC trip data does not contain the information about time andlocation at a particular resting area unless DSRC antenna is installed, we have investigated theresting behavior such as total resting time with the ETC trip data, but we are not able toanalyze the resting behavior at each SA/PA.
In order to make clear the individual resting behavior at each SA/PA during the travel on
expressways, it is required to trace the individual car’s position successively, i.e. probe data.
In this paper, let us introduce, at first, the outline of ‘ETC 2.0’ probe data which is collectedby the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of Japan. The
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Study of resting behavior on inter-urban expressways using ETC2.0 probe dataprocedure to identify the individual rest from the successive probe data will be described next,
followed by the preliminary analysis of the resting behavior on expressways.
Outline of ETC 2.0 Probe Data
The ETC 2.0 probe data contains privacy-free individual vehicle’s location and time for every
100 or 200m approximately. However, it does not contain the data within an area 500m or sofrom a point where the engine is turned off for personal information protection. One record of
ETC 2.0 data contains the items as shown in Table-1.
Table 1- Items of ETC 2.0 probe data
Vehicle ID (privacy free)
Vehicle type and usage
Basic

GPS time

information

Trip number
Serial number in a trip
Road type (expressway, national road, etc.)

Additional
information
after map
matching

Map matching flag (success/fail)Longitude and latitude
Road section ID
Direction
Road operator code

ETC 2.0 probe data can be roughly divided into the basic information and the additional
information after map matching. Regarding the basic information, the vehicle ID (hereinafter,
simply referred as ID) corresponds to the serial product number of individual ETC 2.0
in-vehicle on-board unit but is daily scrambled with random number mask to protect theprivacy of drivers. Therefore, the individual car trips within a day can be traced by the sameID, but overnight trips cannot be traced.
The same trip number is given to the data which are successively recorded within a shortinterval of distance. As this trip number is just provided for the convenience of data handling,
let us note that the meaning of ‘trip’ is different from the conventional meaning in the context
of travel behavior analysis. The trip number may change at the long gaps in the record interval,
which may happen due to many causes such as the malfunctions in data transmission or dataprocessing, the engine off and the resting at SA/PA, of course.

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Study of resting behavior on inter-urban expressways using ETC2.0 probe data

The additional information after map matching contains the result whether the map matching
was successful or not, the position of a vehicle in longitude and latitude and the road operator
code which can be used to distinguish expressways from other road types.
Extraction of Resting Behavior from ETC 2.0 Probe Data
To analyze the resting behavior at each SA/PA, it is necessary to identify the visiting time toand the leaving time from the SA/PA for each individual probe vehicle. Figure 1 shows atime-space trajectory diagram with the ETC 2.0 probe data recorded around the Ebina SA on
the Tomei Expressway eastbound, on August 1, 2015. The black and white circles on the
trajectories mean the changing points of the ‘trip numbers’ which are shown beside the circles.
The purple dotted lines indicate the gaps more than 60 seconds breaking the continuity of thetrip number. These longer gaps seem to express the resting at SA/PA, but could appear at any
place far from SA/PAs for some reason, e.g. the slow speed traveling in traffic jam. Therefore,
let us here set up the criteria to identify the gap corresponding to the individual resting asfollows;- of which the trip numbers at both ends are different;- of which the time interval is more than 180 seconds;- of which the positions at both ends are within 500m from the center of SA/PA.

Figure 1 – Time-space trajectories around the Ebina SA on Tomei Expressway
eastbound
In order to understand the characteristics of resting behavior in conjunction with the trip
context, it is required to identify the successive trip data from its entry interchange to its
destination interchange. Unfortunately, it is not always guaranteed that the both trip ends arerecorded in the ETC 2.0 probe data, and often starts or ends on the expressways somewhere
in-between. In this paper, we had assumed the trip would start from the nearest interchange
4Study of resting behavior on inter-urban expressways using ETC2.0 probe data

beforehand at the location of the first data and would end the nearest interchange afterward at
the location of the last data.
Basic Statistics on the Identified Trips and the Resting Behavior
The procedure described above was applied to the ETC 2.0 probe data collected all over Japan
from August 1-2, 2015. The number of extracted trips was 131,329. As for the vehicle typecomposition, 129,857 trips (98.9%) were of small cars, 1,470 trips (1.1%) were of large
vehicles, and 2 trips (0.0%) were unknown. Hereafter, let us focus on the resting behavior ofsmall cars.
Distribution of Travel DistanceThe cumulative relative frequency of travel distance is shown in Figure 2. The cumulative
share of the trips of which travel distances are less than 100 km reaches to 90%, and for lessthan 200 km it reaches to 99%. The mean, 85th percentile and maximum travel distances of
trips were 47 km, 80 km and 714 km respectively. It should be noted that the another study by
the authors[5] using different data source figured out slightly longer distribution on the travel
distance on expressways, we have to remind the possibility that the travel distance estimatedhere might be shorter than the actual ones. This would come from the limitation of ETC2.0
data that as aforementioned the longer trip in nighttime could not be extracted on the whole
when it ran over the midnight.

99.0%

80%

Mean

46.8

60%

S.D.

49.5

40%

Mode

10

85th percentile

80

Max

720

20%
0%

- 10
- 20
- 30
- 40
- 50
- 60
- 70
- 80
- 90
- 100
- 110
- 120
- 130
- 140
- 150
- 160
- 170
- 180
- 190
- 200
- 210
- 220
- 230
- 240

Cumulative Relative
Frequency

100%

(km)

Figure 2 - Distribution of travel distance of small cars
Distribution of Travel Time
The cumulative relative frequency of travel time is shown in Figure 3. The cumulative shareof the trips of which travel time is less than 90 minutes reaches to 90%. The mean, 85th
percentile and maximum travel times were 38 min, 70 min and 772 min (nearly 13 hours)
respectively.

5

100%

99.0%

80%

Mean

38.4

60%

S.D.

41.6

40%

Mode

10

20%
0%

th

85 percentile

70

Max

780

- 10
- 20
- 30
- 40
- 50
- 60
- 70
- 80
- 90
- 100
- 110
- 120
- 130
- 140
- 150
- 160
- 170
- 180
- 190
- 200

Cumulative Relative
Frequency

Study of resting behavior on inter-urban expressways using ETC2.0 probe data

(Minutes)Figure 3 - Distribution of travel time of small cars

Cumulative Relative
Frequency

Distribution of the Number of Resting
The cumulative relative frequency of the number of resting is shown in Figure 4. The 18,057
trips of small car were accompanied with one resting or more, accounting for nearly 15%.
About 11% of trips took one resting and nearly 3 % took twice.
100%

99.4%
95%
90%
85%

Mean

0.2

S.D.

0.5

Mode

0

th

85 percentile

0

Max

10

80%
0

1
2
Number of Resting3

Figure 4 - Distribution of the number of resting of small cars (N=129,857)

Cumulative Relative
Frequency

Distribution of the Resting Time
The cumulative relative frequency of resting time is shown in Figure 5. The average restingtime at SA was 22.9 min, while at PA was 15.9 min. The mean, 85th percentile and maximum
resting times were 25 min, 40 min and 128 min respectively.
100%

99.0%

80%
60%
40%
20%

Mean

24.8

S.D.

18.1

Mode

10

85th percentile

40

Max

130

0%
- 10

- 20

- 30

- 40

- 50 - 60(Minutes)

- 70

- 80

- 90

Figure 5 - Distribution of resting time of small cars (N=129,857)
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Study of resting behavior on inter-urban expressways using ETC2.0 probe data

Stopping Rate at SA/PA
Table 2 shows top 10 SA/PAs which have highest stopping rate calculated with the number ofresting vehicles divided by the total number of passing and resting vehicles at the SA/PA. As
for the reference, the observation values which came from the on-site survey conducted on theholidays in 2013 by NEXCO were listed in the table. The stopping rates calculated from the
ETC 2.0 probe data are almost the same as previous observation results.
Table 2 - Stopping rate of small cars at SA/PA
Inbound
OutboundResting Passing Stopping (Observation Resting Passing Stopping (Observation
Rate
Value*)
Rate
Value*)(Cars)(Cars)(%)(%)(Cars)(Cars)(%)(%)
Ebina SA
Tomei
353
1,678
17.4
21.1
347
1,596
17.9
23.1
Dangozaka SA
Chuo
402
861
31.8
22.6
244
892
21.5
24.2
Ashigara SA
Tomei
203
1,044
16.3
19.9
262
893
22.7
21.0
Gozaisho SA Higashi-Meihan
170
776
18.0
15.0
130
988
11.6
15.9
Hasuda SA
Tohoku
116
1,122
9.4
16.6
173
886
16.3
18.4
Enakyo SA
Chuo
153
507
23.2
28.7
131
423
23.6
27.1
Tsuchiyama SA Shin-Meishin
99
554
15.2
22.6
157
469
25.1
28.5
Miyoshi PA
Kanetsu
108
920
10.5
22.2
140
895
13.5
16.2
Nishinomiya-Najio SA
Chugoku
96
1,183
7.5
11.2
147
1,132
11.5
11.7
Moriya SA
Joban
150
818
15.5
23.2
86
486
15.0
20.0
*All Vehicle Type
SA/PA
Name

Expressway
Name

Relevancy between Travel Distance and the Number of Resting
The relevancy between the travel distance and the number of resting is shown in Figure 6. It
seems that the number of resting increases with the travel distance.

300 -

9%

Travel Distance (km)250 - 300

22%

15%

200 - 250

35%
37%

12%

25%
43%

36%

100 - 150

22%

28%

26%

150 - 200

42%

27%

16%

51%

50 - 100

9% 1%

39%
25%

73%

0 - 5020%

40%

Number of Resting

2%
5%

95%
0%

5%

60%
0

1

80%
2

100%

3-

Figure 6 – Relevancy between total travel distance and number of resting (N=129,857)
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Study of resting behavior on inter-urban expressways using ETC2.0 probe data

Relevancy between Travel Distance and Total Resting Time
Figure 7 shows the share of total resting time by travel distances. It seems that resting timeincreases in proportion to the travel distance. The share of total resting times less than 60
minutes gets more than 80% in total for travel distance less than 300km, and the share fortrips of over 300km accounts for 34%.
300 -

9%

Travel Distance (km)250 - 300

27%

15%

200 - 250

30%
36%
41%

36%

100 - 150

51%

16%

73%

21%
95%

0%
0

20%
0 - 3040%
30 - 60

5%1%

11% 2% 1%

35%

0 - 507%2%1%

22%42%

50 - 1009% 4%2%33%

26%

150 - 200

8% 7%

19%

5%1%
4%1%

60%
60 - 9080%
90 - 120

100%
120 -

Total Resting Time (Minutes)

Figure 7 – Relevancy between total travel distance and total resting time (N=129,857)
Difference between the first and the second rests
Figure 8 shows the cumulative relative frequency of resting time of the first and the second
rests respectively. The both curves of the first and the second rests seem almost the same. The
mean, 85th percentile and maximum resting times of the both rests were 23 min, 40 min and
120 min respectively.
Figure 9 shows the distributions of running times from the entry of an expressway (referred as‘0’) to the first resting and from the first to the second resting. The running time before the 2nd
resting is longer than that before the 1st resting. The mean, 85th percentile and maximum
running times were 36 min, 60 min and 240 min respectively before the 1st resting, and 49
min, 80 min and 230 min respectively before the 2nd resting. These running times are almost
within 2 hours.
Similarly, Figure 10 shows the driving distance from the entry to the first resting and from thefirst to the second. The running distance before the 2nd resting is longer than that before the 1st
resting. The mean, 85th percentile and maximum running distances were 49 km, 90 km and
370 km respectively before the 1st resting, and 73 km, 120 km and 340 km respectively before
the 2nd resting. It seems that vehicles which get the first rest within 100 km accounts for 90%
approximately of trips. Those which get the second rest within 150 km also accounts for 90%
approximately.
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Study of resting behavior on inter-urban expressways using ETC2.0 probe data

Mean

1st
resting23.0

2nd
resting
23.8

S.D.

17.0

18.2

Mode

10

10

85th percentile

40

40

Max

120

120

Before
2nd
resting
49.4

80.0%
60.0%
40.0%

First Resting

- 120

- 110

- 100

- 90

- 80

- 70

- 60

- 50

- 40

- 30

0.0%

- 20

20.0%
- 10

Cumulative Relative
Frequency

100.0%(Minutes)

Second Resting

Figure 8 – Distribution of resting time (N=2,986)

99.0%

80.0%

Mean

Before
1st
resting
35.7

40.0%

S.D.

29.2

32.4

20.0%

Mode

10

40

Deperture to 1st Resting

- 120

- 110

- 100

- 90

- 80

- 70

- 60

- 50

- 40

- 30

0.0%

- 20

60.0%

- 10

Cumulative Relative
Frequency

100.0%th85 percentile

60

80

Max

240

230

Mean

Before
1st
resting
49.2

Before
2nd
resting
72.8

S.D.

42.8

49.5

Mode

10

20

th

85 percentile

90

120

Max

370

340

(Minutes)

1st to 2nd Resting

Figure 9 – Distribution of running time (N=2,986)

80.0%
60.0%
40.0%

Deperture to 1st Resting

1st to 2nd Resting

- 150

- 140

- 130

- 120

- 110

- 100

- 90

- 80

- 70

- 60

- 50

- 40

- 30

0.0%

- 20

20.0%
- 10

Cumulative Relative
Frequency

100.0%(km)

Figure 10 – Distribution of running distance (N=2,986)
Conclusion
In this paper, we have studied the procedure for the extraction of resting behavior from ETC
2.0 probe data. Preliminary analysis on the resting behavior was conducted in respect toresting time distribution, relevance between travel distances and resting times, etc. The
analysis results show that ETC 2.0 probe data could be a good traffic data for traffic stateprediction and so on although there are still several points to be improved in the future. We
can know from a limited amount of data that expressway users’ resting behavior such as the9Study of resting behavior on inter-urban expressways using ETC2.0 probe data

percentage of mainline traffic entering the service/parking areas during their trips, trip length,
travel time, resting time and running time/distance between successive rests, which will be
used to model resting behavior in the next step.
For our future works, behavioral modeling on the resting behavior with disaggregatedapproach will be tackled base on the results of this analysis. The implementation of the resting
behavioral model into traffic simulator is our future work as well.
References
1. Hirai S., J. Xing, R. Horiguchi, T. Shiraishi, M. Kobayashi (2015). Development of a
network traffic simulator for the entire inter-urban expressway network in Japan,
Transportation Research Procedia, Volume 6, pp285-296.
2. Yaginuma H., T. Ishida, T. Adachi, Y. Asakura, (2012), An empirical analysis of traffic
information provision and route choice behavior with large-scale data. In Proceedings 32nd
Conference of Japan Society of Traffic Engineers (in Japanese).
3. Ishida T., M. Fukase, H. Ishibashi, and H. Kiyomiya, (2013), Analysis of route choice
behavior on intercity expressways, In Proceedings 33rd Conference of Japan Society ofTraffic Engineers (in Japanese).
4. Matsushita, T., T. Kumagaya, Y. Nonaka, and T. Ishida (2011). A basic analysis ofexpressway service area choice behavior. In Proceedings of Infrastructure Planning, Vol.
44 (CD-ROM) (in Japanese).
5. Hirai, S., J. Xing, M. Kobayashi, R. Horiguchi, N. Uno (2015). Preliminary analysis on theresting behavior of expressway users with ETC data. In Proceedings of Infrastructure
Planning, Vol.51 (CD-ROM) (in Japanese).

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