1SIMULATION MODEL FOR ACTIVITY PLANNING(SMAP) :GIS-BASED GAMING SIMULATION
Nobuaki Ohmori, The University of Tokyo, Japan
Yasunori Muromachi, Tokyo Institute of Technology, Japan
Noboru Harata, The University of Tokyo, Japan
Katsutoshi Ohta, The University of Tokyo, JapanINTRODUCTION
Understanding the decision making process of travelers underlying complex travel behavior isthe most significant issue for evaluating the effect of urban transport policy. In 21st century,
our society faces big changes such as evolution of information and communication
technologies, environmental problems and an aging society. Various policy options for thesechanges have been taken in urban transport planning. Transportation Demand Management(TDM) measures and Intelligent Transportation Systems (ITS) technologies are very
promising for solution to the problems of congestion and environmental damages. Urban
infrastructure has been improved under the concept of universal design for the aged society.
Decision makers have to consider what groups of individuals and households are affected by a
new measure, since each individual and household has specific characteristics and lifestyle,and faces specific environment. Travel is a derived demand from the demand for activity
participation and activity-based analysis has been effective to evaluate how travelers respondto the various changes in conditions.
The first comprehensive study of activities and travel behavior is the work at Transport
Studies Unit (TSU) at Oxford University more than 20 years ago. Household Activity-Travel
Simulator (HATS) methodology, 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, 1980; Jones et al.,1983). On the HATS game board, spatial components of activity-travel patterns are2

Proceedings of the 9th WCTR – Seoul

represented on the map, and temporal components are represented on the timelines, using
activity diary data of all members of a household. When a policy measure is introduced,
household members discuss together considering changes of constraints on the game board,and then new activity-travel patterns are simulated in this board. Gaming simulation approachis especially interested in inter-personal linkages and constraints, gaining more realistic
responses than simply asking hypothetical questions. Other than TSU’s study, there have been
many works based on gaming simulation or interactive stated response survey methods (forexample, Phifer et al., 1980; Burnett and Hanson, 1982; Achmed et al., 1995; Lee-Gosselin
and Turrentine, 1997).
Generally, decision process of human activity-travel behavior has been considered as two-stepdecisions—choice set generation and choice from the choice set—and modeling activitytravel behavior has been based on this concept (for example, Bowman and Ben-Akiva, 1996).
It is very important to specify the choice set of feasible activity-travel patterns more
realistically, when modeling activity-travel behavior and investigating the effects of changes
in conditions to activity patterns. The space-time prism constraint formalized by Hägerstrand(1970) is a very useful idea for specifying alternative feasible activity-travel patterns in spacetime. Burns (1979) developed the concept of accessibility to space-time dimensions (spacetime accessibility) introducing the activity duration at opportunities based on space-timeprism 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 to generate alternative activity patterns of one day based on combinatorial algorithms(Jones et al., 1983). These studies generate alternatives of feasible activity-travel patterns.
Feasible choice set has been recognized as an important factor especially in destination choicemodels. Landau et al. (1982) calculated shopping destination choice sets based on prism
constraints and found that adding temporal constraints to a spatial choice model improved its
predictive ability. Thill and Horowitz (1997) demonstrated the effectiveness of the shopping
destination choice model with choice set probabilities. In recent studies, Miller (1999)
operationalized space-time accessibility under the space-time prism on a real world network
using GIS. Although these studies greatly contributed to analyses of feasible activity-travel
patterns in space-time, they are individually based analyses and do not explicitly incorporate
interactions among household members. Recker (1995) developed the model of pick-up anddelivery problem under household inter-personal and temporal constraints, but initial
applications have been limited by solution algorithm.

On the other hand, Geographic Information System (GIS) is a very promising tool for
handling and analyzing spatial data. There is a possibility that GIS helps spatial visualizationand data management in gaming simulation techniques. GISICAS (Golledge et al., 1994;
Kwan, 1997, 1998) is the integration of GIS and computational process model (CPM) forSimulation Model for Activity Planning (SMAP)3

activity scheduling under space-time constraints. The concept of this model is extended totravel decision support system for the dynamic decision under the use of Advanced Traveler
Information Systems (ATIS). However GISICAS does not incorporate multiple travel modes,
and use GIS mainly for managing input data on network and locations. Data on activity
opportunities are not dealt with as real locations but the center of zone, and then opening
hours are not realistic.
The objectives of this study are mainly to enhance gaming simulation methods using GIS
capability and to understand the elderly household activity-travel patterns by interactive
surveys. A GIS-based gaming simulation tool is developed linked to the model generating
alternative activity-travel patterns, which is named Simulation Model for Activity Planning(SMAP). SMAP improves previous gaming simulation practices by introducing space-timevisualization on GIS, and feasible activity patterns are calculated based on space-time andinter-personal constraints. Generation of feasible alternative activity patterns is based on theidea of advanced space-time prism classifying activities into three types according to fixity oftiming, duration and location of activities. Moreover, pick-up behavior within family
members and trade-offs of multi-day activities are examined using household members’ oneweek activity diary data. Applications of SMAP to elderly households are presented, aiming
at understanding the elderly people’s travel behavior and responses to changes in conditions.

DEVELOPMENT OF SIMULATION MODEL FOR ACTIVITY PLANNING(SMAP)
GIS-BASED GAMING SIMULATIONThe outline of SMAP is described below. Interactive surveys on GIS can be conducted using
SMAP to understand household constraints and to examine responses to changes in conditions.
Figure 1 shows the outline of SMAP model structure.
Generally, based on the previous study, the space-time prism is determined by fixed activities
and transportation network, in addition to these two factors feasible activity-travel patterns
participating in out-of-home activities in the prism are determined with activity opportunities.
Input data for SMAP consist of data on travel demand side (individual and household
characteristics and activity schedule), transportation supply side (network travel time between
nodes by mode and time of day calculated using 4transport network data) and opportunity side (location point and opening hours of eachactivity opportunity). Data concerning travel demand side include home location

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points, mode availability, travel time/cost budget, and activity schedule of household
members. Activity schedule contains activity type, start and end time, location and priority.
Priority of each activity is introduced as space-time constraints of time of day, duration andlocation of each activity (the details are described in the next subsection).
Data about transport supply side are network travel times between nodes by mode and time ofday, which is calculated using road network data and public transport network data (only bus
in this study). Digital Road Map (DRM), which is the standard road network data in Japan, isused as road network data in the study. Since digital data on bus network do not exist
especially for local cities, we made bus network data on bus stops, routes and frequency byourselves. Travel times between all nodes are calculated by a C program using minimum path
algorithms. Travel time by car (car driver, car passenger and taxi), bicycle and walk is
calculated using road network. Travel time by bus is calculated as minimum generalized
travel time on routes, using equivalent coefficients (Nitta et al., 1995) considering travel
resistance by each mode as not only boarding but also access/egress walking, transfer andwaiting (Ohmori et al., 1999).
Data for opportunity side consist of location and opening hours of activity opportunities.
Digitalized data on address of opportunities are available in major cities, but data on opening
hours of opportunities are not available and need to be made manually on GIS referring to“paper map” and telephone directory. Opening hours are used as temporal constraints for
specifying feasible activity-travel patterns in a prism
Input Data

Dimensions
Mode- Wal k- Bicycle- Bus- Taxi- Car Driver- Car Passenger
Time of Day- PrismDay- Day of the Week

Travel Demand Side
- Individual/Household Characteristics
Home location
Mode availability
Travel time/cost budget- Activity Schedule
Transport Supply Side- Network Travel Time by Mode andTime of Day
Opportunity Side
- Activity Opportunity
Location
Opening hours

ChangeConstraints

Alternative Activity-Travel Pattern
Generator
SpatiaSimulationl and TempModeloral Refor
preActivity
sentationPlanning (SMAP)of Activity-Travel Patterns on GIS

Figure 1. Outline of Simulation Model for Activity Planning (SMAP)

5

Simulation Model for Activity Planning (SMAP)5

determined by activity schedules. Spatial data on opportunities and network are managed in
the form of longitude and latitude. All spatial data are also used as background data forrepresentation of activity patterns on GIS.
Alternative activity-travel pattern generator, which is the program written in C, is a model
generating, via enumeration of sampling choice sets, feasible patterns like PESASP and
CARLA. Alternative activity-travel patterns for one target out-of-home activity, depending on
mode and time of day, are calculated within space-time prism constraints. Firstly, nearest
network node from each opportunity location (including home) is searched based on
information of longitude/latitude. Then feasibility of participation in out-of-home activities ina prism by each travel mode is calculated, using activity schedule, point-to-point travel time
between opportunities and opening hours, as described in the next subsection. The calculated
results are used as input to next component of SMAP that is space-time representation ofactivity-travel patterns.
Spatial and temporal representation of activity-travel patterns on GIS: Spatial and temporalcomponents of reported activity-travel patterns and alternative activity-travel patterns
generated by the alternative activity-travel pattern generator are represented on GIS (in the
study MapInfo software is used). The way of representation in space-time is based on the
HATS game board. By changing environmental constraints as input data, for example, mode
availability, travel time budget, travel cost budget, activity schedule, travel speed and opening
hours, alternative activity-travel patterns after change of constraints are also calculated andrepresented. Respondents examined the changes and alternatives, and are asked to select a
new activity pattern among alternatives represented on GIS.

Alternative Activity-Travel Pattern Generator
In calculating choice sets of feasible alternative activity-travel patterns, SMAP is based on thefollowing assumptions of individual activity scheduling decisions (Ohmori et al., 1998;
Ohmori et al., 1999). An individual has main planned activities (in this study, this type ofactivity is defined as activity (a)) in the day, and firstly these activities are scheduled, which
are assumed that time of day, duration and location engaged in these activities are fixed.
These activities could also be considered as most prioritized activities. Secondly, planned but
flexible activities (activity (b)) are scheduled, of which duration and location are fixed but
time of day is flexible within specific time period. Finally, non-planned and discretionary
activities (activity (c)) are engaged in the open periods. As you know, travel may be derived
from demand for participation of all of these activities.

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In a traditional concept of space-time prism developed by Hägerstrand, time periods are
divided into two types: open periods and blocked periods. An individual has the option of
traveling and engaging in activities in open periods. On the other hand, the individual iscommitted to engage in fixed activities at fixed locations in blocked
periods. Therefore activities are classified into two types: activity (a) and (c) using the terms
defined in this study. In daily life, people have such kind of activities as activity (b) which isplanned to do and only total duration and location are fixed but time of day is not fixed, forexample, house cleaning, food preparation and personal care. In SMAP, introduction of theidea of activity (b) enables to model travel behavior that is adjusting timing of engaged in outof-home discretionary activities both to opening hours of opportunities and to other members’
activity scheduling constraints, considering limited time budget for activity (c) and travel. If
an individual X has an activity schedule consisting of n activities (a), the number of spacetime prisms formed in the day is (n – 1). In this study, it is assumed that the end time of nX-th
activity (a) is the earliest start time of nX-th prism and the start time of (nX + 1)-th activity (a)is the latest end time of nX-th prism. Components of the 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

L , L :
X
i

X
j

Lk :

Location of opportunity k∑ ATXb ,n X:

The sum of the duration of activities (b) in which individual X engages
between times TS nX and TE nX
Xt m ,ij :X

Travel time from location i to location j by mode m

Tk :Activity times that individual X participates in the target activity atopportunity k
The l-th start time and end time in a day of opportunity k
OS , OE :X
Trip _ Start _ Time n : Travel start time of individual X in the nX-th prism
k
l

k
l

X

( TE nX − TS nX − ∑ ATbX,n ) is the amount of time for participating in activities (c) and travel for
X

X

X

individual X. Individual X can participate in the target out-of-home activity at opportunity k in
the nX-th prism within the amount of available time ( TE nX − TS nX − ∑ ATbX,n ), only when s/heX

X

X

starts traveling after TS nX by mode m, arrives at location Lk after OSlk , participates in theXactivity for Tk , leaves before OElk by mode m and arrives before TE nX . If positive number ofX

Trip _ Start _ TimeXnX

exists subject to equations (1) through (5), individual X has the option to

do the out-of-home activity at opportunity k in the nX-th prism.
Trip _ Start _ Time nXX ≥ TS nXX(1)

Simulation Model for Activity Planning (SMAP)

7

Trip _ Start _ Time nXX + tm, LX L ≥ OSlk

(2)

Trip _ Start _ Time nXX + tm, LX L + Tk ≤ OElk

(3)

i

k

i

k

Trip _ Start _ Time nXX + tm, LX L + Tk + tm, L LX ≤ TE nX(4)

TE nXX − TS nXX − ∑ ATbX,n X ≥ t m , LX L + Tk + tm, L LX

(5)

i

k

k

i

X

j

k

k

j

Under this assumption, alternative activity-travel patterns for one target out-of-home activity
are calculated within space-time constraints, allowing for variation in mode, time of day andday of the week. Although it is assumed that an individual has minimum time necessary foreach kind of activity participation, in the study the observed time in activity diary data is used
as activity time Tk . In addition, pick-up mode availability is also introduced explicitly
considering space-time constraints of two persons’ activity schedules in SMAP. Classified
into four patterns are whether the pick-up mode (car passenger) is available for individual X
by individual Y as a driver, figures 2.1 to 2.4 show examples of two individuals’ prisms,
space-time paths and opening hours of the opportunity when pick-up mode is available.
Likewise individual X, components of individual Y’s activity schedule are defined as:
TS nYY , TE nY :The earliest start time and the latest end time of the nY-th prism ofY

individual Y
LYi , LYj :∑ AT

Yb , nY

Locations where the nY-th prism of individual Y starts and ends:

The sum of the duration of activities (b) in which individual Y engages
between time TS nY and TEnYY

t car ,ij :YTravel time from location i to location j by car

Trip _ Start _ TimenYY : Travel start time of individual Y in the nY-th prism

1. Pick-up mode is available in the reported trip
Pick-up mode is available when a prism of individual Y contains the individual X’s trip inspace-time (figure 2.1). In this case, out-of-home activities of individual X are assumed to be
activity (a) and a prism is formed as shown in figure 2.1. Constrained by the activity schedule,
individual Y can leave location LYi after TS nY and go to LiX to pick up individual X toYlocation Lk , and has to be in location L before TEnY . If positive numbers of bothYjTrip _ Start _ Time

X
nX

Y

and Trip _ Start _ Time exist subject to equations (1) and (6) through (10),YnY

individual X has the option to travel using pick-up mode by individual Y in the trip.
Trip _ Start _ TimenYY ≥ TS nYY
Trip _ Start _ Time

X
nX

(6)

+ tcar , L L ≤ TE
X
i

X
j

(7)

X
nX

Trip _ Start _ TimenYY + tcar , LY LX + tcar , LX LX + tcar , LX LY ≤ TE nY(8)

TE nXX − TS nXX − ∑ ATbX,n X ≥ tcar , LX LX

(9)

i

i

i

i

j

j

j

j

Y

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Proceedings of the 9th WCTR – Seoul
Y
Y
TE nYY − TS nY − ∑ ATb, nY ≥ tcar , LY LX + tcar , LX LX + tcar , LX LY
i

i

i

j

j

(10)

j

Next, feasibility that pick-up mode is available for participation in the target out-of-homeactivity at another timing (prism) is examined. In this case, the following three patterns aretested according to differences of travel directions “to” and “from” the opportunity.
2. Pick-up mode is available in the trip “to” the opportunity k in individual X’s prism
When the individual X’s nX-th prism and the individual Y’s nY-th prism overlap in space-time
as shown in figure 2.2, in other words, if positive numbers of both Trip _ Start _ Time nX andX

Trip _ Start _ TimeYnY

exist subject to equations (1), (6) and (11) through (16), pick-up mode byindividual Y is available for individual X in the trip only “to” the opportunity k, and individual
X can participate in the target activity and travel from location Lk to LXj by mode m other thanpick-up mode.
Trip _ Start _ Time nXX + tcar , LX L ≥ OSlk

(11)

Trip _ Start _ Time nXX + tcar , LX L + Tk ≤ OElk

(12)

i

k

i

k

Trip _ Start _ Time nXX + tcar , LX L + Tk + tm, L LX ≤ TE nX
i

k

k

(13)

X

j

Trip _ Start _ TimenYY + tcar , LY LX + tcar , LX L + t m, L LY ≤ TE nY(14)

TE nXX − TS nXX − ∑ ATbX,n X ≥ tcar , LX L + Tk + tm, L LX

(15)

i

i

i

i

k

k

Y

j

k

k

j

Y
Y
TE nYY − TS nY − ∑ ATb, nY ≥ tcar , LY LX + tcar , LX L + t m , L LY
i

i

i

k

k

(16)

j

3. Pick-up mode is available in the trip “from” the opportunity k in individual X’s prism
When prisms of individual X and individual Y overlap in space-time as shown in figure 2.3
and positive numbers of both Trip _ Start _ Time nX and Trip _ Start _ TimenYX

Y

Range to which prism
can be adjusted

time

OEkl

Individual X’s prism

OSkl

Individual Y’s prism

2.1 Available in the reported trip

Tk

2.2 Available in the trip “to” theopportunity k in the prism
Individual X’s path

X

TE nx

TEYny

Individual Y’s path
Opening hours ofopportunity k
Trip_Start_TimeYny

TSXnx
Trip_Start_TimeXnx

Subject to individual
Y being in this area

TSYny

2.3 Available in the trip “from” theopportunity k in the prism

2.4 Available in the trip both “to” and“from” the opportunity k in the prism

Simulation Model for Activity Planning (SMAP)9exist subject to equations (1), (6) and (17) through (22), pick-up mode is available forindividual X in the trip only “from” the opportunity k.
Trip _ Start _ Time nXX + tm , LX L ≥ OSlk

(17)

Trip _ Start _ Time nXX + tm , LX L + Tk ≤ OElk

(18)

i

k

i

k

Trip _ Start _ Time nXX + tm , LX L + Tk + tcar , L LX ≤ TE nX
i

k

k

(19)

X

j

Trip _ Start _ TimenYY + tcar , LY L + tcar , L LX + t car , LX LY ≤ TE nY(20)

TE nXX − TS nXX − ∑ ATbX,n X ≥ tm , LX L + Tk + tcar , L LX

(21)

i

k

k

i

j

j

k

Y

j

k

j

Y
Y
TE nYY − TS nY − ∑ ATb, nY ≥ tcar , LY L + tcar , L LX + t car , LX LY
i

k

k

j

j

(22)

j

4. Pick-up mode is available in the trip both “to” and “from” the opportunity k in individual
X’s prism
When prisms of individual X and individual Y overlap in space-time as shown in figure 2.4
and positive numbers of both Trip _ Start _ Time nX and Trip _ Start _ TimenY exist subject toXYequations (1), (6) and (23) through (28), pick-up mode is available in the trip both “to” and“from” the opportunity k. In this case, individual Y has to be within the specific space-timearea during individual X participates in the activity at opportunity k.
Trip _ Start _ Time nXX + tcar , LX L ≥ OSlk

(23)

Trip _ Start _ Time nXX + tcar , LX L + Tk ≤ OElk

(24)

i

k

i

k

Trip _ Start _ Time nXX + tcar , LX L + Tk + tcar , L LX ≤ TE nX
i

k

k

(25)

X

j

Trip _ Start _ TimenYY + tcar , LY LX + tcar , LX L + Tk + tcar , L LX + t car , LX LY ≤ TE nY(26)

TE nXX − TS nXX − ∑ ATbX,n X ≥ tcar , LX L + Tk + tcar , L LX

(27)

i

i

i

i

k

k

k

k

j

j

Y

j

j

Y
Y
TE nYY − TS nY − ∑ ATb, nY ≥ tcar , LY LX + tcar , LX L + Tk + tcar , L LX + t car , LX LY
i

i

i

k

k

j

j

(28)

j

The algorithm described above only deals with space-time constraints, after this the feasible
alternatives are reduced by individual capability constraints as mode availability, maximum
walking time per trip and maximum travel cost per day. Feasibility of participating in thetarget activity on a different day is also tested using this algorithm. Since location andduration of the target activities is fixed in the model, alternative patterns only of mode, timeof day and day of the week are generated. This could be extended to introducing choice set of
locations and durations. In the model, it is supposed that the individual must do activities (b)within the same prism, but the algorithm could be extended to within another prism of the dayor another day.

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Enhanced Gaming Simulation Techniques in GIS
The alternative activity-travel pattern generator described above is linked to GIS,
thus constructing a GIS-based gaming simulation tool. Not only observed activity-travel
patterns but also alternative patterns calculated by the alternative activity-travel patterngenerat or can b e repres en ted on G IS. The part of us er i nt erface of SMAP
program for a series of operations is written in MapBasic software, which is special addition
to MapInfo. The features of SMAP are summarized as the followings:
1. Advantages from the use of GIS
Visualization of activity-travel patterns on digital maps and time grids (temporal components
are drawn in another window), showing only appropriate information case by case-Capability of zoom in/out of the specific spatial area and time period
Easiness of changing input data on not only individual and household characteristics
but also level of service of transport network and opportunities on GIS
2. Advantages from linking to alternative activity-travel pattern generation model-

Realization of feasible alternatives explicitly considering space-time and physicalcapability constraints- Examination of pick-up mode availability introducing inter-personal linkages amonghousehold members explicitly considering two persons’ prism constraints- Dealing with alternatives for one out-of-home activity, depending not only mode andtime of day but also day of the week using multi-day activity diary data
Since several indicators about activity-travel patterns are also calculated and presented, forexample, the number of trips, travel time by mode, travel cost and activity duration by activity
types, respondents can examine the alternatives referring to not only spatial and temporal
representation but also this quantitative information.

APPLICATIONS TO THE ELDERLY HOUSEHOLDSSMAP was applied to elderly households to understand constraints of daily activity patterns
and responses to changes in conditions. Generally the elderly have a variety of physicalcapability constraints, for example, some of them can not drive a car, can not walk so long
distance, feel resistance to using public transport and keep regular hours in daily life in orderto keep their health. Also coupling constraints are significant because they want cooperationof other people, for example, they sometimes want to be picked up by other family members.
Therefore activity-based analysis considering various constraints should be effective tounderstand elderly people’s travel behavior. The survey procedure consists of two main
phases: (1) the completion of a self-administered activity diary and (2) household interactive
in-depth interview, based upon the SMAP gaming simulation procedure.
Simulation Model for Activity Planning (SMAP) 11

Simulation Model for Activity Planning (SMAP)

11

SURVEY DESCRIPTION AND DATA COLLECTION
In November 1999, a total of 12 elderly household interviews were carried out in Akita city.
Akita is the city where the prefectural government of Akita prefecture locates. Akita
prefecture is expected to be the most aged one in Japan in about 20
years from now. Population of the city is about 300,000 and the ratio of the elderly is about
14%. Public transportation used by inhabitants is mainly bus. The recent trend is that car
dependency increases and large opportunities move from the city center to suburbs, while thelevel of bus service decreases.
In advance of main interview surveys, one-week activity diary data were collected of family
members over 15 years old in 13 elderly households (20 retired elderly and 10 non-elderly
people). In this survey, not only out-of-home activities but also in-home activities were asked
to record in the questionnaire sheets. Spatio-temporal behavioral data during the survey
period were collected automatically using PHS (Personal Handyphone System) location
tracking system at the same time (Ohmori et al., 1999). PHS is originally a cellular phonesystem in Japan. When a person has PHS, data on location (longitude and latitude) and time
can be collected automatically at the specific time intervals. Each positional data has about
100 meters error. In this study, PHS data were used for help to specify only locations where
respondents had visited during the survey period, although travel routes could be estimatedusing spatio-temporal tracking data collected by PHS (Asakura et al., 2000).
Data on locations and opening hours of all activity opportunities (about 100 sites) including
the respondents’ homes and working places that 30 respondents visited during survey period
are prepared on GIS. Home is assumed to be available all the day and information about
opening hours of other opportunities such as shopping sites were arranged by telephoneinterviews to each facility. Travel times by mode and time of day between opportunities arecalculated using detailed transportation network data as described in the previous section.
The household in-depth interviews were conducted according to the SMAP gaming
simulation procedure. The SMAP program is installed in a notebook PC and an interviewer
visited respondents’ home with it. For single elderly household, interview is conducted for
one respondent. Since the present version of SMAP can deal with two persons’ activity
patterns at the same time, for household more than two persons, “main-respondent (elderly
people)” and “sub-respondent (who can drive a car)” are selected in the household.
First part of the interview procedure is intended for understanding the reasons for theobserved behavior. Respondents are asked about their mode availability, maximum walking
time per trip and maximum travel cost per day for initial settings. After selecting a target day
from one-week (the activity diary survey period), spatial and temporal components of the12Proceedings of the 9th WCTR – Seoul

respondent’s activity pattern of the day is represented on 12
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GIS. Travel pattern is represented as arrows between two locations in a digital map. Timespent on “in-home” and “out-of-home” activities and “travel” is represented on
different sections in vertical timelines of 24 hours starting at 3:00 a.m. All activities of the day
are classified into three types as described in the previous section according to the
respondent’s answers. All elderly respondents are retired and non-working people in thesurvey, and then location of fixed activities (activities (a) in the study) in their schedule isonly their home: activities (a) are sleep and eating according to the answers for the elderly,
activities (a) of other members are sleep, work and study. Then the alternative activity-travel
pattern generator generates alternatives about each out-of-home activity depending on modeand prism. The interviewer asks the reasons and various constraints of choosing the current
activity-travel patterns of the day comparing with alternatives.
After this, they are asked how they would modify the activity pattern under changes of
constraints. In this survey, the following changes in conditions were introduced:- Community bus, which is a very comfortable bus for the elderly in Japan, is available
only once a week. Bus stop is located in front of the respondent’s home.
- Giving up driving due to physical constraints of aging for car drivers.
- Using an electronic car that is very easy to operate (the car is under development in
Department of Engineering Synthesis, the University of Tokyo).
Representation of activity pattern Opening hours of opportunityof individual X on the timeline engaged in the target activity

The target activity

Representation of activity patternof individual Y on the timeline

Representation of travel pattern of
Individual X on the map

Figure 3. Screenshot of SMAP (change the target activity of going to hospital, from bybicycle in the morning on Friday to by car-passenger in the afternoon on Saturday)

Simulation Model for Activity Planning (SMAP)-

13

Extended opening hours of the opportunity for the target activity or this is alsoavailable on holiday.

By changing input data on travel demand, transport supply and opportunity, the choice set ofalternative patterns after changes is calculated by the alternative activity-travel patterngenerator. New feasible patterns for participating in the target activity are represented on GIS,
and the respondent is asked to select one activity pattern, that is day of the week, time of day
and mode. For the respondent living with other members who can drive in the household, “carpassenger” mode is included in the choice set. Two persons’ activities are represented
simultaneously as shown in figure 3. If “main-respondent” selects an alternative traveling by
car passenger mode, the interviewer asks “sub-respondent” whether s/he would accept her/his
pattern including added serve passenger activities. Opening hours are represented as both
directional arrows in the temporal part: in the figure 3, two arrows mean the opening hours of
the opportunities consist of two parts (morning and afternoon). Data on initial settings,
changes of constraints, alternatives, selected patterns and survey procedures are stored in PC
for post analyses. A series of interviews were also video-recorded, so that the discussions canbe analyzed in depth subsequently.

UNDERSTANDING OF CONSTRAINTS AND RESPONSES TO CHANGES IN CONDITIONS
Through the interview surveys, many detailed and individual specific constraints were
understood and various responses to changes in conditions were identified. In this paper, only
two examples are described and discussed below. Figures 4 and 5 shows the activity-travel
patterns and prism constraints by mode in space-time, the observed ones at left side and thestated ones after changes of conditions at right side. In the figures, three prisms are formed in
the activity pattern of the elderly: after breakfast and before lunch, after lunch and before
dinner, after dinner and before sleep. Differences of prism size means differences of travel
speed by mode.
The first example is the case in which opportunities are available on weekends (figure 4). She
is 89 years old and lives with her son, her son’s wife and her grandson. Her available travelmodes are walk, taxi and car passenger. On weekdays, her son works outside (at opportunity
C in the figure 4) and pick-up mode is not available for her. A hospital (opportunity A) is
open only on weekdays, so she goes to the doctor by taxi on a weekday. If the hospital isavailable on weekends, she answered she would go to see the doctor by pick-up mode by her
son’s driving and he also accepted her answer. Another hospital (opportunity B) is also open
in the afternoon, but her good friend comes to the hospital in the morning, so every time shegoes to the hospital in the morning to meet the friend. She habitually doesn’t go out after
having dinner because of darkness outside, therefore she has no options of doing out-of-homeactivities in the prism formed between dinner and sleep.

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Proceedings of the 9th WCTR – Seoul

The second example, in another household, is that in case community bus is available
Age: 89
Sex: female
HH members: son, son’s wife andtime

weekdays

Available mode: walk, taxi and car passenger
Maximum walking time: 20min/trip
Maximum travel cost: 3,000yen/day
weekends
Prism of theelderly person
Car
taxi
walk
Prism ofher soncar
travel

A
home

BC

A
home

activity

B

Figure 4. In case of opportunities available on weekends
Age: 68
Sex: female
HH members: none (living alone)time

beforeAvailable mode: walk, bus and taxi
Maximum walking time: 30min/trip
Maximum travel cost: 5,000yen/day
after

prism
taxi

bus

walk

travelA
B
home

activityA
B Model for Activity Planning (SMAP)
Simulation
home

Figure 5. In case of community bus available once a week15

Simulation Model for Activity Planning (SMAP)15

once a week (figure 5). She is 68 years old and lives alone. She cannot drive a car, and heravailable travel modes are walk, bus and taxi. She walks to a supermarket (opportunity A in
figure 5) and goes to a hospital (opportunity B) by normal bus.
When community bus is in service once a week, for example on Wednesday, she answered
she would go to the supermarket using community bus on Wednesday, but she would not go
to the hospital by community bus on Wednesday because her doctor receives patients only on
Monday and Thursday. Bus is not in service after p.m. 8:00 o’clock, so a prism of bus does
not exist at night. She habitually doesn’t go out after having dinner, either.
Other important constraints to activity-travel patterns of the elderly were found in the 13
household interview surveys. These are the followings:
1. Constraints concerning travel mode
- Intend to walk as much as possible for health, but it is difficult to walk to and from aopportunity, due to physical constraints, so walk in only one way and use other mode in
the other way- Use bicycle for small purchases, car for larger purchases- Does not go to downtown by bicycle because of fear of danger for vehicles running in
downtown- Does not drive a car during congested time periods because of difficulty to park
2. Constraints concerning time of day- Habitually go out before noon and spend time relaxing in the home on afternoon- (Inversely) do housework and rarely go out before noon
- Go out in the morning because of availability of pick-up mode by other family member
living with- Go to a supermarket just on time when the supermarket opens, in order to buy discount
goods- Go to hospital in the morning when not so many patients come
Last part of interview was questions about SMAP interview procedure. Although it took longtime, about 1 to 1.5 hours, to complete the interview, respondents paid much attention to the
PC screen and they were very interested in this high-tech method. Some of the respondents
answered that they were able to look back to daily activity and travel behavior, and others
were surprised at their very small action space. A few respondents answered that temporalcomponent of activity pattern was a little difficult to understand. These answers suggest that
SMAP could help to evaluate a person’s activity pattern and support his decision making ofactivity planning, but improvement of temporal representation would be needed for
application to the elderly.

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Proceedings of the 9th WCTR – Seoul

CONCLUSION
In this paper, Simulation Model for Activity Planning (SMAP): GIS-based gaming
simulation tool linked to alternative activity-travel pattern generator, was developed based on
HATS. SMAP is unique and useful at the points: (1) the advanced space-time prism isintroduced by classifying activities into three types according to fixity oftiming, duration and location of activities, (2) pick-up mode is introduced explicitly
considering two persons’ prism constraints, and (3) multi-day activity-travel patterns can beconsidered. Initial application of SMAP was to understand the elderly household activitytravel patterns and responses to changes in conditions. By conducting interactive in-depth
interview surveys with this tool, various individual and household specific constraints were
explored and the possible new activity patterns to the changes of constraints were examinedconsidering pick-up mode and multi-day activities. GIS advantages were effectively used to
conduct this enhanced gaming simulation technique.
There would be a wide range of application fields of SMAP other than this study of activity
analysis of the elderly household, making use of GIS-based gaming simulation techniques.
Especially, it would be useful to analyze such as inter-household car-pooling behavior
considering activity scheduling of more than one person and travel blending introducing
trade-offs of multi-day activities. Detailed travel data including routes and speeds collectedusing positioning technologies (Global Positioning System (GPS) and PHS) could also
contribute to gaming simulation techniques, for example, responses of travelers after pricing
measures could be well understood on GIS. For these applications, it would be useful thatalternative activity pattern generator should be improved in choice set dimensions introducing
location and route choice.
Another direction is application of SMAP to decision making support system of activity
planning. Although in the present study, SMAP models only choice set and lacks in choicemodels from the choice set, there is a possibility of further development like GISICAS
introducing models of activity pattern generation. Users of this system would be able toevaluate their activity-travel patterns from the viewpoints not only of travel time and travel
cost but also of personal physic2al energy consumption and environmental damages, etc.
Recommended activity-travel patterns are demonstrated according to individual specificobjective functions, being really the aid for individual “activity planning”.
The behavioral analysis in space-time based on the activity-based approach is very useful inorder to understand activity-travel behavior, but data collection of household activity patterns,
transport networks and activity opportunities are critical elements forthe study. For data on travel behavior, use of positioning technologies such as GPS and PHS
could improve quality of diary data and mitigate the burden of respondents. For transport
network and opportunities, GIS database available for researchers need Simulation Model forSimulation Model for Activity Planning (SMAP)17

to be prepared and updated periodically.

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