Australasian Transport Research Forum 2017 Proceedings
27 – 29 November 2017, Auckland, New ZealandPublication website: http://www.atrf.info

Analysis of resting behaviour of inter-urban
expressway users with ETC2.0 probe data
Shoichi Hirai1, Jian Xing2, Shin-ichiro Kai3, Ryota Horiguchi3 and Nobuhiro Uno4
1

Central Nippon Expressway Company Limited

2

Nippon Expressway Research Institute Company Limited
3i Transport Lab Inc.
4

Kyoto University

Email for correspondence: xing@ri-nexco.co.jp

Abstract
To assess measures associated with rest areas, such as making rest areas more attractive
by improving commercial facilities and parking lots or providing information on congestion ofrest areas, it is necessary to analyse the behavioural changes of expressway users. Of thevarious behaviours, those at rest areas have not been studied very much so far. In this study,
we aim to develop a resting behaviour model of inter-urban expressway users, apply themodel into our expressway network traffic simulator, and evaluate the effectiveness of traffic
measures associated with rest areas and mainline, e.g. by encouraging users to take anearly break or by having drivers spend more time at rest areas when traffic is congested on
downstream mainline. This paper reports on creating a database of resting behaviours ofexpressway users using ETC2.0 probe data, and on the analysis of the resting behaviours
using the database.

1. Introduction
Three inter-urban expressway operators in Japan have carried out various measuresconcerning expressway rest areas (service area (SA) and parking area (PA)) to provide
better services: making rest areas more attractive by rearranging parking spaces; renovating
restrooms, shops, restaurants and other facilities; and providing real-time traffic information
on mainline and at rest areas to ease congestion at rest areas. It may also be possible tocontrol traffic to ease congestion on mainline by prompting users to stay at upstream rest
areas longer to avoid the congestion.
To evaluate the rest area related measures, it is needed to predict how the trip behaviours(the sequence from entry of the expressway network to exit) of users change with theapplication of the measures.
Of the trip behaviours, only a limited number of studies have been carried out on resting
behaviour, which comprises rest area selection behaviour and stay time behaviour: e.g. thestudy on short stays at rest areas by Shiino et al. (2011) and the study on how drivers selectrest areas by Matsushita et al. (2011). Since 2015, Seya et al. (2015), Mouri et al. (2015),
Yamada et al. (2015) have conducted the studies on rest area selection behaviour of
expressway users. But the behaviour of users has not been analysed systematically.
Currently, we are developing an Expressway Network Simulator (ENS) to evaluate various
traffic measures taken on expressway network (2015). The ENS is designed to reproduce
the trip behaviour of each vehicle and is equipped with a route choice behaviour model, forselecting routes depending on the traffic condition on each route, but it does not include a
resting behaviour model. By incorporating a resting behaviour model into the ENS, it will bepossible to evaluate the effectiveness of rest area related measures for each rest area and a1

ATRF 2017 Proceedings

single route, and for the entire network. In addition, we expect to improve the accuracy of the
ENS by making it possible to reproduce trip behaviour which is closer to the actual
behaviours of users.
In the study, we aim to build a resting behaviour model of inter-urban expressway users,
based on the analysis of resting behaviour using various traffic data. We also aim to apply
the model in the traffic flow simulator and use it to evaluate traffic flow improvement
measures related to rest areas for each route and for the entire expressway network. In aprevious paper (Hirai et al., 2016), the authors did a macroscopic analysis of trip behaviour
focusing on the stay time at rest areas by combining data from ETC (electronic tollcollection), used by 90 percent of expressway users, and vehicle detector data. A
macroscopic resting behaviour model (Hirai et al., 2015, 2016) was formulated from thefindings. Because ETC data provides only expressway entry and exit data and does not givethe number and location of breaks the users took, details of resting behaviour are not yet
analysed.
To analyse rest area selection behaviour and stay time behaviour, data on each vehicle that
includes route data is necessary. So, the authors used trip history data included in the probe
data of the newly introduced ETC2.0 system to analyse the actual resting behaviour. This
paper reports the findings of the basic analysis of resting behaviour.

2. Outline of ETC2.0 probe data
The so-called ETC2.0 probe data is used in the study, which is output from an ETC2.0
service becoming increasingly popular in Japan (Saji et al., 2014). It includes vehicle type,time, latitude and longitude. Data is recorded every time a vehicle moves 200 m or 45
degrees, from the point where the previous data was recorded. But data withinapproximately 500 m from where the engine was turned on or off is not recorded from theviewpoint of personal information protection. Table 1 shows items included in the ETC2.0
probe data.

Table 1: Typical trip history data items of the ETC2.0 probe data

Trip history data

Added data after
map matching

Operation ID 1
Vehicle type & use
GPS time
Trip no.
Serial no.
Road type code
Matching flag
Matching latitude, longitude
Entry/Exit node
Inbound, outbound code
Road operator code

It should be noted that, to protect personal information data, identification number allocatedto each ETC2.0 on-board equipment is shuffled every day from the viewpoint of personal
information protection, and each equipment is given a separate consecutive number, orOperation ID 1” (hereinafter ID). By gathering data for each ID, the movement of a particularvehicle for one day can be collected. Since the ID changes when the date changes and theengine is turned off, data on trips of overnight or multiple days for a vehicle is basicallydifficult to grasp from the ETC2.0 probe data.

2

ATRF 2017 Proceedings

When using the ETC2.0 data, if there is a continuity of a trajectory data in space-time
diagram collected from the same ID arranged in chronological order, the collection of thedata is referred to as a “trip” (the meaning is different from the earlier mentioned “tripbehavior”). When it is judged that, for some reason, the data discontinues, in other words if
there is a big difference in the time or distance between one recorded datum and the next,
the consecutive number is changed, and this number is called a “trip number.” In addition, aserial number is given to the data in each trip.
The added data after map matching include “matching flag” which indicates whether map
matching was successful or not, “matching latitude and longitude” which are the latitudes
and longitudes of vehicle location after map matching, and links (entry/exit nodes) of thedigital road map (DRM) and their attributions (inbound/outbound code and road operatorcode).

3. Extracting resting behaviours and generating trip
behaviour data
3.1. Grasping the characteristics of vehicle trajectories around rest
areas in time-space diagram
To understand the resting behaviour for each ID, it is necessary to identify whether the user
visited the rest area to take a break or just passed through, and also to get the information of
the rest area, time, date and the length of the stay.

Figure 1: Vehicle trajectories in time-space diagram taken near Ebina SA on the Tomei
Expressway (1 August 2015)
Yokohamamachida

Distance

C
Ebina SA

A

B

D

E

Atsugi/Ebina JCT

Change in trip number

Time
Discontinuous lineof probe data

< 40km/h< 100km/h> 100km/h

Figure 1 shows vehicle trajectories in time-space diagram for each ID taken in the inbounddirection of the Tomei Expressway around the Ebina SA, on 1 August 2015. The marks ● &○ on the trajectory indicate points where the trip number changed. The number beside themis the “trip number.” The dotted purple line indicates places where the trip number haschanged or where the “serial number” is discontinuous (interruption point).
As seen in point A and B in time-space diagram, interruption points are concentrated aroundthe Ebina SA and all intervals between recordings are more than several minutes. At
interruption point A, the trip number changed, indicating that the user stopped by a rest area

3

ATRF 2017 Proceedings

and turned the engine off. With interruption point B, the trip number remains the same, but
the serial number has changed, indicating that the user took a break without turning off theengine.
But these interruption points, as seen in point C and D, are seen regardless of location, andit was assumed that, for some reason, different trip numbers were allotted by the systemeven when the vehicle was travelling on the mainline. Especially with point D, the location isclose to the Ebina SA and it could be considered that a different number was allottedbecause the vehicle was close to a rest area. So, we cannot say for sure that the user
stopped by at a rest area just because the number changed.
With point E, the serial numbers are sometimes not consecutive like point B. But we can seethat there was an error in the map matching outside the mainline. Considering these facts,
we see that the actual data are continuous. With the current ETC2.0 data, mistakes in map
matched data are often found especially around interchanges (IC), junctions (JCT) and rest
areas. If only data that have been matched to mainline links are used, a vehicle just passing
by a rest area may be matched with the rest area link and processed by the system as aninterruption in the trip. On the contrary, sometimes even if a vehicle stops at a rest area, thedata is erroneously matched with a mainline link and, therefore, is not considered as aninterruption or a break at the rest area. From these facts, we can see that a discontinuity ofthe serial number is not sufficient to consider that the user took a break at a rest area.
Below, we will define an interruption point as “a section between two points where the timedifference is more than a fixed amount,” and will extract resting behaviours by determining
whether the interruption point is close to a rest area or not.

3.2. Extracting resting behaviours
3.2.1. Determining whether an interruption point is close to a rest area or not
In extracting resting behaviours, interruption points are used to determine whether they arelocated close to a rest area or not. As explained earlier, when a user takes a break at a rest
area, the probe data on the upstream and downstream side of the rest area is not recorded.
Therefore, we laid down a fixed radius around a rest area (rest area zone) for judging
whether a vehicle stopped at or passed by a rest area. If the rest area zone is too large, it
may include nearby ICs and JCTs, and vehicles leaving or entering the expressway may beerroneously judged as vehicles taking a break.
In this study, the radius of the rest area zone was set basically at 500m. Each zone wasadjusted so that it does not include a nearby IC or JCT. If rest areas on both directions are a
bit far from each other, the zone was set so that it includes both of them. As an example, therelation between rest area zones and interruption points are shown in Figure 2. The twointerruption points or either of them may be included in the rest area zone.

Figure 2: Relation between rest area zone and interruption points

T

Trajectory of the same vehicle ID

Plot・coordinate
・time, etc.

L1

V1 P1

・

L2

P2 V2

4

ATRF 2017 Proceedings

3.2.2. Determination of resting behaviors considering vehicle speed
Once resting behaviour is determined at a rest area, resting time is estimated from theinformation of location and time of the upstream and downstream interruption points with thefollowing formulas.

RT  T  T1  T2 
L
T1  1  3,600
V1L
T2  2  3,600
V2

(1)
(2)
(3)

where,
RT: estimated resting time (sec);
T: time span between interruption points P1 and P2 (sec);
T1: estimated running time from P1 to the centre of the rest area zone (sec);
T2: estimated running time from the centre of the rest area zone to P2 (sec);
L1: straight distance between the centre of the rest area zone to P1 (km);L2: straight distance between the centre of the rest area zone to P2 (km);V1: average vehicle speed between P1 and the immediately upstream point (km/h);
V2: average vehicle speed between P2 and the immediately downstream point (km/h);

3.3. Generating trip behaviour database
Next, we generate trip behaviour database from entry to exit of expressway network to grasp
actual trip behaviour that includes resting behaviour.

3.3.1. Generating entry data
When probe data of a vehicle does not include expressway data on the upstream side of theIC, it was judged that the vehicle entered the expressway from the IC in question, and thetime the initial data was recorded as the time that the vehicle entered the expressway. Whendata was recorded in between ICs, the immediately upstream IC was considered as the IC ofentrance, and the time the data was recorded was understood as the time of entrance.

3.3.2. Generating exit data
When expressway data on the downstream side of the IC does not exist, it was judged thatthe vehicle exited from the IC in question, and the time the last data was recorded wasconsidered as the exit time. When the data was lost in between ICs, the immediately
downstream IC was considered as the exit IC, the time of the data loss was considered as
the exit time.

3.3.3. Generating resting data
To generate resting data, resting behaviour around the rest area was determined by using
the method described in Section 3.2. The time of the final data recorded at the point ofinterruption P1 plus the running time T1 from P1 to the centre of the rest area zone wasdetermined as the vehicle’s entrance of the rest area. Similarly, the time of the initial data
recorded at the point of interruption P2 minus the running time T2 from the centre of the rest
area zone to P2 was determined as exit time. The time difference between the entrance andexit time of a rest area was defined as resting time as defined in formulas (1)-(3).

5ATRF 2017 Proceedings

4. Analysis of resting behaviours
Based on the procedures described above, the trip behaviour database made for the entire
expressway network in Japan is used to analyse the resting behaviour of expressway users.
Since the ETC2.0 probe data at the time of analysis mainly covered small cars with almost
no trucks, the target vehicle type of this paper is therefore focused on small cars.

4.1. Factors assumed to influence resting behaviours
Figure 3 shows various types of factors which are assumed to influence resting behaviours
of expressway users. Of the factors shown in the figure, travel time and distance to andarrival time at a rest area may be taken from the trip behaviour database. In addition, travel
time and distance from the rest area to his or her destination exit and attribution of the rest
area such as the type of commercial facilities and size of parking lots may also affect rest
area choice behaviour (passing/stopping) and resting time of expressway users.
Aiming to model resting behaviour, the paper analysed the actual resting behaviour,
considering the possibility of using the explanatory variables and dependent variables in theresting behaviour model by using the trip behaviour database.

Figure 3: Factors assumed to influence resting behaviour

Feature of rest area•

Day/time•••

Time of day
Day of week
Weekday/Weekend

Drive history of the day••••

OriginDrive time
Drive distanceResting time

Degree of congestion｛vacant・congested・full｝

Rest area attribution

Trip attribution

•
•
•
•

•
•

SA・PA

Vehicle type
Toll discountTraffic condition••

Traffic volume
Average speed

Commercial facility
Rest room
Size of parking area
Distance with neighbour rest area

Drive info remaining••••

Destination
Drive time
Drive distanceCongestion info

4.2. Trip travel time, number of rests and resting time per break
The trip behaviour database was generated from a nationwide trip history data collected
between 1 and 2 August 2015, using the procedures described in sections 3.2 and 3.3. The
number of trip behaviour data extracted was 105,504. As for the type of vehicles, there were
104,415 small vehicles (99%), and 1,088 large vehicles (1%). According to traffic statistics(EHRF, 2015), average daily number of vehicles using expressways in August 2015 was 5.4
million, and average penetration rate of equipped ETC2.0 on-board units was approximately
between 1% and 2%.
Figure 4 shows trip travel time distribution from entries to exits estimated from ETC2.0 probe
data and ETC trip data. The latter was obtained from electronic toll collection trip datacomprising a pair of entry and exit ICs for each vehicle and covered almost 90% of total
vehicles using expressways in Japan. Trip travel time estimated ETC2.0 probe data is a bit
smaller than that from ETC trip data because vehicle ID changed after midnight once engine

6

ATRF 2017 Proceedings

was turned off and overnight long trips were omitted in ETC2.0 probe data. Nevertheless,the two travel time distributions are more or less the same. The 85-percentile trip travel timewas 70 min. 90% travelled 90 min or under, and 772 min was the longest trip time. It should
be noted that the trip time distribution includes resting time.
The trip distance was 100 km or under for about 90% of the trips, and 99% were 200 km or
under. The maximum trip distance was 714 km. On the other hand, from an early study
conducted by the authors (Hirai et al., 2016), the cumulative ratio of the number of trips pertrip distance taken from ETC data of November 2012 shows that 81.3% of small cars
travelled less than 100 km on holidays, and 94.6% travelled less than 200 km. As can beseen, short trips were observed more in this study from ETC2.0 probe data.

Figure 4: Trip travel time distribution of trip behaviour (small cars)

Cumulative frequency

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

ETC2.0(N=104415)(N=7196732)
ETCﾃﾞｰﾀ(N=7196732)

0

30

60

90 120 150 180 210 240 270 300 330 360
Trip travel time (min)Figure 5 shows the distribution of the number of breaks during a trip. Of the 104,415 trips ofsmall cars, 84.1% did not take any breaks; 12.9% took only one break, and only about 3% of
the trips took multiple breaks during their trips. As for the resting time distribution shown in
Figure 6, the average resting time per break was 25.2 min and 85% of the trips took a breakof less than 37 min.

Number of rests

Figure 5: Distribution of number of breaks (small cars)
0
1
2
3
4
5
6
7
8
9
10
11
12

87791
13430
2282
592
217
48
30
14
8
1
N=104415

2
0

20000

40000
60000
Number of vehicles

80000

1000007

ATRF 2017 Proceedings

Figure 6: Distribution of resting time (small cars)

Cumulative frequency

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

N=21,352

0

30

60
90
120
150
180
Resting time per break (min)

210

240

The top 10 rest areas (SA/PA) where small vehicles stopped by to have a break are shownin Table 2. The rate of rest area visits is defined as the percentage of mainline traffic thatstop by a rest area. As seen in the table, maximum rate of rest area visits is as much as
about 30%.

Table 2: Rate of rest area visits (small cars)
SA/PA

Expressway

Ebina SA
Dangosaka SA
Ashigara SA
Gozaisho SA
Hasuda SA
Enakyo SA
Tuchiyama SA
Miyoshi SA
Nishinomiya-Nashio SA
Moriya SA

Tomei
Chuo
Tomei
Higashi-Meihan
TohokuChuo
Shin-Meishin
Kanetsu
Chugoku
Joban

Inbound
Outbound# of vehicles # of vehicles Rate of rest # of vehicles # of vehicles Rate of rest
visitedpassedarea visits
visitedpassedarea visits
353
1,678
17.4%
347
1,596
17.9%
402
861
31.8%
244
892
21.5%
203
1044
16.3%
262
893
22.7%
170
776
18.0%
130
988
11.6%
116
1122
9.4%
173
886
16.3%
153
507
23.2%
131
423
23.6%
99
554
15.2%
157
469
25.1%
108
920
10.5%
140
895
13.5%
96
1183
7.5%
147
1132
11.5%
150
818
15.5%
86
486
15.0%

4.3. Relations between trip distance and number of breaks and total
resting time
Next, we analyse the relations between trip distance and number of breaks and total restingtime. Figure 7 shows the relation between number of breaks and trip distance. Typically, thenumber of breaks increases as trip distance increases. On the other hand, a fraction ofvehicles have no breaks for trip distance over 200 km, and about 6% do not even have a
break for trip distance over 300 km. Therefore, it is necessary to take measures against
these long trip users driving with no break as it is well known that it is better to have a breakevery one and a half to two hours.
Figure 8 shows the relation between total resting time and trip distance. The total restingtime is proportional to trip distance. When the trip distance is under 300 km, more than 80%of the total resting time is 1 hour or under.

8

ATRF 2017 Proceedings

Figure 7: Relation between number of rests and trip distance (small cars)＞300 6%
Trip distance (km)

～300

20%

14%

～250

30%
34%

28%

22%

～200

45%

40%

32%

～150

27%
45%

46%

～100

24%

11%
18% 5%

44%

9%

70%

～50

27%
95%

0%

20%

40%

0

5%
60%

1

2

80%

100%

3回以上
N=104,415

Number of rests

Figure 8: Relation between total resting time and trip distance (small cars)

Trip distance (km)＞300 6%
～300

26%

14%

～250

30%
40%

22%

～200

8% 11%

27%

12% 4%

43%

32%

～150

19%
22%

44%

46%

～100

40%

11%

70%

～50

23%
95%

0%
0

7% 4%

16% 5%

20%40%
0 - 3030 - 60

60%
60 - 90

5%
4%

80%
100%
90 - 120
120 -Total resting time (min)N=104,415

Figure 9: Distribution of resting time (small cars)

Cumulative frequency

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

1st1回目(N=2282)
rest (N=2,282)

2ndrest (N=2,282)
2回目(N=2282)

0

30

60
90
120
Resting time (min)

150

180

Figure 10: Distribution of continuous running time (small cars)

Cumulative frequency

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

1st rest (N=2,282)
0→1回目(N=2282)
2nd rest (N=2,282)
1→2回目(N=2282)

0

30

60

90
120
Drive time (min)

150

180

9

ATRF 2017 Proceedings

4.4. Difference between 1st break and 2nd break
Figure 9 shows the cumulative frequency distribution of resting time of the 1st and 2nd breakin trip behaviours where the users took more than 2 breaks. As can be seen from the figure,the lengths of the two breaks are about the same. Figure 10 compares the drive time fromentry to 1st break and from 1st to 2nd break. We can see that the percentage of vehicles
running longer between the 1st and 2nd break is higher. On the other hand, the percentage
of vehicles running shorter between the 1st and 2nd break is smaller compared to the drivefrom entry to the 1st break. This is because the drive distance to the 1st break does notinclude the distance before entry to expressway. In both cases, the drive time is, for the mostpart, within 2 hours as usually advised for expressway users.

4.5. Difference in resting behaviour per time of day and type of rest
areas (SA/PA)
Next, we calculate the total number of vehicles stopping at a rest area to take a break andtotal resting time per time of day. To include the differences in size and available facilities at
SAs and PAs in the calculation, those for the two types of rest areas were calculated
separately. The results are shown in Figure 11 (total number of vehicles taking breaks),
Figure 12 (total resting time) and Figure 13 (mean and standard deviationσ of resting time).

Figure 11: Total number of vehicles stopping at rest areas per time of day and type of rest area(small cars)

Number of resting vehicles

1000
900
800
700

600
500
400
300
200

SA (N=11277)

100

PA (N=10075)

00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Entry time

Total resting time (h)Figure 12: Total resting time of vehicles per time of day and type of rest area (small cars)
450
400
350
300
250
200
150
SA (N=11277)
100
PA (N=10075)
50
0
0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223
Entry time10

ATRF 2017 Proceedings

Mean & σ of resting time (min)Figure 13: Mean and standard deviation of resting time for a vehicle per time of day and type ofrest area (small cars)
197
140
Mean・SA (N=11276)
Mean・PA (N=10074)
120
σ・SA (N=11276)
100
σ・PA (N=10074)
80
60
40
20
0

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223
Entry time

The total number of vehicles taking breaks at SAs and PAs had their peaks between 3 pm
and 4 pm. In the morning, that at PAs reached its peak between 10 am and 11 am. As forSAs, the peak was observed between 11 am and noon. The difference in the two types ofrest areas is assumed to be caused by the availability of restaurants and other commercial
facilities at SAs. In the afternoon, the peak for both SAs and PAs was observed between 3
pm and 4 pm.
On the other hand, the peak of total resting time was observed between 11 am and noon at
SAs and between noon and 1 pm at PAs, which shows that irrespective of the difference ofavailability of restaurants and other commercial facilities at SAs and PAs, expressway users
took a longer break for lunch than any other time of day. However, there was no muchdifference in the mean resting time between SAs and PAs. They decreased from early
midnight through to late night. The standard deviation of resting time was much larger frommidnight to noon than from noon to midnight. It also varied largely from midnight to noon.

5. Conclusions
This study used trip history data of the ETC2.0 probe data to create a database of theresting behaviours of individual vehicles, and analysed the actual resting behaviours ofexpressway users. Though the data collected only for 2 days (1 & 2 August 2015) was
analysed here, the ETC2.0 probe data is accumulated day by day in the ETC2.0 Probe Data
Utilization System, so it is possible to carry out similar studies using the probe data collected
for a longer period.
On the other hand, in the trip history data, there are some uncertainties in the timing of thedata recordings and added data. Therefore, data cleansing and improvement in the method
to determine resting behaviour may be needed for further examination.
Our future tasks are to further improve the accuracy in determining resting behaviours andconduct analysis on data taken for a longer time. In addition, we plan to develop a resting
behaviour model applying the findings of this study, which will be incorporated into an
expressway network traffic simulator developed by the authors. Thus the integrated traffic
simulator is able to evaluate effectiveness of traffic operation schemes of traffic congestion
mitigation related with mainline and rest areas.

11

ATRF 2017 Proceedings

References
1. Shino, O., N. Hibino, S. Morichi (2011). Analysis of usage of rest areas on expressway
and measures for the congestion, Proceedings of Infrastructure Planning andManagement, Vol.43 (CD-ROM) (in Japanese).
2. Matsushita, T., T. Kumagaya, Y. Nonaka, and T. Ishida (2011). A basic analysis ofexpressway service area choice behaviour, Proceedings of Infrastructure Planning andManagement, Vol.44 (CD-ROM) (in Japanese).
3. Seya, S., J.Y. Zhang, M. Chikaraishi, A. Fujiwara, T. Mukae (2015). Analysis ofexpressway SA/PA choice behaviour of commercial vehicles using digital tachographdata, Proceedings of Infrastructure Planning and Management, Vol.51 (CD-ROM) (inJapanese).
4.

Mouri, Y., H. Oka, Y. Nonaka, A. Kimura (2015). Analysis of traffic behaviours ofexpressway users using PA with commercial vehicle probe data, Proceedings of the 35th
JSTE Annual Conference (CD-ROM).

5. Yamada, T., S. Kurauchi, T. Yoshii, M. Nagai (2015). Analysis of rest area choice
behaviour of the Matsuyama Expressway users based on questionnaire survey results,
Proceedings of Infrastructure Planning and Management, Vol.52 (CD-ROM) (inJapanese).
6. 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.
7. Hirai, S., J. Xing, R. Horiguchi, N. Uno (2016). Modelling total resting time of inter-urban
expressway users based on the analysis of ET trip data, Journal of Japan Society of Civil
Engineers Ser. D3 (Infrastructure Planning and Management)，Vol. 72，No. 5, pp.I661-I-671.
8. Hirai, S., J. Xing, M. Kobayashi, R. Horiguchi, N. Uno (2015). Modelling Total Resting
Time of Inter-urban Expressway Users Based on the Analysis of ETC Trip Data,
Proceedings of Infrastructure Planning and Management, Vol.52 (CD-ROM) (inJapanese).
9. Hirai, S., J. Xing, R. Horiguchi, N. Uno (2016). Modelling total resting time of inter-urban
expressway users taking long time rest, Proceedings of Infrastructure Planning andManagement, Vol.53 (CD-ROM) (in Japanese).
10. Saji, H., Y. Tanaka, H. Okajima, H. Makino (2014). Construction of probe data utilization
system, Civil Engineering Technology Journal, No.8 Vol.56, pp.16～19.
11. EHRF (Express Highway Research Foundation) (2015). Monthly statistics of expressway
traffic (August 2015), Expressways and Automobiles Vol.58 No.11, pp.77-88.

12