Topic Area:
D4
Paper Number:
4414
Authors:
Nobuaki OHMORI, Yasunori MUROMACHI, Noboru HARATA and Katsutoshi OHTA
Title:
Simulation Model for Activity Planning (SMAP): GIS-based Gaming Simulation
Abstract:
This paper presents a Simulation Model for Activity Planning (SMAP): GIS-based gaming
simulation tool linked to models of alternative activity pattern generator, introducing
pick-up mode considering space-time constraints of two people’s activity schedules andtrade-offs of multi-day activities, using one-week activity diary data. Generation of feasible
alternative activity patterns is based on the idea of advanced space-time prism classifying
activities into three types according to fixity of timing, duration and location of activities.
Initial application of SMAP aims to understand the elderly household activity-travel
patterns and responses to changes in conditions. By conducting interactive in-depth
interview surveys with this tool, various constraints to activity-travel pattern are explored
and the responses to the change of constraints were examined considering pick- up modeand multi-day activities. GIS advantages are effectively used to conduct this enhanced
gaming simulation technique.

Key words: Activity- travel pattern, Space-time prism, Gaming simulation, GIS
Method of Presentation:(1) OHP((2) Slide Projector((3) LCD Projector
( ?

)
)
)

Topic Area Code: D41

Simulation Model for Activity Planning (SMAP):
GIS-based Gaming Simulation

Nobuaki OHMORI
Research Associate
Institute of Environmental Studies
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
Yasunori MUROMACHI
Associate Professor
Engineering Research Institute
The University of Tokyo
2-11-16 Yayoi, Bunkyo-ku, Tokyo, 113-0032, Japan
Tel.: +81-3-5841-7732, Fax.: +81-3-5841-8527
E- mail: ymuro@ut.t.u-tokyo.ac.jp
Noboru HARATA
Professor
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
Katsutoshi OHTA
Professor
Department of Urban 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

2

1. INTRODUCTION
Understanding the decision making process of travelers underlying complex travel
behavior is the most significant issue for evaluating the effect of urban transport policy. In
21st century, our society faces big changes such as evolution of information andcommunication technologies, environmental problems and an aging society. Various policy
options for these changes 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 andenvironmental damage. Urban infrastructure has been improved under the concept ofuniversal design for the aged society. Decision makers have to consider what groups ofindividuals and households are affected by a new measure, since each individual andhousehold has specific characteristics and lifestyle, and faces specific environment. Travelis a derived demand from the demand for activity participation and activity-based analysis
has been effective to evalua te how travelers respond to 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 gaming simulation, was usedvery successfully in trying to better understand household travel decisions and theconstraints within which those decisions are made (Jones, 1980; Jones et al., 1983). On the
HATS game board, spatia l components of activity-travel patterns are represented on themap, and temporal components are represented on the timelines, using activity diary dataof 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 newactivity-travel patterns are simulated in this board. Gaming simulation approach isespecially interested in inter-personal linkages and constraints, gaining more realistic
responses than simply asking hypothetical questions. Other than TSU’s study, there has
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 and Turrentine, 1997).
Generally, decision process of human activity-travel behavior has been considered as
two-step decisions— choice set generation and choice from the choice set— and modelingactivity-travel 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 theeffects of changes in conditions to activity patterns. 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-time accessib ility) introducing the activity duration atopportunities based on space-time prism constraint. 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 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 pattern. Feasible choice set has beenrecognized as important factor especially in destination choice models, for example,
Landau et al. (1982) calculated destination choice sets based on prism constraints andfound that adding temporal constraints to a spatial choice model improved its predictive
3

ability. In recent studies, Miller (1998, 1999) operationalized space-time accessibility
under space-time prism on a real world network using GIS. Although these studies greatly
contributed to analyses of feasible activity- travel pattern in space-time, they are
individually based analyses and do not explicitly incorporate interaction among household
members. Recker (1995) developed the model of pick- up and delivery problem underhousehold 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 possibility that GIS helps spatial visualizationand data management in gaming simulation techniques. GISICAS (Golledge et al., 1994,
Kwan et al., 1997) is the integration of GIS and computational process model (CPM) for
activity scheduling under space-time constraints. The concept of this model is extended totravel decision support system for dynamic decision under the use of advanced traveler
information systems (ATIS). However GISICAS does not incorporate multiple travelmodes, and use GIS mainly for managing input data on network and locations. Data on
activity opportunities are not dealt with as real locations but center of zone, and thenopening 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
the idea of advanced space-time prism classifying activities into three types according to
fixity of timing, duration and location of activities. Moreover, pick-up behavior withinfamily members and trade-offs of multi-day activities are examined using household
members’ one-week activity diary data. Applications of SMAP to elderly households arepresented, aiming at understanding the elderly people’s travel behavior and responses tochanges in conditions.

2. DEVELOPMENT OF SIMULATION MODEL FOR ACTIVITY PLANNING(SMAP)

2.1 GIS-based gaming simulation
The outline of SMAP is described below. Interactive surveys on GIS can be conductedusing 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, 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 andhousehold characteristics and activity schedule), transportation supply side (network travel
time between nodes by mode and time of day calculated using transport network data) and4

opportunity side (location point and opening hours of each activity opportunity). Data
concerning travel demand side include home location 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 and location of each activity(the details are described in subsection 2.2).
Data about transport supply side are network travel times between nodes by mode and timeof day, which is calculated using road network data and public transport network data (onlybus in this study). Digital Road Map (DRM), which is the standard road network data inJapan, is used as road network data in the study. Since digital data on bus network do notexist especially for local cities, we made bus network data on bus stops, routes andfrequency by ourselves. Travel times between all nodes are calculated by a C programusing 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 asminimum 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 and waiting (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 temporalconstraints for specifying feasible activity-travel patterns in a prism 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 for representation of activity
patterns on GIS.

Dime ns io ns
Mode- Walk
- Bicycle- Bus- Taxi- Car Driver- Car P assenger
Time of Day- P rism
Da y- Day of the Week

Input Data
Travel Demand Side
- Ind iv id ual/Ho useho ld Characteristics
Ø Home locatio nØ Mode availab ility
Ø Travel t ime/cost budget- Activ ity Schedu leTransport Supp ly S ide- Network Travel Time by Mode andTime of Day
Opportun it y S ide- Activ ity Opport un ityØ Location
Ø Openin g hours

ChangeCo ns traints

Alte rnative Activ ity-Trave l Patte rn
Ge ne rato r
Spatial and Te mpo ral Re presentatio no f Activ ity-Trave l Patte rns o n GIS

Figure 1. Outline of Simulation Model for Activity Planning (SMAP)
5

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 basedon information of longitude/latitude. Then feasibility of participation in out-of- home
activities in prism by each travel mode is calculated, using activity schedule, point-to-point
travel time between opportunities and opening hours, as described in subsection 2.2. The
calculated results are used as input to next component of SMAP that is space-time
representation of activity-travel patterns.
Spatial and temporal representation of activity-travel patterns on GIS: Spatial andtemporal components 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 isbased on the HATS game board. By changing environmental constraints as input data, forexample, mode availability, travel time budget, travel cost budget, activity schedule, travel
speed and opening hours, alternative activity-travel patterns after change of constraints arealso calculated and represented. Respondents examined the changes and alternatives, andare asked to select a new activity pattern among alternatives represented on GIS.
2.2 Alternative activity pattern generator
In calculating choice sets of feasible alternative activity-travel patterns, SMAP is based on
the following 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 anddiscretionary 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.
In a traditional concept of space-time prism developed by Hägerstrand, time periods are
divided into two types: open periods and blocked periods. 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 thisstudy. In daily life, people have such kind of activities as activity (b) which is planned to
do and only total duration and location are fixed but time of day is not fixed, for exa mple,
house cleaning, food preparation and personal care. In SMAP, introduction of the idea ofactivity (b) enables to model travel behavior that is adjusting timing of engaged inout-of-home discretionary activities both to opening hours of opportunities and to othermembers’ 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), thenumber of space-time prisms formed in the day is (n – 1). In this study, it is assumed thatthe end time of nXth activity (a) is the earliest start time of nXth prism and the start time of(nX+1)th activity (a) is the latest end time of nXth prism. Components of the individual X’s
activity-travel pattern are defined as:
6TS nXX , TE nX :The earliest start time and the latest end time of the nXth prism ofindividual X
Locations where the nXth prism of individual X starts and ends
Location of opportunity k
The sum of the duration of activities (b) in which individual X

X

LXi , LXj :

Lk :

∑ AT

X
b, nX

:

engages between times TS nX and TEnX
Travel time from location i to location j by mode m
tm ,ij :Activity times that individual X participates in the target activity at
Tk :opportunity k
The lth start time and end time in a day of opportunity k
OSlk , OElk :Trip _ Start _ Time nX : Travel start time of individual X in the nXth prism
X

X

X( TEnX - TS nX - ∑ ATbX, n ) is the amount of time for participating in activities (c) and travel for
individual X. Individual X can participate in the target out-of-home activity at opportunity kin the nXth prism within the amount of available time (TEnX - TS nX - ∑ ATbX, n ), only whens/he starts traveling after TS nX by mode m, arrives at location Lk after OSlk , participatesin the activity for Tk , leaves before OElk by mode m and arrives before TEnX . If positive
X

X

X

X

X

X

X

X

number of Trip _ Start _ Time nX exists subject to equations (1) through (5), individual X has
the option to do the out-of-home activity at opportunity k in the nXth prism.
X

Trip _ Start _ Time nXX = TS nXX
Trip _ Start _ Time + tm ,LX L = OS

(1)
(2)

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

(3)

X
nX

i

i

k
l

k

k

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

TE nXX - TS nX - ∑ ATb, nX = tm ,LX L + Tk + tm ,L LX

(5)

i

X

k

k

X

j

X

i

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, timeof day and day of the week. Although it is assumed that individual has minimum timenecessary for each 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) isavailable for individual X by individual Y as driver, figures 2.1 to 2.4 show examples of
two individuals’ prisms, space-time paths and opening hours of the opportunity whenpick-up mode is available. Likewise individual X, components of individual Y’s activity
schedule are defined as:
TS nYY , TE Yn :Y

LYi , LYj :

∑ AT

Y
b ,nY

:

The earliest start time and the latest end time of the nYth prism ofindividual Y
Locations where the nY th prism of individual Y starts and ends
The sum of the duration of activities (b) in which individual Y

7engages between time TS nY and TEYn
Travel time from location i to location j by car
t car ,ij :YTrip _ Start _ Time n : Travel start time of individual Y in the nYth prismYYY

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 tobe 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 LXi to pick up
Yindividual X to location Lk , and has to be in location L before TEYn . If positive numbers ofboth Trip _ Start _ Time nX and Trip _ Start _ Time Yn exist subject to equations (1) and (6)through (10), individual X has the option to travel using pick-up mode by individual Y in
the trip.
Y
j

X

Y

Y

Trip _ Start _ Time YnY = TS nYY
X
nX

(6)

Trip _ Start _ Time + tcar, LX LX = TE nX
i

(7)

X

j

Trip _ Start _ Time YnY + tcar, LY LX + tcar, LX LX + tcar, LX LY = TE Yn(8)

TE nXX - TS nX - ∑ ATb, nX = tcar, LX LX

(9)

i

X

i

i

j

j

Y

j

X

i

j

TE YnY - TS nY - ∑ ATb ,nY = tcar, LY LX + tcar, LX LX + tcar, LX LY
Y

Y

i

i

i

j

j

(10)

j

Next, feasibility that pick- up mode is available for participation in the target out-of- home
activity 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 nXth 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 andXYnY

Trip _ Start _ Time exist subject to equations (1), (6) and (11) through (16), pick-up modeby individual Y is available for individual X in the trip only “to” the opportunity k, andindividual X can participate in the target activity and travel from location Lk to LXj bymode m other than pick-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 _ Time YnY + tcar, LY LX + tcar, LX L + tm , L LY = TE Yn(14)

TE nXX - TS nX - ∑ ATb, nX = tcar, LX L + Tk + tm, L L X

(15)

i

X

i

i

k

k

Y

j

X

i

k

k

j

TE YnY - TS nY - ∑ ATb ,nY = tcar, LY LX + tcar, LX L + tm , L LY
Y

Y

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 individua l Y overlap in space-time as shown in figure 2.3

8and positive numbers of both Trip _ Start _ Time nX and Trip _ Start _ Time Yn exist subject toequations (1), (6) and (17) through (22), pick- up mode is available for individual X in thetrip only “from” the opportunity k.
XY

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 _ Time YnY + tcar, LY L + tcar ,L LX + t car, LX LY = TE Yn(20)

TE nXX - TS nX - ∑ ATb, nX = tm, LX L + Tk + tcar ,L LX

(21)

i

X

k

k

j

j

Y

j

X

i

k

k

j

TE YnY - TS nY - ∑ ATb ,nY = tcar, LY L + tcar ,L LX + t car, L X LY
Y

Y

i

k

k

j

j

(22)

j

4. Pick-up mode is available in the trip both “to” and “from” the opportunity k inindividual 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 _ Time Yn exist subject toequations (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.
XY

Trip _ Start _ Time nXX + tcar, LX L = OSlk

(23)

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

(24)

i

i

k

k

Range to which prism
can be adjusted

time

OEkl

Individual X’s prism

OS kl

Individual Y’s prism(1) Available in the reported trip

Tk(2) Available in the trip “to” the opportunity k inthe prism
Individual X’s path

TEX nx

TEY nyIndividual Y’s path
Opening hoursof opportunity k
Trip_Start_TimeY ny

Subject to individual Y
being in this area

TSX nxTrip_Start_TimeX nx

TSY ny(3) Available in the trip “from” the opportunity kin the prism(4) Available in the trip both “to” and “from”
the opportunity k in the prism

Figure 2. Examples of pick-up mode available in SMAP
9

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

k

k

(25)

X

j

Trip _ Start _ Time YnY + tcar, LY LX + tcar, LX L + Tk + tcar, L LX + t car, LX LY = TE Yn(26)

TE nXX - TS nX - ∑ ATb, nX = tcar, LX L + Tk + tcar, L LX

(27)

i

X

i

i

k

k

j

j

Y

j

X

i

k

k

j

TE YnY - TS nY - ∑ ATb ,nY = tcar, LY LX + tcar, LX L + Tk + tcar, L LX + t car, L X LY
Y

Y

i

i

i

k

k

j

j

(28)

j

The algorithm described above only deal 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 ofparticipating in the target activity on a different day is also tested using this algorithm.
Since location and duration of the target activities is fixed in the model, alternative patterns
only of mode, time of 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 theindividual must do activities (b) within the same prism, but the algorithm could be
extended to within another prism of the day or another day.

2.3 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 patterngenerator can be represented on GIS. The part of user interface of SMAP program for aseries 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 map and time grid (temporalcomponents are drawn in another window), showing only appropriate informationcase 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 type, respondents can examine the alternatives referring to not only spatial andtemporal representation but also this quantitative information.

3. 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 of10physical capability constraints, for examp le, some of them can not drive a car, can not walkso long distance, feel resistance to using public transport and keep regular hours in daily
life in order to keep their health. Also coupling constraints are significant because theywant cooperation of other people, for example, they sometimes want to be picked up byother family members. Therefore activity-based analysis considering various constraints
should be effective to understand 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.

3.1 Survey description and data collection
On 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. Recently the trend is that car dependency
increases and large opportunities move from city center to suburbs, while the level of bus
service decreases.
In advance of main interview surveys, one-week activity diary data were collected offamily 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 originallya cellular phone system in Japan. When a person has PHS, data on location (longitude andlatitude) and time can be collected automatically at the specific time intervals. Eachpositional data has about 100 meters error. In this study, PHS data were used for help tospecify only locations where respondents had visited during the survey period, although
travel routes could be estimated using 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 andinformation about opening hours of other opportunities such as shopping sites werearranged by telephone interviews to each facility. Travel times by mode and time of day
between opportunities are calculated using detailed transportation network data as
described in section 2.
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 the11

observed behavior. Respondents are asked about their mode availability, maximum
walking time per trip and maximum travel cost per day for initial settings. After selecting atarget day from one-week (the activity diary survey period), spatial and temporalcomponents of the respondent’s activity pattern of the day is represented on GIS. Travel
pattern is represented as arrows between two locations in digital map. Time spent on“in- home” and “out-of-home” activities and “travel” is represented on different sections invertical timelines of 24 hours starting at 3:00 a.m. All activities of the day are classified
into three types as described in section 2 according to the respondent’s answers. All elderly
respondents are retired and non-working people in the survey, and then location of fixed
activities (activities (a) in the study) in their schedule is only their home: activities (a) aresleep 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 mode and prism. Theinterviewer 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, isavailable 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 patternof individual X on the timeline

Opening hours of opportunity
engaged in the target activity

Representation of activity patternof individual Y on the timeline

Representation of travel pattern of
Individual X on the map

The target activity

Figure 3. Example 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)
12-

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 setof alternative patterns after changes is calculated by the alternative activity-travel patterngenerator. New feasible patterns for participating in the target activity are represented in
GIS, and the respondent is asked to select one activity pattern, that is day of the week, timeof day and mode. For the respondent living with other members who can drive in thehousehold, “car passenger” mode is included in the choice set. Two persons’ activities are
represented simultaneously as shown in figure 3. If “main-respondent” selects analternative 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 (morningand 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 can be analyzed in depth subsequently.

3.2 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 theactivity-travel patterns and prism constraints by mode in space-time, the observed ones atleft side and the stated ones after changes of conditions at right side. In the figures, threeprisms 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 sizemeans 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
travel modes are walk, taxi and car passenger. On weekdays, her son works outside (atopportunity C in the figure 4) and pick- up mode is not available for her. A hospital(opportunity A) is open only on weekday, so she goes to the doctor by taxi on weekday. If
the hospital is available on holiday, 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 she goes 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-home activities in the prism formed between dinner and sleep.
The second example, in another household, is that in case community bus is available oncea 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 Ain figure 5) and goes to a hospital (opportunity B) by normal bus. When community bus isin service once a week, for example on Wednesday, she answered she would go to asupermarket (opportunity A in figure 5) 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 8:00 o’clock, so a prism
13

Age: 89
Sex: female
HH members: son, son’s wife and grandson

Available mode: walk, taxi and car passenger
Maximum walking time: 20min/trip
Maximum travel cost: 3,000yen/day

time

weekends

weekdays

Prism of theelderly person
Car
taxi
walk
Prism o f her
soncar
travel

A
B C
home

A
home

activity

B

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

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

before

after

prism
taxi

bus

walk

travel

A

B

A

home

B
home

Figure 5. In case of community bus available once a week
14

activity

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
long time, 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
temporal component 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 of activity planning, but improvement of temporal representation would
be needed for application to the elderly.

4. CONCLUSION
In this paper, Simulation Model for Activity Planning (SMAP): GIS-based gaming
simulation tool linked to alternative activity- travel pattern generator, was developed basedon 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 of timing, durationand location of activities, (2) pick-up mode is introduced explicitly considering two
persons’ prism constraints, and (3) multi-day activity- travel patterns can be considered.
Initial application of SMAP was to understand the elderly household activity-travel
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
examined considering pick- up mode and multi-day activities. GIS advantages wereeffectively used to conduct this enhanced gaming simulation technique.
There would be a wide range of application fields of SMAP other than this study ofactivity 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-pooling15

behavior considering activity scheduling of more than one person and travel blending
introducing trade-offs of multi-day activities. Detailed travel data including routes andspeeds collected using positioning technologies (global positioning system (GPS) andPHS) could also contribute to gaming simulation techniques, for example, responses oftravelers after pricing measures could be well understood on GIS. For these applications, itwould be useful that alternative 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 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 trave lcost but also of personal physical 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 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 for the 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 to be prepared andupdated periodically.

REFERENCES
Ahmed, A. K. F., Carapetis, S. and Taylor, M. A. P. (1995) Rural transport in Bangladesh:
Impact of non- motorized transport on households’ activity-travel patterns, Journal of
the Eastern Asia Society for Transportation Studies 1(3), pp.893-909.
Asakura, Y., Hato, E., Daito, T. and Tanabe, A. (2000) Monitoring travel behaviour using
PHS based location data, Journal of Infrastructure Planning and Management,
No.653/? -48, 95-104. (in Japanese)
Bowman, J. L. and Ben-Akiva, M. (1996) Activity based travel forecasting, Paper prepared
for a TMIP Conference on Activity Based Travel Forecasting, New Orleans,Louisiana June 2-5.
Burnett, P. and Hanson, S. (1982) The analysis of travel as an example of complex humanbehavior in spatially-constrained situations: Definition and measurement issues,
Transportation Research 16A(2), pp.87-102.
Burns, L. D. (1979) Transportation, temporal, and spatial components of accessibility,
Lexington: LexingtonBooks.
Golledge, R. G., Kwan, M. –P. and Gärling, T. (1994) Computational process modeling ofhousehold travel decisions using a geographical information system, Papers of theRegional Science 73(2), pp.99-117.
Hägerstrand, T. (1970) What about people in regional science?, Papers of the Regional
Science Association 24, pp.7-21.
Jones, P. M. (1980) Experience with Household Activity- Travel Simulator (HATS),
Transportation Research Record 765, pp.6-12.
16

Jones, P. M., Dix, M. C., Clarke, M. I. and Heggie, I. G. (1983) Understanding travel
behavior, Aldershot: Gower.
Kwan, M. -P. (1997) GISICAS: An activity-based travel decision support system using a
GIS- interfaced computational-process model, In Activity-Based Approaches to Travel
Analysis, edited by Ettema, D. F. and Timmermans, H. J. P., New York: Elsevier
Science, pp.117-134.
Kwan, M. -P. (1998) Space-time and integral measures of individual accessibility: A
comparative analysis using a point-based framework, Geographical Analysis 30(3),pp.191-216.
Landau, U., Prashker, J. N. and Alpern, B. (1982) Evaluation of activity constrained choicesets to shopping destination choice modelling, Transportation Research 16A(3),pp.199-207.
Lee-Gosselin, M. E. and Turrentine, T. S. (1997) Investigating past and future choice setsof car-users, with special reference to perceptions of safety, Paper prepared for the 8th
Meeting of the International Association of Travel Behaviour Research, Austin,
Texas.
Lenntorp, B. (1978) A time-geographic simulation model of individual activity
programmes. Carlstein, T., Parks, D. and Thrift, N. eds., Timing Space and Spacing
Time 2, Human Activity and Time Geography, pp.162-180.
Miller, H. J. (1999) Measuring space-time accessibility benefits within transportation
networks: Basic theory and computational procedures, Geographical Analysis 31(2),pp.187-212.
Nitta, Y., Mioshi, A. and Mori, Y. (1995) Basic study on special bus service planning for
improving mobility of elderly, Journal of Infrastracture Planning and Manageme nt
No.518 ? -28, pp.55-67. (in Japanese)
Ohmori, N., Muromachi, Y., Harata, N. and Ohta, K. (2000) Travel behavior data collectedusing GPS and PHS, Traffic and Transportation Studies, Proceedings of ICTTS
2000, pp. 851-858.
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 theEastern Asia Society for Transportation Studies 3(5), pp.139-153.
Ohmori, N., Muromachi, Y., Harata, N. and Ohta, K. (1998) A study on accessibility ofaged people considering daily activity pattern – A case study in Akita city,
Infrastructure Planning Review 15, pp. 671-678. (in Japanese)
Phifer, S. P., Neveu, A. J. and Hartgen, D. T. (1980) Family reactions to energy constraints,
Transportation Research Record 765, pp.12-16.
Recker, W. W. (1995) The household activity pattern problem: General formulation andsolution, Transportation Research 29B(1), pp.61-77.
Thill, J. -C. and Horowitz, J. L. (1997) Modelling non-work destinatio n choices withchoice sets defined by travel-time constraints, In Recent Developments in Spatial
Analysis: Spatial Statistics, Behavioural Modelling, and Computational
Intelligence, edited by Fischer, M. M. and Getis, A., pp.186-208, Heidelberg: Springer.

17