22nd ITS World Congress, Bordeaux, France, 5–9 October 2015

Paper number ITS-2498

Preliminary analysis on resting behavior of expressway users
with ETC data
Shoichi Hirai1*, Jian Xing1, Masato Kobayashi2, 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 Transportation Lab Inc., Japan
3. Kyoto University, Japan

Abstract
This paper describes the preliminary analysis on the resting behavior of expressway users byusing electric toll collection (ETC) data. For the sake of the impact assessment of various
traffic management schemes, we have been developing a mesoscopic traffic simulator which
covers whole inter-urban expressway network in Japan. The simulator takes account of the
dynamic route choice behavior of drivers and, for the further step, is expected to model theresting behavior during their trips. For this purpose, ETC data of which penetration is almost
90% is one of valuable data sources. In this draft, the procedure of the analysis to estimate theresting duration from ETC data is outlined with its result. Some findings highlighting thedistinct behaviors to be considered in the modeling are discussed.
Keywords: ETC data, resting behavior, inter-urban expressway service area.

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 todevelop a network traffic simulator dealing with traffic flow dynamics and driver behaviors.
From the seeds side, road operators are now motivated to utilize ETC data collected everyPreliminary analysis on resting behavior of expressway users with ETC datatime 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 theaccuracy 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 conventional studies[4] are found in this topic and mostly they are based on asurveillance data at a certain service area or on a questionnaire survey. It seems insufficientthrough those approaches to analyze the resting behavior relating with trip context and traffic
conditions.
In this paper, the procedure of to estimate total resting duration from ETC data is outlinedwith its estimated results. Some findings highlighting the distinct behaviors to be consideredin the modeling are discussed.

Feature of ETC Data from the Viewpoint of Resting Behavior Analysis
The ETC trip data used in the study includes encrypted ID, vehicle type of toll payment,
expressway entrance and exit interchange, and their entering and exit time. Figure 1 describes
the relationship of travel time, driving time and total rest time spent at service/parking areas.
Here, travel time is calculated from ETC trip data and driving time is estimated fromvehicular detectors that are installed at a reasonable spacing. In such a way, total rest time of aparticular trip is predicted by the difference between travel time and driving time.

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Preliminary analysis on resting behavior of expressway users with ETC data

（Estimated from ETC data）①Travel time
Enter

Exit

②Driving time

③Rest time

（Estimated from veh. detectors）（③＝①－②）

Figure 1 – Travel time, driving time and rest time of a trip on expressways
Though probe data giving continuous location and time every 200m can be used to predictdetailed information of driver’s resting behavior such as when and where and how long avehicle makes a rest at service or parking areas and number of rests, the penetration rate of the
ETC 2.0 on-board unit (OBU) is too low to be used for prediction of driver’s resting behavior
as a typical example. ETC trip data, however, are obtained from nearly 90% of theexpressway users, and it can be used to estimate total rest time although it is difficult tounderstand vehicle’s detailed resting behavior from the ETC data. With the accumulation of
microscopic probe data, total rest time estimated from the ETC data can be used to check theaccuracy of microscopic models formulated from probe data.
Analysis of Expressway Trips Based on ETC Data
The objective of this chapter is to understand actual situation about expressway trips such
number of trips, trip distribution and travel time distribution and so on from the analysis of
the ETC trip data, and also to make clear subjects in estimation of rest time. The data used foranalysis is all the ETC data using the expressway network operated by all the three interurban
expressway operators (NEXCO-East, Central and West) in one month in November of 2012.
Two vehicle types, i.e. passenger car (PC) and heavy vehicle (HV) and weekdays andholidays judged by the expressway entering time were taken for trip analysis. A trip covering
neighboring months was not included in the analysis. It is assumed in the study that a
particular interchange pair always takes the shortest path.
Characteristics of Interchange-paired Trips
Table 1 describes trip related indices such as total monthly number of trips, average daily
number of trips and also avg. trip distance, for weekdays and holidays and vehicle type. It isseen that more trips are made on holidays rather than weekdays. Similarly, heavy vehicles
travel longer distance than passenger cars. In particular, passenger cars make more trips on
holidays than on weekdays, whereas heavy vehicles take more trips on weekdays rather thanon holidays. The average percentage of heavy vehicles is 23.5% on weekdays and 11.5% on
3Preliminary analysis on resting behavior of expressway users with ETC dataholidays.
Table 2 shows the trip distributions for each vehicle type and weekdays and holidays. The
percentage of trip distance below 50km is 71% on weekdays and 59% on holidays forpassenger cars and 54-56 for heavy vehicles. The 85 percentile trip distance is approximately
80km on weekdays and 110km on holidays for passenger cars, and 140-150km for heavy
vehicles. On the other hand, the trips of distance over 100km in which resting behavior is
expected only account for 10.5% on weekdays and 18.3% on holidays for passenger cars, and23-24% for heavy vehicles. The long trips of distance over 500km, however, takes up lessthan 1% for passenger cars and 3% for heavy vehicles; and there are a few long trips over
1,000km.
Table 3 shows as an example the relationship between trip distance and number of trips forpassenger cars on weekdays. The trip distance of the interchange pairs of more than 1,000
vehicles per day is below 50km. The trips of distance over 100km in which resting behavior is
expected only account for less than 1%.
Therefore, it is concluded from the viewpoint of resting behavior analysis that only a small
number of trips are long distance trip during which resting behavior is expected. Rare trips
account for a majority of trips and they are difficult to be analyzed from the sample size pointof view.
Table 1 – Expressway trip related indices by weekdays/holidays and vehicle type
Trip indicesWeekdays(PC)

Weekdays(HV)holidays(PC)holidays(HV)

Total monthly number oftrips (x1000)

51,085.7

15,931.5

25,991.3

3,364.4

Avg. daily number of trips(x1000)2,432.7758.6

2,887.9

373.8

Total vehicle kilometers
traveled (million veh-km)

2,487.9

1,397.7

1,678.3

303.8

Avg. trip distance (km)

48.70

87.73

64.57

90.30

4

Preliminary analysis on resting behavior of expressway users with ETC data

Table 2 – Trip distribution by weekdays/holidays and vehicle type
Trip distance

Weekdays (PC)

Weekdays (HV)

holidays (PC)holidays (HV)

- 10km

11.0%

8.1%

7.7%

9.7%

- 20km

32.8%

24.1%

24.8%

23.5%

- 50km

70.8%

55.6%

59.1%

54.5%

- 100km

89.5%

77.4%

81.7%

76.0%

- 200km

97.1%

89.2%

94.6%

88.7%

- 300km

98.9%

93.4%

98.0%

93.3%

- 500km

99.8%

97.7%

99.7%

97.7%

- 1,000km

99.99%

99.9%

99.99%

99.8%- 1,500km

99.9999%

99.998%99.9999%

99.996%

Table 3 – Relationship between trip distance and number of trips (Weekdays, PC)

No. of trips

Interchange-paired distance- 10km- 20km- 50km- 100km- 200km- 200km1 car/month

8

10

24

237

3345

42386

- 1 car/day

7

19

225

2195

18821

64110

- 10 car/day

152

178

1165

6348

15366

9850

- 100 car/day

550

840

3634

5604

3279

839

- 1,000 car/day

330

908

2142

907

204

32

1,000 - car/day

60

89

100

28

2

0

Travel Time Distribution
Travel time depends on vehicle type, day of week, expressway entry and exit time, purpose of
travel, etc. Figure 2 shows as an example the relationship between expressway entering time
and travel time for trips from Tokyo to Hamamatsu on weekdays for heavy vehicles. It can beseen from the figure that
 Heavy vehicles entering expressways late at night;
 There is variation in travel time. Considering driving speed of 80km/h, over 2 hour
total rest time would lead to more than5 hour travel time.

5

Travel time (h)Preliminary analysis on resting behavior of expressway users with ETC data

Exit timeat midnight

12
11
10
9
8
7
6
5
4
3
2
1
0

Drive at 80km/h
0

2

4

6

8

10 12
Entry time

14

16

18

20

22

24

Figure 2 – Relationship between expressway entry time and travel time
(Tokyo – Hamamatsu of Tomei Expressway, Weekdays, Heavy vehicles)
Analysis on Total Resting Time of Expressway Users with ETC Trip Data
Outline of the Analysis
The ETC trip data and the traffic detector data used for the analysis are collected on wholeinter-urban expressways operated by the NEXCO. More than 126M ETC trips as shown in
Table 4 were used for the analysis.
Table 4 – Number of ETC trip data in November 2012 [1000 trips/month]
Weekdays
Holidays
Total
Passenger cars (PC)67,937

34,132102,070

Heavy vehicles (HV)

19,958

4,277

24,234

Total

87,89538,409126,304

The total resting time of each trip is estimated by subtracting the presumed running time fromthe travel time on the expressway. The presumed running time is calculated by accumulating
the travel time along the time-space trajectory running at the average speed for each detector
coverage section, as shown in Figure 3. In the case there are couple of possible paths for thetrip, let us assume it takes the shortest distance path.

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Preliminary analysis on resting behavior of expressway users with ETC data

Distance
Goal

Detector
coveragesection5 min.
Start

Time
Running time
(time-slicing cumulative travel time)

Figure 3 – Time slicing cumulative travel timeDistribution of the Estimated Resting Time
Figure 4 is the histogram of the estimated resting times of the HV trips of which distance is320-330 km and exiting expressway during 6-7 AM on holidays. There are unneglectable
numbers of negative resting times which come from the faster running speeds than the
average. It is considered that there will be similar number of ‘without-resting’ trips with theslower running speeds in the positive side of the histogram. As those slower ‘without-resting’trips are hard to be distinguished from the ‘with-resting’ trips, we should be careful to discuss
the resting time shorter than 30 minutes.
There are not so many but remarkable numbers of resting time longer than couple of hours,
sometimes more than one day. Those longer resting times would be spared for the sleeping
during nighttime or the adjustment of exit time from the expressway, and should be treatedseparating from the majority of ‘normal’ resting times less than couple of hours.
To know the adequate range for such long resting times, let us focus on the shape of the
cumulative relative appearance curve, the red line, in Figure 4. From the visual inspection, thecurve less than 120 minutes seems to have the sigmoid shape and the curve more than 120
minute looks like linear slope. Similar feature in the curve shape can be found in the otherdistance range, let us hereby set the criterion for the long resting time at 120 minutes.

7Preliminary analysis on resting behavior of expressway users with ETC data

long time
resting

1.0

40

0.8

30

0.6

20

0.4

10

0.2

0

0.0
720

0

120

240

360

480

600

Cumulative rate

Number of trips

50

normalresting

Resting time [minutes]
Figure 4 – Histogram of the estimated resting times(Holidays/HV/320-330km/exit during 6-7AM)
A Study on the Long Resting Behaviour
Figure 5 draws the colour contour map with 85 percentile of resting time for every 1 hour ofthe exit times and 10 km of the trip distance. The colour of the cell closer to red means thehigher ratio of the long resting time for each cell. The pattern of the red cells which
concentrate from 4 to 12AM attracts attention and it implies us two incentives to the exit time
adjustment as follows.
The first incentive is to have toll discount for late night travels. In those days, the drivers whostay on the expressway during 23PM – 4AM could get the discount to the half of normal toll.
We may recognize the concentration of the long resting times at around 23PM to 0AM andmore than 300km, especially for heavy vehicles. This concentration would be the result ofexit time adjustment of the drivers who could reach the destination before 23PM but took rest
for a couple of hours. The longer distance trips would take enough margin to the uncertaintyof their travel times and consequently had to take longer resting time for the adjustment.

8Preliminary analysis on resting behavior of expressway users with ETC data

PC - weekday

PC - holiday
180

Trip distance [km]

500

120
60
0

0

12

24 0

HV - weekday

1224

0 [min]

HV - holiday
180

Trip distance [km]

500

120
60
0

0
12
24 0
12
24
Exit time from expressway Exit time from expressway

0 [min]Figure 5 – Contour map with 85 percentile of resting times
The second incentive would come from the restriction of business opening hours. The reverse
triangular shape of the red cells has the sloping edge in the morning hours. This distinctive
feature can be explained as shown in Figure 6, which overlays the frequency distributions of
the entry and the exit times of PC-weekday trips of which distances are 300-310 km and ofwhich resting times are more than 120 minutes onto the color contour map of PC-weekday in
Figure 5. The exit time distribution has the morning peak at around 7AM.
As the expected travel time without resting will be about 3 hours at the cruising speed 100kph,
those trips exiting at around 7AM peak may enter the expressway at latest before 4AM, andconsequently can have the toll discount. If only the first incentive is affecting, there is lessattraction for drivers to shift their departure time earlier, and we may expect the similar peakat around 4AM in the entry time distribution.
However, such peak in the entry time distribution is found at around 11PM, 5 hours earlier
than the expectation. This may suggest the restrictions on travel schedule by the businesshours, meaning many travelers have to leave their origin places before the business closing
9Preliminary analysis on resting behavior of expressway users with ETC datatime but cannot reach the destinations before the business opening time because of theunavailability of their business facilities.
There is another remarkable peak at around 3AM in the entry time distribution. This peak can
be explained by the early departure time shift of the travelers who are motivated to have thetoll discount.

Distance todestination [km]Toll discountperiodExit time frequency

0

100

5%
freq.

200
Entry time frequency
300
4pm

8pm

0am

4am

8am

0pm

Time
4pm of day

Figure 6 – The entry and the exit time restriction by the business hours
A Study on the Normal Resting Behaviour
The next focus moves on the ‘normal’ resting behaviour within 120 minutes which would bespent for repose, having meal, buying souvenirs, etc. Figure 7 shows the contour map of the
proportion of the resting time more than or equal to 60 minutes and less than 120 minutes.
Similarly to the early morning pattern in Figure xx, we may recognize the second reverse
triangular shape with light blue to green colour in the early afternoon, and in case of
PC-holiday, the third triangle can be found in the evening. Those patterns appearing at mealtime reveal the resting behaviours for lunch and dinner. The reverse triangular shape may
come from the reason that the longer trips tending to run far from their destination at mealtime have more necessity to have meal at that time.

10Preliminary analysis on resting behavior of expressway users with ETC data

PC - weekday

PC - holiday
100%≥ 60 min.

Trip distance [km]

500

0

Lunch

0

12

24 0

HV - weekday

Lunch

Dinner12

24

HV - holiday

500

100%
≥ 60 min.

Trip distance [km]

0%

0

Lunch

Lunch

0
12
24 0
12
24
Exit time from expressway Exit time from expressway

0%

Figure 7 – Contour map of the proportion of the resting time > 60 min.
Conclusions and Future Works
In this paper, we have analyzed the resting behavior with the ETC trip data and the detector
speed data. The study on the long resting time more than 2 hours suggests two incentives tohave toll discount in the midnight and to postpone the exit time until business opening time.
Another study reveals that the normal resting times less than 2 hours concentrate at aroundmeal time. Our future works will identify the macroscopic model to predict total resting timeon expressway.
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.

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Preliminary analysis on resting behavior of expressway users with ETC data

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. Proceedings of the 32nd Annual
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 onintercity expressways, Proceedings of 33rd Conference of Japan Society of Traffic Engineers (inJapanese).
4. Matsushita, T., T. Kumagaya, Y. Nonaka, and T. Ishida (2011). A basic analysis of expressway
service area choice behavior. Proceedings of Infrastructure Planning and Management, Vol.44，（CD-ROM）(in Japanese).

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