Activity-based Travel Behavior Analysis Using the Disaggregate Logit Model
非集計ロジットモデルによる活動ベースの交通行動分析

Infrastructure Planning Lab. Renni Anggraini
Advisor Prof. Shoji Matsumoto
1. Introduction
During the last 25 years a large amount of research has been done to incorporate theinsights gained on activity-based travel theory into urban travel forecasting models. Some ofthe most advanced operational model systems capture the interrelated personal decisions
regarding the travel from home to one or more activity locations and to home back again. Atour can be defined as a sequence of trip segments that start at home and end at home, asdepicted in Figure 1. Tour-based systems were first developed in the late 1970s and 1980s in
the Netherlands, and were applied extensively there and elsewhere.
This research presents the tour-based model concept, which explicitly models anindividual’s choice of an entire day’s schedule, as briefly described by Bowman (1995, 1998).
The research analyzes the travel behavior of workers and non-workers considering nonworking activity, either Maintenance or Discretionary activity (MD). Hear, maintenanceactivities include business of household or individual (for example, pick up or drop off achild), and discretionary activities include those engaged in for pleasure, recreation orrefreshment. The significant distinction is that non-workers can freely travel every time
without the constraint of fixed activity, while workers have a fixed and subsistence activity
such as Work on Tour (WT) or Work at Home (WH). They may not perform maintenance or
discretionary activities very often.
The objectives of the research are to describe individual choice of doing maintenanceand discretionary activity, staying at home or just carrying out work activity for both ofworkers and non-workers, and how the constraint of subsistence activities influences
maintenance and discretionary activities for workers.

Case I

Case II
Stay athome

Case III
9:00-13:30

Home

7:00 -17:00

12:00 - 13:00

WorkPlace

Subsistence
BusinessPlace

Sub Tour
Lunch atRestaurant

14:00-16:00

17:00 - 18:00

Home

20:00-22:00

MD Primary
Destination

Dinner at
Restaurant

Supermarket

Figure 1. Tour Concept for Workers

12. Model System and Data
Figure 2 shows a hierarchical structure of the daily activity travel model comprising ofa series of the disaggregate nested logit models. Lower level choices are conditional on
decision at a higher level, and a higher-level decision is informed from a lower level throughthe expected maximum utility (Logsum) variable. The model system is designed as a seriesof disaggregate nested logit models assuming a hierarchy of model components based on thedemonstration project for travel model improvement in Portland, Oregon (TMIP (1997),Bowman et. al. (1998)).
For each hierarchical structure of workers and non-workers, three types of a submodel comprise the system: (1) daily activity pattern, (2) time of day, and (3) mode. The daily
activity pattern model includes decision of whether to make home-based trips for maintenanceand discretionary activity (MD on Tour=MDT), or stay at home (MD at Home=MDH, or No
MD=NMD). The time of day model includes decision of choosing time to travel, which isbroken down into 4 time periods, either in AM peak, Midday, PM peak, or in the evening.
Further, those 4 time periods are combined into 5 alternatives, Time 1, Time 2, Time 3, Time4, and Time 5. The mode choice model includes decision of choosing mode from 5 available
alternatives, either drive alone (DA), drive with passenger (DP), car passenger (CP), twowheels vehicle (TWV), or walk (W). The mode choice model is conditioned by choice of time,
and the time of day choice model is conditioned by choice of daily activity pattern.
The probability of a particular daily travel pattern p(daily travel) is therefore
expressed in the model as the product of marginal probability and conditional probability(Bowman, 1995, 1998).
p(daily travel) = p(pattern) p(time|pattern) p(mode|time),
The Hierarchy of Daily Activity Travel

Workers: WT & WH

Activity Pattern

Non-workers

WT & WH
without MD(NMD)MT

LogsumLogsumMDT

MDTDT

MDHMTDTTime of Day
LogsumLogsum
Time
1Time
2Time
3Time
4Time
5Time
1Time
2Time
3DA

DP

CPTime4Time
5ModeDA

DP

CP

TWV

W

Fig. 2. Daily Activity Travel Hierarchy
2TWVW

where p(pattern) is the probability of choosing activity pattern, while p(time|pattern) is theconditional probability of choosing time given choice of pattern, and p(mode|time) is theconditional probability of mode given time.
Survey of daily travel behavior was performed in November 1999 in the Nagaoka
Metropolitan Area, involving 4,944 households and more than 16,000 individuals. Samples of
only a householder (one person household) were removed from the data set. Samples ofhousehold members with two or more persons (a householder and spouse, and a householderwith family) were used in the analysis, which consist of 9,222 individuals.
3. Estimation Results
3.1 Model fitness
Table 1, Table 2 and Table 3 show estimated results of workers and non-workers modelsof choosing mode, time of day or activity pattern for maintenance and discretionary trips. The
log-likelihood ratio ρ2 results in the range between 0.101 and 0.375 for the entire models,
indicating that fitness of some models is not so good and satisfactory. The hit ratios are notuniform, that is some are high and some are very low.
3.2 Mode choice
Table 1 shows the model of mode choice for workers and non-workers. Both for theworkers and non-workers models, car competition in household is applied only to car-chosenalternatives: drive alone, drive with passenger, or car passenger, excluding two-wheels
vehicle and walking. It indicates the ratio of number of adults in household divided bynumber of cars in household. The result yields a positive sign for those three modes, whichmeans that as car competition in household becomes bigger, people tend to use more carrelated modes including car passengers.
For the workers model, drive to work place yields a positive sign. It indicates thetendency that workers who drive to work place would also choose car mode for MD activity.
Variable of travel to work place (not staying at home for work) yields a negative sign. It
indicates that they would not choose drive alone for primary MD destination. Workers ofreturn home after 5 PM may not choose car passenger for MD activity, but they prefer to use
other modes. A destination land use variable, JR station as destination zone presents apositive sign; it means that they choose car passenger to go to a city center zone around the JRstation for maintenance and discretionary activities. People of age 50 to 70 tend to choose
two-wheels vehicle and walking for primary MD destination. People who have more than
two cars in household tend to choose drive alone, and they might not choose driving withpassenger.
For the non-workers model, some of choice tendency are almost similar to the workers
model. People who make secondary tour result in a positive sign, indicating that they choose
drive alone or drive with passenger. Female with two or more adults in household and single
person with no spouse (live without spouse) will choose car passenger, two-wheels vehicle orwalking, and they may not choose drive with passenger because of its negative sign. JRstation as destination zone results in a negative sign for drive alone and two-wheels vehicle,
meaning that they would not choose those modes to go to city a center zone for primary MD
activity.
3.3 Time of day choice
Table 2 represents the model of time of day. It shows a slight difference between theworkers and non-workers models. The non-workers model gets feedback from the mode
choice model through a logsum variable, while the workers model not. Parameter of mode
choice logsum yields a small value, indicating decision of mode choice influences very3Table 1. Mode Choice
WORKERSNo
1Choice
DA2DP

3CP45TWVWNON-WORKERS

Variables

Coef.

T-StatFemale, 2+adults in HH-0.798-3.887

Variables
Constant

Coef.

T-Stat-2.347-8.728Car competition in HH

3.183

8.150

Car competition in household

4.957

15.998DA cost (円)-0.004-1.797

DA cost (円)-0.020-10.385

Drive to work place

2.0887

4.188

Male, less than 4 person in HH

0.704

2.507

No intermediate stop

-0.5109-2.324

Male, 2+workers in HH

0.668

2.170

Travel to work place-0.7970-2.379JR station as destination zone

-0.441-3.0232+cars in household

0.2697

1.189

Secondary tour

0.902

5.072

Male, less than 4 person in HH
Drive to work place
Secondary tour
Car competition in HH
DP cost (円)Male, 1+workers in HH
Leave home during midday
2+cars in household
Female, 4+person in HH

0.4139
2.293
0.353
2.719
-0.0040.832-0.524-0.409
0.473

2.492
5.545
1.775
6.018
-1.8403.434-2.694-1.523
1.791

Secondary tour
Car competition in household
DP cost (円)Female, kids under 12 in HH
Live without spouseAge 55-70
Female, 2+workers in HH
Female, 2+adults in HH

1.013
4.644
-0.0201.125-1.515-0.347-1.184-1.042

4.821
12.836-10.586
4.657-1.959-1.855-3.188-3.261

No intermediate stop
Car competition in HH-1.3350.859-5.3992.257

Constant-0.004-1.618Car competition in HH-4.543
1.693-10.312CP cost (円)-10.094

3.986

JR station as dest. zone

0.390

1.966

CP cost (円)-0.022

Female, no kids in HH0.766

3.248

Female, 2+adults in HH

3.200

7.783

Leave home during midday-0.659-2.783

Female, 1+workers in HH-1.020-4.641

Drive to work place

1.383

2.406

Residential area as dest. zone

0.445

2.403

Return home after 5 PM from WP-0.760-1.6242+cars in HH

0.457

1.930

No intermediate stop
Constant
TWV cost (円)Female, 2+adults in HH
Age 50-70
Male, 1+workers in HH
Female, no kids in HH-1.2481.784-0.004-1.1480.640-2.114-0.398-4.8192.113-2.634-1.418
2.758-2.642-1.332

Constant
TWV cost (円)Female, 2+adults in HH
JR station as destination zone
Fewer cars than adult in HH
Female, husband worker-1.161-0.0160.850-0.2750.515
0.548-3.664-11.2192.621-2.0792.918
3.274

Constant
W cost (円)Female, 4+person in household
Age 50-70

0.429-0.001
0.401
0.433

1.143
-2.727
1.628
2.202

Live without spouse

1.167

2.896

Female, less than 4 person in HH-0.265-1.433

No kids in HH
W cost (円)Live without spouse
Male, only adults in HH
Male, 4+person in HH-0.292-0.0031.083-1.071-0.823-1.611-11.274
2.941-3.693-2.780No. of obs: 1181

No. of obs: 1654

Rho-squared:0.259

Rho-squared:0.219

DA Hit Ratio: 0.871

DA Hit Ratio: 0.607

DP Hit Ratio: 0.239

DP Hit Ratio: 0.209

CP Hit Ratio: 0.200

CP Hit Ratio: 0.302

TWV Hit Ratio: 0.246

TWV Hit Ratio: 0.201

W Hit Ratio: 0.325

W Hit Ratio: 0.696

slightly on the choice of time of day. For the workers model, workers who return home after
5 PM result in a significant value, and they tend to perform MD activity during time 5 or in
the evening. Secondary tour variable results in a big value for non-workers, and they tend tochoose time 1 or in the morning. Maintenance tour or Discretionary tour variables areapplied to all alternatives in order to give feedback of logsum into the activity pattern choicemodel.

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3.4 Activity pattern choice
Table 3 presents the models of activity pattern choice for workers and non-workers.
Workers who drive to work place tend to choose DT or NMD rather than MT, because of anegative sign. Workers who work at home tend to choose MT and DT. Male worker with lessthan four people in household and male worker with more than two adults in household tend
to choose MT or NMD rather than DT.
Time choice logsum for the non-workers model results in a very big value, indicating
Table 2. Time of Day
WORKERNo12

345

ChoiceTime 1Time 2Time 3Time 4

Time 5

Variables

NON-WORKER

CoefT-Stat

Variables

Coef.
0.047
1.298
-0.346

T-Stat1.0787.249-2.396

0.518

4.025

Constant
Age under 50

0.455-0.4412.811-2.703

Residential area as dest. zone
Return home after 5 PM fromWP
No stop to/from Work Place
WH with Discretionary on
TourConstant
Secondary tour
Male, only adults inhouseholdMale, 4+person in household
WH with Maintenance on
Tour

0.206

1.304

Mode Choice Logsum
Secondary tour
Female, husband worker
Grandfather/grand mother age
over 70

1.920-0.6083.902-1.972

Male, 4+person in HH
Discretionary Tour

0.853
-0.2683.716-1.9010.340
0.327-1.9661.996
1.619-7.949Male, 4+person in household
No intermediate stop

0.701
-0.871

2.675-5.918

0.364
0.660

2.256
3.777

Female, no kids in HH
Origin zone dummy-0.381
0.462-2.426
2.610

0.596

3.366

Residential area as dest. zone

0.404

2.343

0.669
-0.764

3.119-3.705Discretionary Tour
Secondary tour
Female, 1+workers in HH

0.530
0.719
-0.305

3.142
4.096-2.494-0.254-1.123

Maintenance Tour

1.348

8.883

Constant
Secondary tour
Male, 1+workers inhousehold
Female, less than 4 person in
HH

0.320

1.370

No stop to/from Work Place

0.409

1.147

Constant
Return home after 5 pm fromWP
Female, less than 4 person inHHFemale, no kids in household
JR station as destination zone
No cars in household-0.215-1.798

Female, less than 4 person in
HH

0.220

1.246

-1.2403.534

Male, wife non-worker-0.344-2.077-0.536
1.138-0.4170.481-2.282
1.264-1.0472.415

Age under 50
Children over 12 in HH
Female, 2+adults in HH
Maintenance Tour-0.393-0.356-0.3661.226-2.346-1.517-1.8786.684-0.267

3.119-0.845-3.705

3.285

7.293

Male, 2+adults in HH,
1+nonworkers

1.398-6.631-0.492-0.726-2.404-1.353

Female, kids under 12 in HH
Secondary Tour
Female, less than 4 person in
HH-0.7360.506-3.4372.102-1.126-7.236

Maintenance Tour

1.114

6.229

Female, 2+workers in HH
WT with Maintenance on
Tour
Return home after 5 pm fromWP
Male, 2+adults in HH,
1+nonworkers
No cars in household

No. of obs: 1181

No. of obs: 1654

Rho-squared:0.134

Rho-squared:0.101

Time 1 Hit Ratio 0.343

Time 1 Hit Ratio: 0.409

Time 2 Hit Ratio: 0.671

Time 2 Hit Ratio: 0.351

Time 3 Hit Ratio: 0.291

Time 3 Hit Ratio: 0.489

Time 4 Hit Ratio: 0.223

Time 4 Hit Ratio: 0.191

Time 5 Hit Ratio: 0.481

Time 5 Hit Ratio: 0.224

5Table 3. Activity Pattern
No123

WORKER
Variables

Choice
MTDT

NMD/
MDH

NON-WORKER
Variables
Coef.

Coef.

T-StatFemale, kids under 12 in HH

0.771

5.025

Time Choice Logsum

0.984

T-Stat20.665

Drive to work place-0.822-5.768

Female, kids under 12 in HH

0.863

6.364

Return home after 5 PM-2.304-15.648

Female, less than 4 person in HH-0.268-3.770Male, 2+workers in household-0.120-1.156

Female, 2+non-workers in HH-0.497-5.938Working at home
Male, less than 4 person in HH

0.279-1.781

3.027-17.469

Age under 50
Time Choice Logsum

0.125
0.984

1.558
20.665Working at home

0.897

9.947

Male, less than 4 person in HH

0.302

1.465

Male, 2+adults in HH, 1+nonworkers-1.664-15.617

Male, only adults in HH-0.270-1.232

Female, husband worker
Female, husband worker
Grandfather/grand mother age over 70 in
HH-1.390
0.838-10.106
7.611

Male, 2+non-workers in HH
Live without spouse-0.4501.864-3.346
11.723

0.522

6.227

Female, kids under 12 HH

1.417

9.787

Stop to/from home

0.624

4.226

No. of obs :.5662

No. of obs : 3478

Rho-squared: 0.375

Rho-squared: 0.103

MT Hit Ratio : 0.357

MT Hit Ratio: 0.155

DT Hit Ratio : 0.114DT Hit Ratio : 0.475

NMD Hit Ratio : 0.874

MDH Hit Ratio: 0.720

decision of time influences significantly the choice of MT and DT. Non-workers of female
with kids under 12 in household tend to choose MT and MDH rather than DT.
4. Conclusions
For the workers model of maintenance and discretionary activity, activity pattern, timeof day and mode models were estimated without logsum variables, because parameters oflogsum did not fit the acceptable range between 0 and 1, and their estimation had very large
standard errors. Working conditions influence mode, time of day and activity pattern choicein performing maintenance and discretionary activity, but without any feedback interaction.
Workers who drive to work place would choose drive alone or drive with passenger, and theytend to perform discretionary activity or working tour only rather than maintenance activity.
Workers who return home after 5 PM from work place would carry out MD activities in theevening.
For the non-workers model, activity pattern, time of day and mode choice wereestimated as a sequential nested logit model system. Parameter of mode choice logsum is verysmall, 0.048; therefore mode choice slightly influences time choice decision. Time choicelogsum computed for activity pattern model has a significant value, 0.98, meaning that thedecision of activity pattern is considerably influenced by time of day choice.
References.
Bowman, J.L. (1995). Activity based travel demand model system with daily activity schedules,
Master of Science, MIT.
Bowman, J.L. (1998). The day activity schedule approach to travel demand analysis, Ph. D
Dissertation, MIT.
Bowman, John L., Mark A. Bradley, Yoram Shiftan, T. Keith Lawton and Moshe E. Ben-Akiva(1998). Demonstration of an activity based model system for Portland, Conference on Transport
Research, July 12-17, 1998, Antwerp, Belgium.
TMIP (1997). A System of Activity-Based Models for Portland, Oregon, A Demonstration Project for
FHWA Travel Model Improvement Program (TMIP), December 1997.

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