Two Applications of GIS-Based Activity-Travel Simulators

Nobuaki OHMORI
Department of Urban Engineering
The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo
113-8656, Japan
Tel: +81-3-5841-6232
Fax: +81-3-5841-8527
E-mail: nobuaki@ut.t.u-tokyo.ac.jp
Noboru HARATA
Institute of Environmental Studies
The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo
113-8656, Japan
Tel: +81-3-5841-6233
Fax: +81-3-5841-6267
E-mail: nhara@ut.t.u-tokyo.ac.jp
andKatsutoshi OHTA
Department of Regional Development Studies
Toyo University
1-1-1 Izumino, Itakura-machi, Oura-gun, Gunma
374-0113, Japan
Tel: +81-276-82-9026
Fax: +81-276-82-9026
E-mail: kohta@itakura.toyo.ac.jp

To be presented at EIRASS workshop on Progress in Activity-Based Analysis, in Maastricht, The
Netherlands, May 28-31, 2004.

1

Abstract
This paper presents the development and applications of GIS-based activity-travel simulators. Twosystems were developed based on the existing system, the Simulation Model for Activity Planning(SMAP) developed by the authors. One application was developed especially for the purpose of
instructing students in understanding the theory of space-time prism/accessibility and travel
behavior under spatio-temporal constraints. It was used in a graduate course as an educational tool(SMAP for Education: SMAP-E). The other was developed as a decision-making support system
for activity planning using interactive surveys to collect information on the activity schedulingprocess of tourists’ leisure activities (SMAP for Leisure: SMAP-L). Both systems were
programmed with MapBasic software by customizing MapInfo GIS. In both systems, the time useof a day was represented on a timeline and the spatial component of travel pattern was represented
on a map. In addition, the feasibility of alternative activity-travel patterns was tested, based on
space-time prism constraints. A GIS database for a transportation network and activity opportunities
with some attributes was used in the systems as input. GIS applications of activity-travel simulators
were very useful not only for the decision-making support systems but also for the interactive
survey tools, which could have wide possibilities contributing to further progress in activity-based
analysis.
Keywords: GIS, activity-travel pattern, space-time prism, activity scheduling, simulation

2

1. Introduction
Understanding mechanism of individual travel behavior in urban space is essential for urban
transportation planning and policy. Conventionally, travel is considered a derived demand from thedesire to engage in activities at certain locations. Hence, understanding the relationships between
travel behavior and daily activity engagement is effective in estimating individual and household
responses to policy measures and to changes in environmental constraints. In addition, public policy
should provide individuals with a greater set of options to choose from and distribute these options
among the population in more equitable ways (Burns, 1979). It is therefore important to evaluate
the potential of engaging in activities considering the wide variety of constraints that individuals
face. From this view, extensive research has been conducted on the feasibility of engaging in
activities under spatio-temporal constraints, referred to as space-time accessibility (see, e.g.,
Lenntorp, 1978; Burns, 1979; Kwan, 1998, Miller, 1999). Especially in recent years, methodologies
to analyze travel behavior in the temporal dimension are necessary for evaluation of the impact ofTransportation Demand Management (TDM) policies and Intelligent Transport Systems (ITS)
technologies. In addition, travelers can acquire information not only on transport systems but also
activity opportunities at pre-trip and en-route from various media such as TV, websites andcar-navigation system, so travelers can make decisions about activity-travel pattern more
dynamically than before. There has been research on investigating scheduling behavior using datacollected by systems such as “CHASE” (Doherty and Miller, 1997) and “React!” (Lee and McNally,2001) which were developed to trace the process of activity scheduling. Zhou and Golledge (2004)
developed a real-time tracking system of activity scheduling/schedule execution on the platform of
Personal Digital Assistance (PDA).
Geographic Information System (GIS) is a very effective tool for the management, analysis andrepresentation of spatial elements of the transportation network and individual travel patterns. In
recent years, the development of software and the progress of database preparation have contributed
to a wide use of GIS in the transportation field (Thill, 2000; Duker and Ton, 2001). GIS has also
been used for studies on activity-travel patterns under spatio-temporal constraints (see, e.g., Segawa
and Sadahiro, 1995; Kwan, 1998; Miller, 1999). In this context, the ability to visually represent
travel patterns in GIS is considered very useful for understanding travel behavior and responses topolicy options and to changes of environmental constraints not only for practitioners involved withtransportation planning and policy, but also for students studying travel behavior. Moreover, GIS
can be used for travel surveys, showing the respondents information on travel patterns, thetransportation network and urban environment more realistically. For example, “React!” uses a
GIS-based map for collecting activity locations. Spatio-temporal travel tracking data automaticallycollected with Global Positioning System (GPS) can be also used to represent travel routes andspeed profiles. As seen more recently, the three-dimensional visualization of activity-travel patterns
using 3D-GIS (Kwan, 2003) can be more powerful to represent individual movement in space-time,
which has the possibility to make it easier to understand activity-travel behavior intuitively.
This paper presents the development and applications of GIS-based activity-travel simulators. Twosystems were developed based on the existing system, the Simulation Model for Activity Planning(SMAP), developed by the authors. One application was developed especially for the purpose of
instructing students in understanding the theory of space-time prism/accessibility and travel
behavior under spatio-temporal constraints. It was used in a graduate course as an educational tool(SMAP for Education: SMAP-E) (Ohmori et al. 2003). The other was developed as adecision-making support system for activity planning using interactive surveys to collectinformation on the activity scheduling process of tourists’ leisure activities (SMAP for Leisure:SMAP-L). Both systems were programmed with MapBasic software by customizing MapInfo GIS.
In both systems, the time use of a day was represented on a timeline and the spatial component oftravel pattern was represented on a map. In addition, the feasibility of alternative activity-travel3

patterns was tested, based on space-time prism constraints. A GIS database of the transportation
network and activity opportunities with some attributes was used in the systems.
2. GIS-based Activity-Travel Simulators
More than twenty years ago, the Household Activity-Travel Simulator (HATS) developed atTransport Studies Unit (TSU) at Oxford University, essentially a 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 componentsof activity-travel patterns were represented on a map and temporal components were represented ona timeline, using information from the activity diaries of all members of a household. When a policy
measure was introduced, household members discussed and considered the changes of constraints
on the game board and new activity-travel patterns were simulated on this board. The gaming
simulation approach was especially interested in inter-personal linkages and constraints, gaining
more realistic responses than simply asking hypothetical questions. This tool had originally beendeveloped for policy evaluation but was also used by practitioners and students to understand theinterrelationships between activity and travel behavior (Jones, 1982). Other than TSU’s study, there
have been many works based on gaming simulation or interactive stated response survey methods(for example, Phifer et al., 1980; Burnett and Hanson, 1982; Achmed et al., 1995; Lee-Gosselin andTurrentine, 1997).
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 concept for
specifying alternative feasible activity-travel patterns in space-time. Burns (1979) developed theconcept of accessibility to space-time dimensions (space-time accessibility) introducing the activity
duration at opportunities based on space-time prism constraints. Lenntorp (1978) operationalized
Hägerstrand’s approach by developing the 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 to generate alternative activity patterns of the day, basedon combinatorial algorithms (Jones et al., 1983). In recent studies, Kwan (1998) and Miller (1999)
operationalized space-time accessibility under the space-time prism on a real world network using
GIS. GISICAS (Golledge et al., 1994; Kwan, 1997) is the integration of GIS and computational
process model (CPM) for activity scheduling under space-time constraints.
The authors developed the GIS-based gaming simulation system: SMAP, as an innovative
integration of GIS and a feasible activity-travel pattern generation model (Ohmori et al., 2003).
MapInfo GIS software was used as the system platform. MapBasic programming software was used
for customizing MapInfo. Users could operate the system by clicking the added menu bars in
MapInfo. In HATS, transparent sheets were used and layered for drawing activity schedule, travel
pattern, and transportation network and opportunities (Jones, 1982). Each GIS layer in SMAP
substitutes this function. Figure 1 shows the concept of GIS data use in SMAP for the
spatio-temporal dimension. The activity-travel pattern generation model could enumerate feasible
activity-travel patterns under spatio-temporal constraints of the individual’s scheduled activities andopening hours of activity opportunities. With the introduction of two persons’ activity schedule
constraints, not only private travel modes (walk, bicycle, car-driver and public transport) but also
car-passenger mode availability restricted by car-driver’s schedule constraints was explicitly dealt
with in the model. The series of system operations were logged and saved in a text file, which madeit possible for the researcher to post-analyze the simulation results such as respondents’ answers to
the questions. The initial application of SMAP was aimed to understand constraints that affect travel
behavior of the elderly households and their responses to changes of the constraints in a local city inJapan. In particular, showing their one-week activity-travel patterns and places visited on GIS wasvery useful to examine their lifestyle.
4

Travel pattern(collected by PHS)

時間

Opening hours of theopportunity
Time
Transportation network

Activity opportunity

Urban space
都市平面
Activity schedule

Figure 1. Concept of GIS data use in GIS-based activity-travel simulator (SMAP)

Travel demand side•••

Input Data

Activity schedule (activity diary with space-time constraints)
Individual/household characteristics (e.g., mode availability, travel
cost budget, the maximum walking time)(Travel tracking data collected by positioning technologies (e.g.,
GPS, PHS)

Changeconstraints

Transportation supply side•

Travel times between two locations (calculated from road andpublic transport network)

Activity opportunity side•

Locations and opening hours of opportunities

Generate alternative activity-travel patterns and/or test the feasibility ofthe patterns based on space-time prism constraints
Represent spatio-temporal components of activity-travel patterns on GIS
Log file of the operation process is stored in the computer.

Figure 2. Basic structure of GIS-based activity-travel simulator
5The authors emphasize that such a GIS-based activity-travel simulator should be useful for mainly
two research purposes:(1) Data collection: as an enhanced interactive GIS-based travel survey tool for collecting data on
constraints, response to environmental changes and the scheduling process; and(2) Traveler’s decision-making support: as a simulation tool to analyze alternative activity patterns,
providing information on the transport system, activity opportunities, and individual andspace-time constraints.
Based on the original SMAP, two applications of GIS-based activity-travel simulator weredeveloped. Figure 2 shows the basic structure of the GIS-based activity travel simulator. Input dataconsists of travel demand (activity schedule, individual/household characteristics, and travel
tracking data), transportation supply (travel times between two locations calculated from transport
network data) and activity opportunities (location point and opening hours of activity opportunities).
Using the above information, alternative activity-travel patterns are generated and/or the feasibilityof the patterns is tested in the system based on the concept of space-time prism constraints. Then,
spatial and temporal components of activity-travel patterns are represented in GIS. By changing
constraints as input data, changes in alternative activity-travel patterns after environmental change
can be simulated. Table 1 shows a comparison of the systems. Characteristics of each system anddifferences among the systems will be explained in detail after the following sections.
3. Simulator of Space-Time Prism and Accessibility
SMAP-E is a new version of SMAP improved for system users to operate by themselves. The users
can simulate potential path area (PPA), which consist of available opportunities with the available
time and the feasibility of engaging in an activity in prism constraint. Using real activity schedules
from the users’ activity diary data enable the users to conduct simulation of more realistic situationsin urban environment. The concept of the space-time prism would be easy to understand, if travel
speed is assumed to be constant across space for analytical simplicity (the shape of PPA is anellipse and that of potential path space (PPS) is a combination of two cones). However, in real
urban space, the shape of the PPA is not simple, because it depends on the 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-temporal constraints to understandthe space-time prism theory. For the above reasons, simulation exercises using real activity diarydata is considered useful to better understand the concept of prism and space-time accessibility.
3.1 Development of SMAP-E
Since the original version of SMAP required several complicated procedures to operate, aninterviewer operated it and respondents answered the questions by looking at a computer screen. In
contrast, SMAP-E was developed so that system users could operate it by themselves. Input dataconsist of three components: travel demand, transportation supply and activity opportunities. Travel
demand side data on activity-travel diary with space-time constraints of scheduled activities arenecessary for calculation of prisms. Detailed spatio-temporal travel tracking data, collected bypositioning technologies such as Personal Handyphone System (PHS) (see Ohmori et al, 2000;
Asakura et al., 2001), are used for representing travel routes in GIS. Data on spatio-temporalcomponents of transportation network and activity opportunities are prepared in GIS. Although the
only available mode is railway with access/egress by walk in this study, it is enough for the users,
who reside in the Tokyo metropolitan area and travel mainly by walk and railway, to simulate theiractivity-travel patterns. The links in the railway network include attributes of their length and theaverage 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 torailway stations is supposed to be walking. Instead of preparing road network data, access andegress time is calculated from the distance between the opportunity and the railway stations divided
by walk speed. Since the timetable of each railway route is not considered in the study, service
6

Table 1. Comparison between the systems
SMAP
To understandactivity-travel behavior,and spatio-temporal andinter-personal constraints
to out-of-home activitiesof the elderly household.

SMAP-ETo help students
understand the theory of
space-time prism andactivity-travel behavior
under spatio-temporalconstraints

SMAP-LTo understand thescheduling process of
tourists’ leisure activities

MethodFace-to-face interview

Self completion

Face-to-face interview

Input data (activity
schedule)

One-week activity diaryof all the household
members

One-week activity diary

Pre-planned activity
schedule(transport network)

Road network
Bus network

Railway network

Road network(activity opportunity)

Opportunities which the
respondents visited
during the reporting week

Opportunities which the
respondents visited
during the reporting weekand railway stations

All the opportunities in a
tourism informationalmagazine

Study area

Local city (Akita city)

Mega city (Tokyo
metropolitan area)

Local city (Awaji-shimaIsland)Travel mode

Car driver
Car passenger
Bus with walk
access/egress
Taxi
BicycleWalk

Railway with walk
access/egress

Car

Alternative
activity-travel pattern
in a prism

One out-of-homediscretionary activity bydifferent modes, time of
day and day of the week,
based on two individuals’
prism constraints

One out-of-homediscretionary activity by
railway and different timeof day, based on an
individual’s prism
constraint

Multiple out-of-homeactivities (trip chain) by
car and different routes,
based on a party’s prism
constraint

Main study purposeof development andapplication of thesystemhours are simply set so that all routes have the same hours. Opportunity data consist of location
points and opening hours of all the activity opportunities that the system user visited at during thediary survey week.
SMAP-E is designed for the spatial volume of space-time prism (PPA) and the feasibility ofengaging in activities in the prism to be simulated and represented virtually on GIS, before and after
the 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. The PPA and thefeasibility of activity engagement are calculated according to the advanced space-time prism
concept (see Ohmori et al., 1999; Ohmori et al., 2003). Activities are classified into three types:
Activity (a) is fixed activity, Activity (b) is planned but flexible activity and Activity (c) is
discretionary activity. Under this assumption, PPA consists of all the opportunities at which theengagement in the target activity is feasible in a prism.
3.2 Application to Graduate Course7SMAP-E has been applied to the course “Environmental Information Exercise in Spatial Planningand Policy” for graduate students at the University of Tokyo in the years 2001, 2002 and 2003. The
objective of this exercise was to complement the lecture “Environmental Information System in
Spatial Planning and Policy” and to help the students to better understand interrelationships
between environmental information and individual activity patterns under space-time constraints.
The basic schedule of the exercise is as follows:
1st week : Practice spatio-temporal travel tracking data collection using GPS and PHS.
2nd week : Record one-week activity-travel diary in a survey sheet, and collect travel tracking
data by carrying PHS.
3rd week : Learn how to use MapInfo software, and represent PHS data on MapInfo.
4th week : Input activity-travel diary data in a Excel sheet, and prepare opportunity data on
MapInfo.
th
5 week : Represent and analyze spatial and temporal components of one-week activity-travel
pattern.
th
6 week : Submit the first report titled “My one-week activity-travel patterns.”
7th week : Take a lecture on individual activity-travel patterns under spatio-temporal constraints
in urban space.
8th week : Take a lecture on transportation network and activity opportunities.
9th week : Learn how to use SMAP-E, and simulate activity-travel patterns.
10th week : Submit the second report titled “Understanding activity-travel patterns using
SMAP-E.”The numbers of students who participated in the exercise were 17 in 2001 year, 11 in 2002 year and
13 in 2003 year. About half of the students had not used any GIS software before the exercise. In
the 1st week, the students traveled around Tokyo by car equipped with GPS and PHS for practice oftravel tracking data collection. From the 2nd week to the 3rd week, the students recorded their
one-week activity diary in a diary survey sheet and carried the PHS device for the purpose of
automatically collecting data on spatio-temporal movement. PEAMON (see Asakura et al., 2001)
was used in the 2001 and 2003 exercises, and PHS operated by NTT DoCoMo, Inc. (see NTT
DoCoMo website) was used in the 2002 exercise. Positional data were collected from morning tomidnight at a specific time interval during the reporting week. In the 2001 and 2003 exercises,
travel-tracking data were collected using an off-line PHS system, whereas an on-line PHS system
was used in the 2002 exercise. Several maps showing their own travel trajectory of that day weresent to the students by e-mail, after 23:00 every other day. They were asked to correct their activity
diary recorded in the diary sheet, if any, by checking their travel patterns in the map. PHS systems
used in the three exercises are summarized in Table 2.

Number of participants
PHS system
Data collection
Time IntervalTime period

Table 2. PHS systems used in the exercises
2001 year
2002 year
17
11
PEAMONNTT DoCoMo, Inc.
Off-line
On-line1 minute
5 minutes
From 8:00 to 22:00
From 7:00 to 23:00

2003 year
13
PEAMON
Off-line
2 minutes
From 8:00 to 22:00

Before using SMAP-E, the students were assigned to submit their first report on their one-week
activity-travel patterns (see the 6th week of the couse schedule). This report was written on theanalysis of only travel demand side data, i.e., activity-travel diary and PHS tracking data. Then, thestudents participated in a lecture on activity-travel patterns under spatio-temporal constraints(activity schedule, transportation network and activity opportunities in urban area). Having basic
8

knowledge about space-time prism and accessibility, the students conducted the simulation exercise
using SMAP-E and submitted the second report on activity-travel patterns under spatio-temporalconstraints.
3.3 Simulation Exercise Using SMAP-E
One-week activity diary and PHS spatio-temporal tracking data of all users were stored in the server
computer. The users were able to access the data via the intranet. The procedure of operating
SMAP-E is described below. In the beginning, a user starts a MapBasic program on MapInfo. Then,the user selects her/his identification number and sets constraints such as the maximum walking
time. Next, s/he selects a target day from seven days and sets fixed activity scheduling constraints.
As shown in Figure 3, time use of the day is represented separately by in-home, travel andout-of-home at the left side timeline. In the timeline, red colored rectangles show fixed activities inspace and time (Activity (a)), and blue colored rectangles show discretionary activities (Activity(c)) and travel. Travel trajectory of the day is represented drawn by blue poly-line on the right side
map. The C program continuously calculates PPA for all prisms in the day. The PPA can be
identified on a map component and simulate the PPA after changing constraints. Actually, railway
stations within PPA are displayed and color-coded by the available time at the station (in Figure 3,
red indicates 0–30 minutes, yellow 30–60 and green 60–90) as a representative of available
opportunities. Furthermore, the user can test the feasibility of engaging in a target activity at a
specific site (one of her/his visited opportunities during the reporting week) within a prism (asshown in Figure 4).
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 5):• 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)travel speed and (4) railway service hours.
• 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 opportunity and (8)opening hours of the opportunity.
Finally, they submitted the second report that consisted of discussion about the simulation results
and their opinion about effectiveness and recommended improvement of SMAP-E.
3.4 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 alarge amount of a day,” “Every day, I sleep for a very long time,” “I found that my time use was notefficient,” “My activity-travel patterns varied every other day,” “I didn’t go to any other places thanthe university, what a boring life!” “It is the first time I identified 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 on 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 byexamining not only activity schedule but also transportation network and opportunities. In thesecond report were a number of descriptions that suggest the usefulness of SMAP-E. Some of theinteresting comments on the simulation results are described below:
9

Railway station
outside PPA

Railwaystation within
PPA

Time use
Time budgetof the prism

Traveltrajectory by
PHS

In-home Travel Out-of-home

Activity opportunity visited
during the reporting days

The number of the station within PPA
classified by the available time

Figure 3. Representation of activity-travel patterns and available opportunities within aspace-time prism on SMAP-E

Opening
hours of theopportunity
Time budgetof the prism

The message says, “The activity engagement at the opportunity is
feasible in this prism.”

Figure 4. Representation of the feasibility of engaging in the target activity on SMAP-E
10

Space-time pathtime(5) The volumeof the prism(6) Activity
duration(1) Time budget(4) Railway
service hours(8) Opening hoursof the opportunity(3) Speedspace(2) Distance between fixed
activity locations(7) Location of
the opportunity

Figure 5. Operational variables in the simulation exercises in SMAP-E
•
•
•
•

•
•

“Moving to nearer the university contributed both to decreasing travel time and increasing theavailable time for discretionary activities.”“Deleting a short-time fixed activity resulted in combining two consecutive prisms andincreasing PPA considerably.”“Getting up earlier enlarged a prism in the morning but did not contribute to increase of theavailable time at an opportunity. This was because opening hours of the opportunity
constrained the available time in the early morning.”“Comparing 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 to buy. If only 2 minutes
are available at the bookstore, I won’t be able to buy any books unless I know where they areplaced in the store.”“I 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 some extent, by mydiscretion.”“I 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 the available times
differ in two. Just before submitting this report, I became aware that the opening hours of one
restaurant limited the available time in a prism.”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 thesystem could enable the users to do more realistic and practical simulation. From the analysis of thestudents’ reports, the following improvements are considered to be useful in the future:• Introducing travel modes such as walk, bicycle, car driver and bus, in addition to railway: It is
important to provide individuals with multi-modal alternatives in transportation policy.
Preparing and introducing road and bus network data would contribute to more realistic
calculation of travel times by multi-mode.
• Considering the difference of travel times across the day and the week: As for the railway, thisis possible by preparing timetable database or integrating public transport information software(for example, “Ekispert” by Val Laboratory Corporation and “Norikae Annai” by Jourdan Co.
11

•

•

•

Ltd.) with SMAP-E. Although it is difficult to explicitly deal with day-to-day travel time
variability on the road network, at least the difference between peak and off-peak periods, andweekdays and weekends should be considered.
Preparing data on activity opportunities other than those visited during the survey period: It isalso important to provide individuals with alternative opportunities in spatial policy. Preparing
database about various kinds of activity opportunities will be useful for the users to find newalternative opportunities and activity patterns.
Indicating the available time length at the opportunity: In the present system, the available timeat each station is represented as different colors classified into four or five categories, and thejudgment of the feasibility of activity engagement is just “yes” or “no.” There were several
students who tried to change activity duration step by step and identify the available time length.
Giving information on the exact available time at opportunities will be very effective and thisfunction can be easily added to the system.
Setting up fixed activity schedules more flexibly: Start and end times of prisms are determinedby classifying each recorded activity according to fixity in space and time. Thus, increase oftime budget can be achieved only by deletion of a fixed activity. Improvement of this point canenable the users to conduct more useful simulation, for example, “What time should I get up toattend the early morning class in time?”

4. Simulator of Leisure Activity Planning
Activity-travel scheduling is a necessary task when people travel and participate in activities.
Especially for tourists visiting unfamiliar places, not only travel information (e.g., travel time, route,toll road fee, congestion, accident point, parking places and parking fees) but also activity
information (e.g., location, opening hours, entrance fee of opportunities) is useful for traveling andparticipating in activities efficiently. Usually, tourists can get information on leisure spots (activity
opportunities) from a variety of media, such as information magazines, TV and also websites,
before leaving home for leisure travel. In recent years, when traveling by public transportation, by
inputting information on origin/destination stations and departure/arrival times, travelers can get
information on the alternative routes from computer software and websites. Car-navigation systems
can also provide information on the shortest route, congestion and accident points on real-time.
However, at the present, integrated information on transport systems and activity opportunities is
not provided to travelers. Such kind of information should be helpful for leisure activity planning.
4.1 Development of SMAP-L
SMAP-L was also developed on the platform of MapInfo GIS software with MapBasic for its
customization. The authors prepared GIS database on road network and activity opportunities in
Awaji-shima Island as an input to SMAP-L. The Digital Road Map (DRM), which is the standard
road network data in Japan, was used for preparing travel times between two locations in
Awaji-shima. Two different travel speeds were set to each link for highway and expressway. The
opportunity data were made by the authors and consisted of all the activity opportunities(amusement parks, historic spots, restaurants, etc.) found in the tourism information magazine“RURUBU Awaji-shima’02” with characteristics as activity type, location, opening hours, entrance
fee and recommended activity duration to spend at the site. Users of SMAP-L can simulate theiractivity-travel patterns by successively selecting destinations from the list of opportunities anddeciding activity duration at each destination and money spending for travel (toll road and parkingfee) and activities (entrance fee and food expenses). SMAP-L automatically searches travel times
and routes (of the minimum travel time) between two locations calculated from road network andrepresents the activity-travel pattern on a map and a timeline on GIS. SMAP-L can check time
constraints of the latest time to return home and opening hours of the opportunities, and monetarybudget constraint. Also, SMAP-L can give information on alternative opportunities nearer the12preceding destination, alternative travel routes with good scenery, recommended activity duration ateach opportunity and recommended lunch time. Figure 6 shows a snapshot of SMAP-L. Figure 7
shows a concept of activity scheduling in SMAP-L. As compared to SMAP-E, SMAP-L was used
for planning of trip-chain including multiple destinations in a prism. SMAP and SMAP-E produce
automatically alternative activity-travel patterns under space-time prism constraints, while inSMAP-L, the feasibility of the activity pattern is tested after making the whole activity schedule.
4.2 Application to One-Day Leisure Activity Planning
SMAP-L was applied to one-day leisure activity planning in Awaji-shima Island to investigate its
usefulness for the decision-making support system and to examine travelers’ activity scheduling
behavior. Awaji-shima Island is located at about 50 km from Osaka city and the travel mode to visit
there is almost limited to traveling by car. The main industry on the island is tourism. First, we
conducted a questionnaire survey in January 2003 for 33 respondents living in the Osaka
metropolitan area, who were recruited by the authors’ personal contact. Among the respondents, 3
were university students, 17 were workers and 13 were part-time job workers or housewives. The
age of the respondents ranged from the 20s to 60s years old. There were 13 male and 20 female
respondents. Only two of the respondents had never visited Awaji-shima Island and 26 had visited
there several times before the survey. In the questionnaire survey, respondents were asked about
their experience of and attitude toward leisure travel and asked to plan one-day home-based leisuretour schedule to Awaji-shima Island with family members or some friends. Most of the respondents
answered that they usually got information on leisure activities from magazines and websites. The
information respondents wanted when going-out for leisure activities was what type of activity they
can participate in a leisure spot, opening hours of the facility, travel times and routes from theirhome, weather condition and what kind of food they can have. The information which respondents
wanted before starting travel was on leisure spots, whereas during travel was on road congestion.

Menu
Time
schedule

Travel route

Selected
destination

Money
expenseAwaji-shimaIslandTravel Out-of-home Name ofopportunities

Figure 6. Snapshot of SMAP-L
13

Alternative routewith good scenary
time
Opening hours of
the opportunity

Recommended
activity duration

Time budget

Recommended
lunch time
Space-time path
Alternative opportunity nearer
the preceding destination+Monetary budget
Home

Opportunities
in Awaji-shima

space

Figure 7. Activity scheduling in SMAP-L

In planning one-day leisure tour schedule to Awaji-shima Island, the respondents were asked torefer only to information in the “RURUBU Awaji-shima’02” magazine to which we had delivered
to them in advance. Information in the magazine consists of activity type, brief introduction,
explanation of location, telephone number, opening hours and entrance fee of leisure spots andrestaurants and a map showing their location and road network. Eighty percent of respondents spent
more than 20 minutes to complete the schedule. After that, an interviewer visited their home oroffice to conduct a face-to-face interview survey. The purposes of the interview were to ask the
respondents about their pre-planned activity schedule in detail and to simulate the schedule on
SMAP-L. It took about 60 minutes to finish the interview survey per respondent. The interviewer
operated SMAP-L on a laptop PC. The respondents looked at a PC screen and answered a series ofquestions to simulate the pre-planned activity-travel schedule on SMAP-L. First, departure andarrival time at home, monetary budget and the number of travelers were inputted as an initial setting.
The respondent selected a destination from a list of leisure spots and activity duration spending at
the destination. Then, travel time from her/his home to the first destination and the activity duration
were represented on the timeline, and travel route was represented on the map in SMAP-L. The
entrance fee at the destination, which was prepared as an attribute of the opportunity database wasautomatically added to the money expense indicator at the left side. However, the toll road fee andother expenses such as for lunch had to be manually added to the indicator by the interviewer. In thesame way, until “going back home,” the respondent made decisions about destinations, activity
duration and money expenses successively, examining travel time, activity duration, and money
expenses for travel and activities. For the respondents who did not have or could not make acomplete schedule, for example, in case that “destinations” and the sequence of visiting thedestinations were pre-planned but “departure/arrival time” at their home and each destination were
not determined, they were asked to make a complete schedule with SMAP-L. A series of operation
process of SMAP-L in the interview survey were stored in a laptop PC for post analysis. Theinterviews were also video-recorded.

144.3 Analysis of Activity Scheduling Process
After the interviews, analysis was carried out on the respondent’s scheduling process using datacollected with SMAP-L. From the analysis of the pre-planned activity schedule made by the
respondents before the face-to-face interview, it was revealed that there was an inter-personal
difference of scheduling patterns as shown in Table 3. All the respondents planned “destinations”
with visiting sequence in advance, but some of them did not plan or could not decide some elementsof activity-travel schedule such as “travel routes,” “location of having meals,” and“departure/arrival time” at home and each destination. Only 7 of the respondents took into
consideration the maximum “monetary budget” spending for travel and activities in the leisure tour.
In terms of combination of each element, the scheduling pattern 2, in which destinations, routes,
meals and timing were pre-planned, was the most dominant. The number of the respondents
classified into the scheduling pattern 4, in which only destinations were pre-planned, was not small(7 respondents were classified into this pattern). Scheduling patterns 1–4 occupied 90% of the total.
Table 3. Scheduling patterns and elements of pre-planned activity schedule
SchedulingNumber of
Destination
Route
Meals
TimingMoney
pattern
respondents
1
X
X
X
X
X
5
2
X
X
X
X
10
3
X
X
X
8
4
X
7
5
X
X
X
X
2
6
X
X
X
1
Number of
33
26
25
16
7
33
respondents
In the face-to-face interview survey, when the respondents were simulating their activity schedulewith SMAP-L, sometimes the schedule would not be feasible because of the original time andmonetary budget constraints. For example, the following situations occurred: time arriving at home
was later than the pre-planned time; and time arriving at the restaurant was before the opening hours.
In such cases, they had two alternative responses: “modifying” the current schedule, or “ignoring”
the original pre-planned constraints. Figure 8 shows the responses. When arriving at home later thanthe latest time in which each respondent decided to return home, the most dominant response was“no change,” whereas “modification to reduce activity duration at destinations” was seen manytimes. In contrast, when arriving at a destination “out of the opening hours,” the respondents
modified the schedule by “changing destinations,” or by changing time elements such as “change
departure time from home,” and “extend” or “reduce activity duration at the preceding opportunity.”On the other hand, it appeared that even if the total money expense exceeded the limit of the
pre-planned monetary budget, no one worried about money spending for an one-day leisure tour.
At the last part of the interview, the respondents were asked to make comments on advantage andimprovement of SMAP-L. The following points are about the inconvenience of SMAP-L as adecision-making support system of leisure travel:• When confronted with constraints, the respondent had to revise the activity schedule by trial
and error. It would be better if the alternative recommended activity schedules are produced
automatically.
• There were respondents who did not want to know total money expense. Represented
information should be selected by the user.
• The present version is difficult for the respondents to operate. Self-operating software is the15

no change

arrive at homelaterchange the sequence ofdestinations
delete destinations

out of opening
hours

alter destinations
change departure timeover monetarybudgetextend activity duration

0

10

20
30
frequency

40

50

reduce activity duration

Figure 8. Modification of activity schedule
ultimate development.
Reliability of information, especially on travel times, is a critical element. Acquiring
information on time is desirable and it should be at least updated periodically.
• Some respondents answered that they wanted information on travel distance and fuelconsumption. Location and opening hours of gas stations could be very useful information for
car drivers.
Nevertheless, if SMAP-L was improved technically in the above points in the near future, more than
80% of the respondents answered that they wanted to use such kind of tool as a decision-making
support system when going-out for leisure travel.

•

5. Conclusions
Two applications of GIS-based activity-travel simulators were developed based on the SMAP which
was an innovative integration of GIS and alternative activity-travel pattern generation model. One
application was SMAP-E developed especially for the purpose of instructing students inunderstanding the theory of space-time prism/accessibility and travel behavior under
spatio-temporal constraints. It was used in a graduate course as an educational tool. The participants
simulated the volume of prism and the feasibility of engaging in a discretionary activity in the prism,
using their own one-week activity diary data, before and after changing important variables in
activity schedule, transport system and activity opportunities affecting space-time accessibility.
From the analysis of students’ reports, it was found that a series of simulation using SMAP-E had
contributed to help the students to better understand the theory of space-time prism and humanactivity-travel patterns under spatio-temporal constraints in the urban area, as demonstrated in
HATS. SMAP-E was also effective for participants to reflect on their daily life activities. However,there is a still room for improvement of SMAP-E in order to conduct more realistic and practicalsimulation. This improvement concerns the preparation of the database on network andopportunities, and advanced techniques of programming. The other application was SMAP-L as adecision-making support system for activity planning using interactive surveys to collectinformation on the activity scheduling process of tourists’ leisure tour. Face-to-face interview
surveys were conducted with SMAP-L to simulate pre-planned activity schedule of one-day leisure
tour. SMAP-L was very useful for supporting decision-making on activity scheduling, providing
information on travel times, routes and opportunities, examining time and monetary budget
constraints. Data on the scheduling process collected with SMAP-L were used to analyze
respondents’ scheduling process in activity planning. The analysis revealed the inter-personal
difference of scheduling patterns in activity planning and responses when travelers found that theschedule was infeasible under time and monetary constraints. Further research concerns analysis of16relationships between activity scheduling and activity pattern using revealed preference data on realleisure tours with SMAP-L.
GIS-based activity-travel simulators developed in this study were very useful as both an interactive
survey tool and a decision-making support system. There is a wide possibility of contributing tofurther progress in activity-based analysis in the near future. First, GIS-based activity-travel
simulators can contribute to the evaluation of space-time accessibility measures after introducing
various space-time related policy options, such as congestion pricing and flexible working hours.
Since the change of travel cost by the 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 the case of flexible working hours, because the timing of work activity engagement depends on
the worker’s discretion, not only the volume and timing of prisms but space-time accessibility canbe changed. It would be meaningful to investigate the feasibility of engaging in discretionary
activities at different working hours when making decision about departure time to commute. Travel
tracking data collected by GPS could be also used for representation of detailed travel routes andspeed profiles, and for estimation of vehicle emissions. Second, it is very effective to investigate
individual travel patterns and advise travelers to encourage environmental friendly travel patterns,
as demonstrated in the Travel Blending (Rose and Ampt, 2001) and Travel Feedback Program(Taniguchi et al., 2003). In the future, the GIS-based activity-travel simulator can make it possible
for people by themselves to investigate alternative activity-travel patterns and to find more suitable
patterns in every situation, which could encourage voluntary behavioral changes. For that purpose,an object function to determine better alternatives should be a key element. For example, it ispossible to introduce some object functions to minimize travel times, travel distance, fuelconsumption, environmental damage, travel costs, etc., into the GIS-based activity-travel simulator.
Also, a system developed on the platform of web-GIS for getting real-time information on travel
and activities will be used as an enhanced car-navigation system with a real-time activity-travel
scheduling function.
Acknowledgement
The work was funded by The Japan Research Center for Transport Policy and Foundation for
Fusion of Science & Technology. Programming of SMAP-L mostly depended on Yoshiaki Shouji.
Takuya Maruyama assisted in preparing the railway network data in Tokyo Metropolitan Area.
Institute of Urban Transport Planning Co., Ltd. and NTT DoCoMo, Inc. assisted us in using PHS
location tracking systems.
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