Cover Page

Abstract ID

8104

Paper Title

Development and Application of GIS-based System for
Simulating Activity-Travel Patterns under Spatio-temporal
Constraints

Contact Author

Nobuaki Ohmori (author1)

Email
Phone
Fax

nobuaki@ut.t.u-tokyo.ac.jp+81-3-5841-6234+81-3-5841-8527

DEVELOPMENT AND APPLICATION OF GIS-BASED SYSTEM FOR
SIMULATING ACTIVITY-TRAVEL PATTERNS UNDER SPATIO-TEMPORALCONSRAINTS
Nobuaki OHMORI
Research Associate
Institute of Environmental Studies,
Graduate School of Frontier Sciences,
The University of Tokyo
7-3-1, Hongo, Bunkyo-ku, Tokyo,113-8656, Japan
Tel: +81-3-5841-6234
Fax: +81-3-5841-8527
E-mail: nobuaki@ut.t.u-tokyo.ac.jp

Noboru HARATA
Professor
Institute of Environmental Studies,
Graduate School of Frontier Sciences,
The University of Tokyo
7-3-1, Hongo, Bunkyo-ku, Tokyo,113-8656, Japan
Tel: +81-3-5841-6233
Fax: +81-3-5841-8527
E-mail: nhara@ut.t.u-tokyo.ac.jp

Katsutoshi OHTA
Professor
Department of Urban Engineering,
Graduate School of Engineering,
The University of Tokyo
7-3-1, Hongo, Bunkyo-ku, Tokyo,113-8656, Japan
Tel: +81-3-5841-6235
Fax: +81-3-5841-8527
E-mail: katsuohta@ut.t.u-tokyo.ac.jp
Abstract: This paper presents the development and application of GIS-based system forsimulating activity-travel patterns under spatio-temporal constraints. Input data consist of
three components: travel demand (individual characteristics, activity-travel diary withscheduled activities and travel tracking data), transportation supply (railway network) andactivity opportunities (locations and opening hours). The system was developed to apply
especially to graduate students studying urban transportation planning. Users can simulatechanges of the volume of space-time prisms and feasibility of activity-travel patterns after
changing variables such as available time budget, distance between fixed activity locations,travel speed, service hours of railway, location of opportunities, duration of target activities
and opening hours of opportunities. The system was very useful for students to understandactivity-travel behavior in urban space. The system would contribute to evaluation of
space-time accessibility measures after introducing various space-time related policy options
not only for students but also for practitioners.
Keywords: GIS, activity-travel pattern, space-time prism
1. INTRODUCTION
Understanding mechanism of individual travel behavior in urban space is essential for urban
transportation planning and policy. Conventionally, travel is considered as a derived demand
from the desire to engage in activities at certain places. Hence, understanding therelationships between travel behavior and daily activities is effective in estimating individual
and household responses to policy measures and to changes of environmental constraints. In
addition, public policy should provide individuals with a greater set of options to choose from,and it should distribute these options among the population in more equitable ways (Burns,
1979). It is therefore important to evaluate the potential to engaging in activities considering awide variety of constraints individuals face. From this view, a lot of research has been
conducted about feasibility of engaging in activities under spatio-temporal constraints, what is1

called space-time accessibility (see, e.g., Lenntorp, 1978; Burns, 1979; Kwan, 1998, Miller,1999). Especially in recent years, methodologies to analyze travel behavior in temporaldimension are necessary for evaluation of the impact of Transportation Demand Management(TDM) policies and Intelligent Transport Systems (ITS) technologies.
On the other hand, Geographic Information System (GIS) is very effective tool for
management, analysis and representation of spatial elements of transportation network andindividual travel patterns. In recent years, the development of software and the progress of
database preparation have contributed to a wide use of GIS in transportation fields (Thill,
2000; Duker and Ton, 2001). GIS has also been used for the study on activity-travel patterns
under spatio-temporal constraints (see, e.g., Segawa and Sadahiro, 1995; Kwan, 1998; Miller,1999). In this context, not only for practitioners concerning transportation planning and policy,
but also for students studying travel behavior, the ability of visual representation of travel
patterns in GIS is considered very useful for understanding travel behavior and responses topolicy options and to changes of environmental constraints.
This paper presents the development and application of GIS-based system for simulating
activity-travel patterns under spatio-temporal constraints. The system is developed to apply
especially to graduate students studying urban transportation planning. Section 2 reviews
previous studies on activity-travel patterns under spatio-temporal constraints. Section 3
describes the development of the system of which the concept is based on the theory of
space-time prism and space-time accessibility. Section 4 describes the results of application tograduate students’ exercises in a university. Section 5 concludes the study and mentions
further improvement of the system and its application fields.
2. ACTIVITY-TRAVEL PATTERNS UNDER SPATIO-TEMPORAL CONSTRAINTS
More than twenty years ago, Household Activity-Travel Simulator (HATS), essentially
gaming simulation, was used very successfully in trying to better understand household travel
decisions and the constraints within which those decisions are made (Jones et al., 1983). On
the HATS game board, spatial components of activity-travel patterns were represented on amap, and temporal components were represented on a timeline, using information on activity
diaries of all members of a household. When a policy measure was introduced, household
members discussed together considering changes of constraints on the game board, and newactivity-travel patterns were simulated in this board. This tool had originally been developed
for policy evaluation but had also been applied for practitioners and students to understand theinterrelationships between activity and travel behavior (Jones, 1982).
On the other hand, it is very important to specify the choice set of feasible activity-travel
patterns more realistically. The space-time prism constraint formalized by Hägerstrand (1970)
is a very useful idea for specifying alternative feasible activity-travel patterns in space-time.
Burns (1979) developed the concept of accessibility to space-time dimensions (space-timeaccessibility) introducing the activity duration at opportunities based on space-time prism
constraints. Lenntorp (1978) operationalized Hägerstrand’s approach by developing PESASP
model that calculated the total number of space-time paths given a specific activity program
and the urban transport network. CARLA, which was developed at TSU, is a similar model togenerate alternative activity patterns of the day based on combinatorial algorithms (Jones etal., 1983). In recent studies, Kwan (1998) and Miller (1999) operationalized space-timeaccessibility under the space-time prism on a real world network using GIS.
We have developed GIS-based gaming simulation system: SMAP (Simulation Model for
Activity Planning), which has been innovative integration of GIS and feasible activity-travel2

pattern generation model (Ohmori et al., 2001). The activity-travel pattern generation model
can enumerate feasible activity-travel patterns under spatio-temporal constraints ofindividual’s scheduled activities and opening hours of activity opportunities. With theintroduction of two persons’ activity schedule constraints, not only individual travel modes(walk, bicycle, car-driver and public transport) but also car-passenger mode availability
restricted by car-driver’s schedule constraints are explicitly dealt with in the model. Initial
application of SMAP aimed to understand constraints to affect travel behavior of the elderly
households and their responses to changes of the constraints in a local city.
3. DEVELOPMENT OF THE SYSTEM
3.1 Theory of Space-Time Prism and Space-Time Accessibility
Accessibility is an important concept in urban transportation planning and policy. In general,
only spatial components of accessibility, e.g., distance or travel time between two separate
locations, have been emphasized when evaluating accessibility-enhancing policies. However,essentially both spatial and temporal components of accessibility, which represent potential ofengaging in activities at opportunities, should be evaluated considering that travel is a derived
demand from engaging in activities. This concept is defined as space-time accessibility(Burns, 1979; Kwan, 1998; Miller, 1999). In this context, the concept of space-time prism is avery useful especially for determining individual choice set of alternative activity patterns
engaging in discretionary activities under spatio-temporal constraints. The space-time volume
of prism is also called as Potential Path Space (PPS), and projecting the PPS onto the planar
space provides the potential path area (PPA). The PPS and PPA are determined by threeelements: time budget (the amount of available time between two consecutive fixed scheduledactivities), velocity of available travel mode and distance between two locations at which
fixed activities are engaged in. When an individual uses public transport, the service hours
and timetable of public transport could also restrict the prism.
In addition, the individual can generally engage in certain activities only at the opportunities
in urban space (except for the case in which travel is the main activity, e.g., jogging, bicycling
and leisure travel). The prism delimits, both spatially and temporally, available opportunities
within the time budget. Locations and opening hours of opportunities are therefore significant
elements affecting space-time accessibility. Another element is activity duration needed forcomplete implementation of the activity. These elements are criminal variables to determine
space-time accessibility. The system developed in this paper aims to simulate the space-time
volume of prism and feasibility of engaging in discretionary activities in prisms, which
represents space-time accessibility measures, before and after changing the variables
mentioned above. Table 1 shows the six variables and possible options for increasing
space-time accessibility.
The system presented in this paper is a new version of SMAP and improved for system users
to operate by themselves. The users can simulate PPA (available opportunities with theavailable time) and feasibility of engaging in an activity in prism constraint. A distinctive
point is to use not hypothesized but real activity schedule from the users’ activity diary data,so that they can conduct simulation in more realistic situations on the basis of their daily
activity schedules and urban environment. The concept of space-time prism would be easy tounderstand, if travel velocity is assumed to be constant across space for analytical simplicity(the shape of PPA is an ellipse and that of PPS is a combination of two cones). However, in
real urban space, the shape of PPA is not simple, because it depends on transportation
network system. It may be difficult for (under) graduate students to imagine the actual PPA
they face in daily life, even if they take a lecture on travel behavior under spatio-temporal3

Table 1 The Variables Affecting Space-Time Accessibility and Possible Options for
Increasing Space-Time Accessibility
Variables
Time budget
Distance between two fixed activities
Travel velocity
Location of opportunities
Opening hours
Activity duration

Options for increasing accessibilityz Delete fixed activities or shorten the durationof fixed activities
z Relocate base locations (home, work andschool, etc.)
z More rapid travel mode is available
z Increase speed of the available modez Relocate locations of discretional activity
opportunitiesz Closer opportunities are available
z Extend opening hours of opportunities
z Increase efficiency of the activityz Reduce waiting time at the opportunity

constraints to understand the theory of prism. For the above reasons, simulation exercises
using their real activity data is considered to be useful to better understand the concept ofprism and space-time accessibility.
3.2 Details of the System Developed in the Study
MapInfo GIS software was used for the system platform. MapBasic programming software
was used for customizing MapInfo, and system users could operate the system by clicking
added menu bars. Since the previous SMAP needed several complicated procedures tooperate, an interviewer operated it and respondents answered the questions by looking at acomputer screen (Ohmoi et al., 2001). This new system was developed so that system users
could operate it themselves. We named this system SMAP-E (SMAP for Education).
Figure 1 shows the structure of SMAP-E. Input data consist of three components: travel
demand, transportation supply and activity opportunities. Travel demand side data on
individual characteristics (travel mode availability, travel cost budget and the maximum
walking time), activity-travel diary with space-time constraints of scheduled activities arenecessary for calculation of prisms. Detailed spatio-temporal travel tracking data, collected bypositioning technologies (Global Positioning System (GPS) or Personal Handyphone System(PHS)), are used for representing travel routes in GIS.
Data on spatio-temporal components of transportation network and activity opportunities areprepared in GIS. Although available mode is only railway in this study, it is enough for users,
who live in Tokyo metropolitan area and travel mainly by walk and railway, to simulate theiractivity-travel patterns. Line hole and transfer links in the railway network have attributes of
their length and the average travel speed. Travel time through the route of the minimum travel
time between two stations is calculated using the railway network. Access and egress mode
from an opportunity to railway stations is supposed to be walking. Without preparing road
network data, access and egress time is calculated from the distance between the opportunity
and the railway stations divided by walk speed (4km/h). Since the timetable of each railway
route is not considered in the study, service hours are simply set that all routes have the samehours. Opportunity data are locations and opening hours of all opportunities that the systemuser visited at during the study term.

4Input Datazzz

Travel demand side
¾ Activity schedule (activity diary and space-time
constraints of activities)¾ Individual characteristics (mode availability, travel
cost budget, the maximum walking time)¾ Travel trajectory (collected by PHS location
tracking system)
Transportation supply side
¾ Railway network
Activity opportunity side
¾ Locations and opening hours of opportunities

Changeconstraints

Calculation of PPA and the feasibility of activities

Representation of activity-travel patterns on GIS

Figure 1 The Structure of SMAP-E
SMAP-E has been designed for the spatial volume of prism (PPA) and the feasibility ofengaging in activities in PPA to be simulated and represented virtually on GIS, before andafter changes of constraints. The C program that is run from a menu bar on the customized
MapInfo implements the module calculating PPA and the feasibility of activity in a prism.
PPA and the feasibility of activity engagement are calculated according to the following
advanced space-time prism concept (see Ohmori et al., 1999; Ohmori et al., 2001). Activities
are classified into three types: activity (a) is fixed activity, activity (b) is planned but flexible
activity and activity (c) is discretionary activity. Components of individual X’s activity-travel
pattern are defined as:
The earliest start time and the latest end time of the nX-th prism of
TS nX , TE nX :X

X

individual X
Locations where the nX-th prism of individual X starts and ends

LiX , LXj :

Lk :

∑ AT

X
b ,n X

Location of opportunity k
The sum of the duration of activities (b) in which individual X:t m ,ij :engages between times TS nX and TE nX
Travel time from location i to location j by travel mode m

Tk :Activity duration that individual X engages in the target activity at

Xopportunity k
The l-th start time and end time of opening hours of opportunity k
Travel start time of individual X in the nX-th prism

OS , OE :
k
l

X

k
l

Trip _ Start _ Time nXX :TE nXX − TS n X − ∑ ATbX,n X is the time budget for engaging in activities (c) and travel for individual
X

X. Individual X can engage in the target activity at opportunity k in the nX-th prism within thetime budget TE nX − TS nX − ∑ ATbX,n , only when s/he starts traveling after TS nX by travel mode
X

X

X

X

5

m, arrives at location Lk after OSlk , engages in the activity for Tk , leaves before OElk bymode m and arrives before TE nX . If positive number of Trip _ Start _ Time nX exists subject toXX

the following equations (1) through (5), the activity engagement at opportunity k in the nX-th
prism is feasible for individual X.
(1)
Trip _ Start _ Time nX ≥ TS nX
X

X

Trip _ Start _ Time nXX + t m , LX L

≥ OSlk(2)

Trip _ Start _ Time nXX + t m, LX L

+ Tk ≤ OElk

(3)

Trip _ Start _ Time nXX + t m, LX L

+ Tk + t m, L L

i

i

i

X
TE nXX − TS n X −∑ AT

Xb ,n Xkk≤ TE nX(4)≥ tm, L L + Tk + t m, L L

(5)

k

k

X
i

X
j

k

X

k

X
j

Under this assumption, PPA consists of all the opportunities k at which the engagement in thetarget activity is feasible in a prism.
As shown in Figure 2, transparent sheets had been used and layered in HATS for drawing
activity schedule, travel pattern, and transportation network and opportunities (Jones, 1982).
Each GIS layer in SMAP-E exactly substitutes this function. Figure 3 shows the concept of
GIS data use in SMAP-E on spatio-temporal dimension.
4. APPLICATION TO GRADUATE STUDENTS EXERCISES
4.1 Schedule of the Exercise
SMAP-E had been applied to the exercise “Environmental Information Exercise in Spatial
Planning and Policy” for graduate students at the University of Tokyo in 2001 and 2002 years.
Opportunity

Travel pattern

Transportation network

Activity schedule

Figure 2 HATS Game Board (Jones, 1982)
6

Time

Travel pattern(collected by PHS)

時間

Transportation network

Urban space
都市平面
Opportunity

Activity schedule

Figure 3 The Concept of GIS Data Use in SMAP-E
The objective of this exercise was to complement the lecture “Environmental Information
System in Spatial Planning and Policy” and to better understand interrelationships between
environmental information and individual activity patterns. The schedule of the exercise is asfollows:
The 1st week : Collect travel data using GPS and PHS.
The 2nd week : Record one-week activity-travel diary in a survey sheet, and collect travel
tracking data by carrying PHS.
The 3rd week : Learn how to use MapInfo software, and represent PHS data on MapInfo.
The 4th week : Input activity-travel diary data in a Excel sheet, and prepare opportunity
data on MapInfo.
The 5th week : Represent and analyze spatial and temporal components of one-week
activity-travel pattern.
The 6th week : Submit the first report titled “My one-week activity-travel patterns.”
The 7th week : Take a lecture on individual activity-travel patterns under spatio-temporalconstraints in urban space.
th
The 8 week : Take a lecture on transportation network and activity opportunities.
The 9th week : Learn how to use SMAP-E, and simulate activity-travel patterns.
The 10th week : Submit the second report titled “Understanding activity-travel patterns
using SMAP-E.”
Seventeen students participated in the exercise in 2001 year and eleven students in 2002 year.
About half of the students had not used any GIS software before the exercise. From the 2nd
week to the 3rd week, the students recorded their one-week activity diary in a survey sheet andcarried PHS device for the purpose of automatically collecting data on spatio-temporal
movement. PEAMON (see Okamoto et al., 2000) was used in the 2001 exercise, and PHS
operated by NTT DoCoMo, Inc. was used in the 2002 exercise. Positional data were collectedfrom morning to midnight at a specific time interval during the survey period. In the 2001
exercise, travel-tracking data were collected using off-line PHS system. On the other hand,7

on-line PHS system was used in the 2002 exercise. The students were sent, by e-mail, several
maps showing travel trajectory of that day, after 23:00 every other day. PHS systems used in
the two exercises are summarized in Table 2.
Before using SMAP-E, the students were assigned to submit the first report on their one-week
activity-travel patterns (see the schedule, 6th week). This report was written on the analysis of
only travel demand side data, i.e., activity-travel diary and PHS tracking data. Then, thestudents had lecture on activity-travel behavior under spatio-temporal constraints (activity
schedule, transportation network and activity opportunities in urban area). Having basic
knowledge about prism and accessibility, the students conducted the simulation exercise using
SMAP-E and submitted the second report on activity-travel patterns under spatio-temporalconstraints.
Table 2 PHS Systems Used in the Exercises

The number of participants
PHS system
Data collection
Time IntervalTime period

2001 year

2002 year

17
PEAMON
Off-line
1 minute
From 8:00 to 22:00

11
NTT DoCoMo, Inc.
On-line5 minutes
From 7:00 to 23:00

4.2 Simulation Exercise Using SMAP-E
One-week activity diary and PHS spatio-temporal tracking data of all users were stored in theserver computer. The users were able to access these data via the intranet. The process ofoperating SMAP-E is described below. In the beginning, a user starts a MapBasic program on
MapInfo. Then, the user selects his identification number and set constraints such as mode
availability, travel cost budget and the maximum walking time (in the exercise, available
mode was only railway, and travel cost were not introduced, because database of travel fare
had not been prepared). Next, he selects a target day from seven days and sets fixed activity
scheduling constraints. As shown in Figure 4, time use of the day is represented as in-home,travel and out-of-home at the left side timeline, and travel trajectory (drawn by blue poly-line)of the day is represented on the right side map. Continuously, the C program calculates PPA
for all prisms in the day. He can identify a PPA on a map component and simulate the PPA
after changing constraints. Actually, railway stations within PPA are displayed andcolor-coded by the available time at the station (in Figure 4, red indicates 0-30 minutes,
yellow 30-60 and green 60-90) as a representative of available opportunities. Furthermore, hecan test the feasibility of engaging in a target activity at a specific site (one of his visited
opportunities during the survey period) within a prism (as shown in Figure 5).
As mentioned previously, the objective of the exercise was to help students to understand theimportant elements directly affecting space-time accessibility. The students were assigned to
conduct the following simulation exercises ((1) to (8) correspond to Figure 6):z Compare the volume of prisms (locations and the number of the railway stations in a
prism, classified by the available time) at different time of day, and simulate the volume
of a prism before and after changing (1) time budget, (2) distance between fixed activity
locations, (3) velocity and (4) railway service hours.
z Simulate the feasibility of engaging in a discretionary activity in a prism before and after
changing (5) the volume of a prism, (6) activity duration, (7) location of an opportunityand (8) opening hours of the opportunity.
8

Figure 4 Representation of Activity-Travel Patterns and Available Opportunities within aSpace-Time Prism on SMAP-E

Figure 5 Representation of the Feasibility of Engaging in the Target Activity on SMAP-E
9

time

(5) the volumeof the prism(6) activity
duration(1) time budget(4) railway
service hours(8) opening hoursof the opportunity(3) velocity

space(2) distance between fixed
activity locations(7) location of
the opportunity

Figure 6 Operational Variables in the Simulation Exercises
Finally, they submitted the second report that consisted of discussion about the simulation
results and their opinion about effectiveness and improvement of SMAP-E.
4.3 Analysis of the Students’ Reports
From the students’ reports, it was found that a series of simulation using SMAP-E had
contributed to help students to understand theory of space-time prism and activity-travel
patterns under spatio-temporal constraints. In the first report, before the simulation exercises
using SMAP-E, there were descriptions about their time use and travel patterns, for example,“Travel time occupied a large amount of a day,” “Every day, I sleep very long time,” “I foundthat my time use was not efficient,” “My activity-travel patterns varied every other day,” “Ididn’t go to any other places than a university, what a boring life!” “It is the first time toidentify the travel routes of my trips.” These statements indicate that analysis of their own
one-week activity-travel patterns by themselves should be effective for looking back their
daily life. In the simulation exercise, on the other hand, the students were able to realize
spatio-temporal constraints to their activity-travel patterns by examining not only activity
schedule but also transportation network and opportunities. In the second report were anumber of descriptions that suggest usefulness of SMAP-E. Some of the interesting
comments on the simulation results are described below:
z Moving to nearer the university contributed both to decreasing travel time and increasing
the available time for discretionary activities.
z Deleting a short-time fixed activity resulted in combining two consecutive prisms andincreasing PPA considerably (see Figure 7).
z Getting up earlier enlarged a prism in the morning but did not contribute increase of theavailable time at an opportunity. This was because opening hours of the opportunity
constrained the available time in the early morning (see Figure 8).
z Compared the difference of activity type depending on the available time at an
opportunity. At a bookstore, 150 minutes are enough for me to browse and choose books,
but in 18 minutes, I can search for a book only when I have already decided what book tobuy. If only 2 minutes are available at the bookstore, I won’t be able to buy any books
10

time

time

Delete a fixed
activity

space

space

Figure 7 Combining Two Consecutive Prisms by Deleting a Fixed Activity
time

time

Wake up earlier(delete a fixed
activity)

space

space

Figure 8 The Unchanged Available Time at an Opportunity after Prism Enlargement

time

The same travel times(distances)
space

Figure 9 The Difference of the Available Time at Two Locations Reached at in the Same
Travel Timeszzunless I know where they are in the store.
Realized that I couldn’t actually change the locations or opening hours of any
opportunities, or railway speed, but the activity schedule could be coordinated, to someextent, by my discretion.
Examined the feasibility of having a lunch activity at two restaurants that located at
different places but were reached at in the same travel times, when I wondered why theavailable times differ in two. Just before submitting this report, I became aware that11

opening hours of one restaurant limited the available time in a prism (see Figure 9).
SMAP-E was found to be the very effective system for simulating activity-travel patterns
after changing major elements affecting space-time accessibility. However, further
improvement in the system could enable users more realistic and practical simulation. From
the analysis of the students’ reports, the following improvements are considered to be usefulin future:z Introduce travel modes such as walk, bicycle, car driver and bus, in addition to railway.
¾ It is important to provide individuals multi-modal alternatives in transportation
policy. Preparing and introducing road and bus network data would contribute more
realistic calculation of travel times by multi-mode.
z Consider the difference of travel time across the day and week.
¾ As for the railway, this is possible by preparing timetable database or integrating
public transport information software (for example, “Ekispert” by Val Laboratory
Corporation and “Norikae Annai” by Jourdan Co. Ltd.) with SMAP-E. Although it isdifficult to explicitly deal with travel time variability on road network, at least thedifference between peak and off-peak periods, and weekdays and weekends should
be considered.
z Prepare data on activity opportunities other than those visited during the survey period.
¾ It is also important to provide individuals alternatives of opportunities in spatial
policy. Preparing database about various activity opportunities would be useful forusers to find new alternative opportunities and activity patterns.
z Indicate the available time at the opportunity.
¾ In the present system, the available time at each station is represented as different
colors classified into four or five categories, and the judgment of the feasibility ofactivity engagement is just “yes” or “no.” There were several students who tried tochange activity duration step by step and identify the available time. Giving
information on the exact available time at opportunities would be very effective andthis function could be easily added to the system.
z Set up fixed activity schedules more flexible.
¾ Start and end times of prisms are determined by classifying each recorded activity
into three types (see section 3.2). Thus, as shown in Figures 7 and 8, increase of timebudget can be represented only by deletion of a fixed activity. Improvement of thispoint could enable users to conduct more useful simulation, for example, “What timeshould I get up to attend the early morning class in time?”
5. CONCLUSION
This paper presents the development of SMAP-E that is a GIS-based system for simulating
space-time prisms and the feasibility of engaging in activities in prisms using activity diary,railway network and opportunity data in Tokyo Metropolitan area. SMAP-E was applied tothe exercises for graduate students majoring spatial planning and policy. The students
recorded their own one-week activity diaries and simulated PPA and the feasibility ofactivities in prisms before and after changing important variables affecting space-timeaccessibility. It was found that the exercises using SMAP-E was very useful for the students
to better understand activity-travel behavior under spatio-temporal constraints. However,there is still room for improvement of SMAP-E in order to conduct more realistic andpractical simulation. This improvement concerns preparation of database on network andopportunities, and advanced techniques of programming.
In future, the GIS-based system that can simulate individual activity-travel patterns would
contribute to evaluation of space-time accessibility measures after introducing various
12space-time related policy options, such as congestion pricing and flexible working hours.
Since the change of travel cost by introduction of congestion pricing at peak periods affects
travel behavior, it would be effective for system users to investigate alternative activity-travel
patterns (alternative routes, departure times, destinations and modes) with information on
travel cost. In case of flexible working hours, because timing of work activity engagiment
depends on worker’s discretion, not only the volume and timing of prisms but space-timeaccessibility can be changed. It would be meaningful to investigate the feasibility of engaging
in discretionary activities at different working hours, when making decision about departuretime to commute. Furthermore, the system would have much advantage for tourists andbussiness travelers when planning activity schedue. Travel tracking data collected by GPS
could be also used for representation of detailed travel routes and speed profiles, and for
estimation of vehicle emissions.
ACKNOWLEDGEMENTS
The work is funded by Foundation for Fusion of Science & Technology and The Japan
Research Center for Transport Policy. Takuya Maruyama assisted in preparing the railway
network data in Tokyo Metropolitan Area. Institute of Urban Transport Planning Co., Ltd. andNTT DoCoMo, Inc. assisted us in using PHS location tracking systems.
REFERENCES
Burns, L.D. (1979) Transportation，temporal，and spatial components of accessibility.
LexingtonBooks, Lexington.
Duker, K. and Ton, T. (2001) GIS for transportation, In D.A. Hensher and K.J. Button (eds.),
Handbook of Transport Modeling. Elsevier, Oxford.
Hägerstrand, T. (1970) What about people in regional science?, Papers of the Regional
Science Association, Vol.24, 7-21.
Jones, P.M. (1982) ‘HATS’ educational manual: Studying travel in the context of household
activity patterns. Transport Studies Unit, Oxford University, Ref.193/PR.
Jones, P.M., Dix, M.C., Clarke, M.I. and Heggie, I.G. (1983) Understanding travel
behavior. Gower, Aldershot.
Kwan, M.-P. (1998) Space-time and integral measures of individual accessibility: A
comparative analysis using a point-based framework, Geographical Analysis, Vol.30, No.3，
191-216.
Lenntorp, B. (1978) A time-geographic simulation model of individual activity programmes.
In T. Carlstein, D. Parks and N. Thrift (eds.), Timing Space and Spacing Time 2, Human
Activity and Time Geography. Arnold, London.
Miller, H.J. (1999) Measuring space-time accessibility benefits within transportation
networks: Basic theory and computational procedures, Geographical Analysis, Vol.31, No.2,
187-212.
Ohmori, N., Muromachi, Y., Harata, N. and Ohta, K. (1999) A study on accessibility andgoing-out behavior of aged people considering daily activity pattern, Journal of the Eastern
Asia Society for Transportation Studies, Vol.3, No.5, 139-153.
13

Ohmori, N., Muromachi, Y., Harata, N. and Ohta, K. (2001) Simulation model for activity
planning (SMAP): GIS-based gaming simulation, Proceedings of the 9th World Conference
on Transport Research, Soul, July 2001.
Okamoto, A., Suzuki, A., Lee, Y.H., Tanabe, J. and Asakura, Y. (2000) Development andFunctional Experiment of PEAMON (Personal Activity MONitor), Proceedings ofInfrastructure Planning, Vol.23, 659-662. (in Japanese)
Segawa, S. and Sadahiro, Y. (1995) A Policy Making Support System for Child Care
Facilities using GIS, Papers and Proceedings of the Geographic Information Systems
Association, Vol.4, 59-64 (in Japanese).
Thill, J. (2000) Geographic information sysems for transportation in perspective,
Transportation Research C, Vol.8, 3-12.

14