Activity Pattern Analysis and Exploration: Transportation Research Record
Activity Pattern Analysis and Exploration: Transportation Research Record
Activity Pattern Analysis and Exploration: Transportation Research Record
1718
Activity Pattern
Analysis and Exploration
Travel Behavior Analysis and Modeling
Planning and Administration
Click on article title to reach abstract; abstracts link to full textclick on Full Text icon.
CONTENTS
Foreword
Identifying Decision Structures Underlying Activity Patterns: An Exploration of Data Mining Algorithms
Geert Wets, Koen Vanhoof, Theo Arentze, and Harry Timmermans
Conjoint-Based Model of Activity Engagement, Timing, Scheduling, and Stop Pattern Formation
Donggen Wang and Harry Timmermans
Activity-Travel Patterns of Nonworkers in the San Francisco Bay Area: Exploratory Analysis
Rajul Misra and Chandra Bhat
Are Travel Times and Distances to Work Greater for Residents of Poor Urban Neighborhoods?
Asad J. Khattak, Virginie J. Amerlynck, and Roberto G. Quercia
Evaluating the Effects of Traveler and Trip Characteristics on Trip Chaining, with Implications for
Transportation Demand Management Strategies
Brett Wallace, Jennifer Barnes, and G. Scott Rutherford
FOREWORD
The papers contained in this volume were among those presented at the 79th Annual Meeting of the Transportation Research
Board in January 2000. Nearly 1,600 papers were submitted by authors; more than 1,000 were presented at the meeting; and
approximately 600 were accepted for publication in the 2000 Transportation Research Record series. The published papers will
also be issued on CD-ROM, which will be available for purchase in late 2000. It should be noted that the preprint CD-ROM
distributed at the 2000 meeting contains unedited, draft versions of presented papers, whereas the papers published in the
2000 Records include author revisions made in response to review comments.
Starting with the 1999 volumes, the title of the Record series has included Journal of the Transportation Research Board to
reflect more accurately the nature of this publication series and the peer-review process conducted in the acceptance of papers
for publication. Each paper published in this volume was peer reviewed by members of the sponsoring committee listed on
page ii. Additional information about the Transportation Research Record series and the peer-review process can be found on
the inside front cover. The Transportation Research Board appreciates the interest shown by authors in offering their papers and
looks forward to future submissions.
FULL
TEXT
The utility-maximizing frameworkin particular, the logit modelis the dominantly used framework
in transportation demand modeling. Computational process modeling has been introduced as an
alternative approach to deal with the complexity of activity-based models of travel demand. Current
rule-based systems, however, lack a methodology to derive rules from data. The relevance and
performance of data-mining algorithms that potentially can provide the required methodology are
explored. In particular, the C4 algorithm is applied to derive a decision tree for transport mode choice
in the context of activity scheduling from a large activity diary data set. The algorithm is compared
with both an alternative method of inducing decision trees (CHAID) and a logit model on the basis of
goodness-of-fit on the same data set. The ratio of correctly predicted cases of a holdout sample is almost
identical for the three methods. This suggests that for data sets of comparable complexity, the accuracy
of predictions does not provide grounds for either rejecting or choosing the C4 method. However, the
method may have advantages related to robustness. Future research is required to determine the ability
of decision treebased models in predicting behavioral change.
FULL
TEXT
Although stated preference or conjoint-based models have recently found ample application in the
transportation literature, there have been no attempts to use this modeling approach to develop an
activity-based model of transport demand. The development of such a model, called COBRA, is
discussed. The model examines individuals choices on activity engagement, scheduling, and stop
pattern formation. The model is calibrated using experimental design data collected to examine the
potential effects of several policies recently proposed in the Netherlands. The modeling results indicate
that although people prefer activity schedules involving fewer home-based tours, they do not prefer
the combination of all individual trips into a single home-based tour. Furthermore, it is found that
individuals will change their activity engagement patterns only if government policies induce substantial
changes in individuals time availability.
FULL
TEXT
A methodology to estimate the location and size of space-time prisms that govern individuals activity
and travel is presented. Because the vertices of a prism are unobservable, stochastic frontier models are
formulated to locate prism vertices along the time axis using observable trip starting or ending times
as the dependent variable and commute characteristics, personal and household attributes, and area
characteristics as explanatory variables. Models are estimated successfully with coherent behavioral
indications. A mean difference of 1.46 h is found between the observed trip ending time and the
expected location of the terminal vertex for workers evening prisms. The estimation results aid in
enhancing the understanding of prism constraints by identifying the determinants of prism vertex
locations.
FULL
TEXT
Coast-to-Coast Comparison of
Time Use and Activity Patterns
Sachin Gangrade, Krishnan Kasturirangan, and Ram M. Pendyala
Department of Civil and Environmental Engineering, University of South Florida, ENB 118, 4202 East Fowler Avenue,
Tampa, FL 33620-5350.
FULL
TEXT
Activity-based travel analysis has been gaining increasing attention in travel demand research during
the past decade. Activity and trip information collected at the person level aids in understanding the
underlying behavioral patterns of individuals and the interactions among their activities and trips.
Activity and time use patterns across geographical contexts are compared. Such a comparison could
shed light on the differences and similarities in travel behavior that exist between areas. To accomplish
this objective, activity, travel, and time use information derived from surveys conducted in the
San Francisco Bay and Miami areas has been analyzed to identify differences in activity engagement
patterns across different sample groups. In general, it was found that activity and time use patterns are
comparable across the two areas as long as the commuting status and demographic characteristics of
the individuals are controlled for. In addition, the time-of-day distributions of various events such as
wake-up time, sleeping time, time of departure and arrival at home, and work start and end times were
compared. These events were considered important in defining the temporal constraints under which
people exercise activity and travel choices. Once again, it was found that the distributions followed
similar trends as long as the commuting status and the demographic characteristics of the individual
were controlled for. However, there were noticeable differences that merit further investigation.
Activity-Travel Patterns of
Nonworkers in the San Francisco Bay Area
Exploratory Analysis
Rajul Misra and Chandra Bhat
Department of Civil Engineering, ECJ 6.810, University of Texas at Austin, Austin, TX 78712.
FULL
TEXT
FULL
TEXT
A dynamic analysis of travelers attitudes, preferences, and values was carried out using three waves
of the Puget Sound Transportation Panel survey to investigate dynamics in traveler attitudes and
perceptions. An in-depth descriptive analysis was performed to examine the variations in attitudinal
ratings over time. A traditional one-way analysis of variance (ANOVA) method was used to explore for
similarities and differences in traveler attitudinal ratings across different waves of the panel survey.
A similar analysis was performed on the stayer sample (i.e., the respondents who participated in all
three waves of the panel used in this study). The ANOVA results show significant differences in mean
attitudinal ratings across the three waves of the panel survey. The differences in traveler attitudes and
perceptions among stayers, dropouts (respondents who leave the panel survey), and refreshments
(respondents who are newly recruited as the panel survey proceeds) were also captured. Finally,
differences in traveler attitudes and preferences across different modal market segments were
examined. The results indicate the need for greater consideration of attitudinal dynamics in
transportation planning and policy analysis.
FULL
TEXT
An innovative modeling framework to estimate household trip rates using 1995 Nationwide Personal
Transportation Survey data is presented. A generalized linear model with a mixture of negative
binomial probability distribution functions was developed on the basis of characteristics observed from
the empirical distribution of household daily trips. This model provides a more flexible framework and
a better model specification for analyzing household-specific trip production behavior. Compared with
traditional least squaresbased regression models, the parameter estimates from the proposed model
are more efficient. Although the mean accuracies from the two modeling approaches are comparable,
the mixed generalized linear model is more robust in identifying outliers due to its unsymmetric
prediction bounds derived from more correct model specification.
Determinants of Distance
Thresholds for Driving
Tommy Grling, Ole Boe, and Reginald G. Golledge
T. Grling and O. Boe, Department of Psychology, Gteborg University, P.O. Box 500, SE-40530 Gteborg, Sweden.
R. G. Golledge, Department of Geography, University of California, Santa Barbara, CA 93106.
FULL
TEXT
The hypothesis that habitual drivers become averse to exerting physical effort by walking was tested.
In support of the hypothesis, distance thresholds for driving measured in a Swedish (n = 60) and a
U.S. sample (n = 51) of undergraduates decreased with driving habit. Respondents in the U.S. sample
were more frequent drivers and had a lower distance threshold than respondents in the Swedish
sample.
FULL
TEXT
The commuting patterns of low-income urban residents are discussed. On the basis of the spatial
mismatch hypothesis, the question of whether central city low-income residents face an undue burden of
commuting cost (time and distance) to work compared with the rest of the population is examined. Data
from the 1995 Nationwide Personal Transportation Survey were used in the analysis. Models explaining
travel time and distance to work are combined with a model explaining the probability of being
employed on the basis of individual and neighborhood characteristics, thus correcting for sample
selectivity. In general, it was found that urban residents are less likely to work than their suburban
counterparts. Among the people who work, residents of low-income urban neighborhoods commute
longer and farther than residents of low-income suburban neighborhoods. The average differences for
the residents of the lowest-income neighborhoods are only 6 min and 2 mi (3 km). On the basis of the
value of time, it is concluded that these national differences are not too large. The undue commute
burden faced by residents of low-income neighborhoods may be shown to be a greater problem in some
metropolitan areas than in others, suggesting further research at the metropolitan or regional level.
FULL
TEXT
The analysis of the factors determining changes in travel behavior on the individual (or individual
household) level requires information on the behavior of individuals over time. Such transport panel
surveys are rarely available, particularly for a sufficiently long time period to examine such changes
more than cursorily. For the United Kingdom, none exists for other than limited regions. However, the
ongoing British Household Panel Survey (BHPS), begun in 1991, provides some information related
to transportspecifically, household car ownershipas well as information on the economic and
sociodemographic characteristics of the households surveyed. BHPS data for 1993 to 1996 are used to
analyze car ownership and the factors determining car ownership decisions on an individual household
level. As far as is known, this has not yet been done in any systematic manner. The relationship between
car ownership, income, and sociodemographic factors such as household composition, residential
location, and population density (persons per hectare in the local authority district in which the
household resides) is investigated. Both descriptive statistical measures and formal modeling
approaches, based on dynamic discrete choice models and panel data econometric techniques, are used.
FULL
TEXT
Travel behavioral data from five successive waves of the Puget Sound Transportation Panel were
examined to determine whether period effects or cohort effects have a significant effect in life-cycle
behavior. It was found that period and cohort effects may have a greater influence in household
life-cycle models than previously believed. The results bring into question current methods using
cross-sectional data analysis for life-cycle models, since predicted changes in activity behavior were
not observed in households that made life-cycle transitions. The results are not conclusive, since
other variables influencing activity behavior were not accounted for in the analysis.
FULL
TEXT
The relative effect that each of a wide variety of factors has on the extent to which a traveler will chain
trips was investigated. The objectives were to empirically determine which factors influence a travelers
tendency to chain two or more trips within one tour, as well as the relative significance of these
considerations; to more specifically determine the level of influence that urban centers have on trip
chaining; and to evaluate the potential effects on trip-chaining behavior of specific transportation
demand management (TDM) strategies through examination of variables that describe effects
associated with TDM. A negative binomial regression model was developed in which the number of
trips in a chain is related to household characteristics, traveler characteristics, trip characteristics,
and urban form. After the model was estimated, the significance of individual variables was analyzed.
Characteristics from each of these categories were found to be statistically significant. A number of the
significant variables help to describe effects of specific TDM strategies, and the relative effects of these
variables on trip-chaining behavior were addressed. Some of the variables representing TDM strategies
increased the level of trip chaining, whereas other variables decreased the level of chaining. Potential
policy conflicts between trip chaining and specific TDM programs are discussed.