OBSERVATION OF TRAVEL BEHAVIOR BY IC CARD DATA
AND APPLICATION TO TRANSPORTATION PLANNING
T. Fuse a, *, K. Makimura b, T. Nakamura b
a
Dept. of Civil Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-8656, Japan, fuse@civil.t.u-tokyo.ac.jp
b
Transport Research Div., The Institute of Behavioral Sciences, Ichigaya Honmuracho 2-9, Shinjuku-ku, Tokyo,
162-0845 - (kmakimura, tnakamura)@ibs.or.jp
Commission IV, ICWG II/IV
KEY WORDS: IC Card, Travel Behavior Observation, GIS, Transportation Planning, ITS
ABSTRACT:
So far, there have been many attempts to observe travel behavior of people in detail for transportation planning and ITS. One of the
promising tools is IC card carried by people. The IC cards as bus tickets (bus smart cards) were introduced in March 2007 in Kanto
area in Japan, and the bus smart cards have been in widespread use. The records of the bus smart card data are more than 38 million
a month, and can represent the travel behavior of people. In this study, system of the bus smart card data collection in Japan is
introduced, and the enormous bus smart card data is visualized for grasping the current transportation situation in the area of
interest. In order to establish application of the bus smart card data to transportation planning, plausible analysis methods with the
detailed travel behavior of people acquired by the bus smart card data are developed. For utilization of the bus smart card data, a
method of converting bus smart card data to travel time and bus trip data is developed. The converted empirical data is applied to
origin-destination analysis, and average of bus travel time estimation. By using the average bus travel time, relationship between the
travel time and traffic congestion are analyzed. Additionally, an integration method of the bus smart card data with probe data is
investigated. The integrated empirical data is applied to traffic monitoring, and extraction of obstructive factor for traffic flow.
1. INTRODUCTION
So far, there have been many attempts to observe travel
behavior of people in detail for transportation planning and
Intelligent Transportation System (ITS). One of the promising
tools is IC card carried by people. The IC cards as bus tickets
(bus smart cards) were introduced in March 2007 in Kanto area
in Japan, and the bus smart cards have been in widespread use.
The number of people who use the bus smart cards is around 20
million per month, and the records of the bus smart card data
are more than 38 million per month, and can represent the travel
behavior of people.
The IC card data of public transportation have been applied to
transportation planning studies. Studies on the IC card data are
under development. The examples of the studies are
understanding of transport phenomena, utilization for traffic
survey, estimation of traffic demand, and so on.
For understanding of transport phenomena, a data mining
method for public transport user behavior was developed with
2.5 million people’s IC card data (4 months). According to the
mining method, the user behavior patterns were divided into 4
groups (Agard et.al., 2006).
was proposed, and the method was applied to characteristics
analysis of public transport user in Otawa (Chu et.al., 2009).
In the estimation of traffic demand, origin-destination data (OD
data) is used. For creating the data, IC card data can be used. By
integrating household travel surveys results and IC card data,
user characteristics in various routs and time, and OD
distribution characteristics were analyzed. Through the analysis,
applicapability of the IC card data to traffic demand estimation
were discussed (Trépanier et.al., 2009). IC card data also were
used to monitor trend of transportation use. The IC card data
were compared with traffic surveys (index of passenger miles
and unlinked trips etc.) by New York City Department of
Transportation (Reddy et.al., 2009)
Moreover, IC card data were used for analysis of bus transfer
characteristics in West Yorkshire (Bagchi and White, 2004).
Not only bus transfer but also multi-modal transfer (from
subway to bus, from bus to subway, and from bus to bus) were
analyzed in London, and then number of trips of public
transportation and rate of transfer were estimated (Seaborn
et.al., 2009).
Thus, studies concerning of IC card data has started, the number
of analysis with the IC card data can be expected to increase.
IC card data can be utilized as a means to traffic survey. A new
traffic survey method combining interview and IC card data
A special joint symposium of ISPRS Technical Commission IV & AutoCarto
in conjunction with
ASPRS/CaGIS 2010 Fall Specialty Conference
November 15-19, 2010 Orlando, Florida
In this study, system of the bus smart card data collection in
Japan is introduced, and the enormous bus smart card data is
visualized for grasping the current transportation situation in the
area of interest.
2. BUS SMART CARD DATA
2.1 Utilization Trends of Bus Smart Cards
Private railroad and bus companies in the Tokyo metropolitan
area introduced smart cards in March 2007. The smart cards can
be used in both private railroads and buses. There were 23
private railroad companies and 31 bus companies (74 service
offices and 4500 vehicles), which introduced the smart cards,
and 15 million cards have been published (April 2010).
When a bus smart card is touched to an in-vehicle reader (the
timing at getting on/off), card ID and time are recorded. The
time is considered as the passage time of the bus stop. By using
the passage time, the travel time between the bus stops can be
calculated (Figure 2). In the case of multiple passengers, the
time of the first passenger’s data is considered as arrival time
and the time of the last passenger’s data is considered as
departure time. Since stopping time can be calculated from the
arrival and departure time, the stopping time is excluded from
the travel time. Additionally, the travel time between bus stops
can be converted to travel time between intersections with
travel route data and digital road maps to make transportation
analysis easier. By counting the number of the bus smart cards,
number of passengers can be acquired.
bus stop
Figure 1 shows the number of card holders, times of card usage,
and total times of card usage. The times of cards usage were
totally 6.6 million times (0.2 million times a day) at start period.
In 2010, the number of card holders is still increasing, there are
2 million times of card usage per day on average, 40 million
times per month.
ス停①
バス停②
バス停④
バス停③
バス停④
乗車時刻：7:05
time 7:05
乗車時刻：
7:12
time 7:12
乗車時刻：
7:17
過時刻は，
車券に
た利用者
時刻
time 7:17
乗車時刻：
7:12
time 7:12
乗車時刻：7:21
time 7:21
IC#card users
カード利用者数
I#total of card usage
C カード延べ利用回数
日平均利用回数
#average card usage per day
50
194.8
2.0
40
1.6
147.8
117.2
30
1.2
91.1
20
0.8
#average card usage (MM/day)
#card users, #total of card usage (MM/month)
150.9
39.7
10 24.8
month
year
0.4
4月 5月 6月 7月 8月 9月 10月 11月 12月 1月 2月 3月 4月 5月 6月 7月 8月 9月 10月 11月 12月 1月 2月 3月 4月 5月 6月 7月 8月 9月 10月 11月 12月 1月 2月
2007年
2008年
2009年
passage
7:21 travel time: 4min
7:32
7:12 travel time: 5min
7:17 travel time: 4min
時刻 7:05 travel time: 7min
所要時間：
7分
所要時間：5分
所要時間：
4分
所要時間：4分
利用者数：
1人
time
利用者数：3人
利用者数：
4人
利用者数：3人
#passengers: 1 #passengers: 3 #passengers: 4 #passengers: 3 Figure 2. Travel time and number of passengers
using bus smart cards
3. APPLICATIONS
The converted empirical data is applied to congestion spots
analysis and improvement planning of bus stops. The area of
interest is Saitama City, in where bus smart cards have been in
widespread use.
2010年
Figure 1. Utilization trends of the bus smart cards
3.1 Characteristics of Bus Smart Card Data
The number of passengers at/between bus stops and OD data
between bus stops can be generated from the mobile history
data in the bus smart card data.
2.2 Bus Smart Card Data
The bus smart card data consist mainly of ID, and mobile
history (getting on/off bus stops and getting on/off time), and
also include travel and stopping time of the bus, bus
information (line ID, travel route, and number of buses), bus
stop information (bus stop ID and longitude/latitude). By
combining these data, number of getting on/off people at a bus
stop, number of passengers between bus stops, OD data
between bus stops can be generated.
In order to establish application of the bus smart card data to
transportation planning, traffic monitoring, ITS, and so on,
plausible analysis methods with the detailed travel behavior of
people acquired by the bus smart card data are needed. For
utilization of the bus smart card data in analysis of
transportation planning, a method of converting bus smart card
data to bus travel time and number of passengers between bus
stops is developed.
For example, Figure 3 shows number of passengers at all bus
stops during 10 minutes in a weekday and a holiday. Figure 4
depicts time series data of number of passengers in a day. These
figures can help to understand the traffic situation.
Additionally, the relationship between frequency of bus use and
weather can be analyzed using the mobile history. Generally
speaking, people who walk or use bicycles in fine days shift to
use buses in rainy days. By using data for a one month period
(October 2009), the relationships were analyzed. Figure 5
shows the relationship between number of passengers and
precipitation amount. Especially, the numbers of passengers are
superior in rainy days. Figure 6 shows composition ratio of
frequency of bus use in fine and rainy days. In weekday, ratio
of low frequency users increases from fine day to rainy day. It
implies that many people use bus only rainy day. On the other
A special joint symposium of ISPRS Technical Commission IV & AutoCarto
in conjunction with
ASPRS/CaGIS 2010 Fall Specialty Conference
November 15-19, 2010 Orlando, Florida
hand, in holiday, the frequency use is almost same between fine
and rainy day. It is considered that many people use bus in only
holiday. Since the ratio of captive and choice can be understood
through this analysis, the result will be expected to contribute
analysis of relationship between fare and frequency.
25000
20000
15000
10000
5000
20時台
21時台
22時台
21時台
22時台
18時台
19時台
17時台
18時台
20時台
16時台
17時台
15時台
14時台
13時台
12時台
11時台
9時台
10時台
8時台
7時台
16時台
Grasping congestion spot from the view of public transport with
objective data is required to determine priority level of road
maintenance and improvement. The bus smart card is suitable
for the request.
6時台
5時台
0
3.2 Congestion Spot Analysis
(a) Weekday
25000
20000
15000
10000
5000
19時台
15時台
14時台
13時台
12時台
11時台
10時台
9時台
8時台
7時台
6時台
5時台
0
(b) Holiday
Figure 4. Time series data of number of passengers
Bus traveling speeds are overlaid to ordinary congestion spots
(Figure 7). The ordinary congestion spots are determined by
private car traveling speed. Figure 8 shows congestion time at
intersections (above) and bus traveling speed on average at
ICカードによる総乗客数
ICカード
による総乗客数precipitation amount
#passengers
#passengers
50
Saturday
土曜日
Sunday
日曜日
休日
0.4
40
0.3
30
0.2
20
0.1
10
0
10/1
10/2
10/3
10/4
10/5
10/6
10/7
10/8
10/9
10/10
10/11
10/12
10/13
10/14
10/15
10/16
10/17
10/18
10/19
10/20
10/21
10/22
10/23
10/24
10/25
10/26
10/27
10/28
10/29
10/30
10/31
0
Figure 5. Relationship between number of passengers
and precipitation amount
times/month
41回～/
～
#passengers
36～40回
31～35回
26～30回
(b) Holiday
Figure 3. Number of passengers
21～25回
16～20回
11～15回
6～10回
1～5回/
10/19（月）10/26（月）
雨
晴れ
fine
rainy
平日
weekday
10/3（土） 10/10（土）
雨
晴れ
fine
rainy
休日
holiday
Figure 6. Relationship between frequency use and weather
A special joint symposium of ISPRS Technical Commission IV & AutoCarto
in conjunction with
ASPRS/CaGIS 2010 Fall Specialty Conference
November 15-19, 2010 Orlando, Florida
precipitation amount (mm/day)
#passengers (MM/day)
(a) Weekday
降水量
0.5
12
180
intersections (below). The bus traveling speed was calculated
by using average speed in a day during 11 day in each
intersection. The ranks of congestion intersections are quite
deferent between ranks based on private car data and bus smart
card data. By considering the bus smart car data, more
appropriate maintenance planning in the sense of public
transport can be conducted.
congestion time
(min/3h)
120
60
0
0
1
10
20
30
40
50
60
69
rank of
intersections
5
3.3 Improvement Planning of Bus Stops
10
Road maintenance and improvement have been restricted
because of financial difficulties in Japan. In such situation,
public mobility improvement became important policy issue.
Public mobility is affected by bus stop construction and
maintenance. For example, inadequate bus bay raise problem of
traveling performance degradation, because congestion occurs
after stopping bus. When such type of bus stops is extracted by
using bus smart card data, more effective and plausible
selection of bus stop to be constructed and maintained among
many bus stops can be conducted.
15
20
bus travelling speed
(km/h)
25
Figure 8. Rank of congestion intersections,
congestion time, and bus traveling speed
武蔵野学院前
大門
県立養護学校
北原橋
念仏橋
尾間木
浅間 下
駒方
尾間木公民館
緑区役所入口
花月
北原山
原山三丁目
本太坂下
原山
本太小学校
入口
本太坂上
大崎園芸植物園
direction
For the application, an integration method of the bus smart card
data with probe data was investigated. The integrated empirical
data was applied to traffic monitoring, and extraction of
obstructive factor for traffic flow.
東仲町
浦和駅東口
（km/h） 50 ＜＜＜---------40
congestion
H20渋滞ポイント
time (min.)
平日
渋滞発生時間
30
over 150
150分以上
120～150分
120‐150
90～120分
90‐120
60～ 90分
60‐90
60分未満
bus
private
20
10
distance between 0
bus stops（m）→
平日渋滞多発箇所
bus stop number
53
24
533974
533964
60
北区
59
48
49 66
32
大宮区 50
西区
533975
533965
見沼区
37
56
40
14 5
16 20 8
13
大宮駅
46
42
26
64
65
23
54
31
58
浦和区 63
29
17
30 28
61 15 52 18 44
1112
3
7
55
6
2 桜区
45
浦和駅
9
47 67
41
68
中央区
10
51
4
240
2
270
3
500
4
338
5
235
6
631
7
576
8
288
9
407
10
393
11
754
12
550
13
217 249
14
15
16
403
409
17
33
62岩槻区
1
19
34
25
432
Figure 9. Comparison between traveling speed
of bus and private car
22
21
43
1
緑区
35
36
27
57
69
Main route in Saitama City were set as area of interest. Because
the probe data of private car is organized digital road map links,
the data were converted to the data based on bus stop links. In
Figure 9, red line denotes bus traveling speed and blue one
private cars. On the link between bus stop number 7 and 8,
traveling speed of private cars are much slower than one of bus.
This means that the congestion occurs after stopping bus
because of inadequate bus bay.
38
南区
Figure 7. Bus traveling speed and congestion spots
4. CONCLUSIONS
This study proposes use of bus smart card data for observation
of travel behavior and applications of transportation planning.
To be specific, characteristics the bus smart card data are
investigated, and then relationships between traveling time and
traffic congestion, and integrated data of the bus smart card data
with prove data are analyzed. Through the application to
Saitama City data, the significance of the proposal are
confirmed.
A special joint symposium of ISPRS Technical Commission IV & AutoCarto
in conjunction with
ASPRS/CaGIS 2010 Fall Specialty Conference
November 15-19, 2010 Orlando, Florida
18
However, the bus smart card data is just sampling data. All
passengers cannot be tracked with the data. Additionally,
attribution of the people like ages cannot be acquired. These
points are limitation of the data.
As a further work, bus stop construction and maintenance
planning will be continued through improvement of the analysis
method with additional data. Finally, the proposed method will
be reflected to actual policy decision stage.
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Agard, B., Morency, C., and Trépanier, M., 2006. Mining
public transport user behaviour from smart card data.
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Chu, K. K., Chapleau, R. and Trépanier, M., 2009. DriverAssisted Bus Interview (DABI): Passive transit travel survey
using smart card automatic fare collection system and its
applications. Proceeding of 88th Annual Meeting of the
Transportation Research Board, CD-ROM.
Trépanier, M., Morency, C. and Blanchette, C., 2009.
Enhancing household travel surveys using smart card data?.
Proceeding of 88th Annual Meeting of the Transportation
Research Board, CD-ROM.
Reddy, A., A., Kumar, S., Bashmakov, V. and Rudenko, S.,
2009. Application of entry-only Automated Fare Collection
(AFC) system data to infer ridership, rider destinations,
unlinked trips, and passenger miles. Proceeding of 88th Annual
Meeting of the Transportation Research Board, CD-ROM.
Bagchi, M. and White, P. R., 2004. What role for smart-card
data from bus systems?. Municipal Engineer 157, pp.39-46.
Seaborn, C., Wilson, N. H. and Attanucci, J., 2009. Using
smart card fare payment data to analyze multi-modal public
transport journeys (London, UK). Proceeding of 88th Annual
Meeting of the Transportation Research Board, CD-ROM.
A special joint symposium of ISPRS Technical Commission IV & AutoCarto
in conjunction with
ASPRS/CaGIS 2010 Fall Specialty Conference
November 15-19, 2010 Orlando, Florida