Dynamic model of activity-type choice and scheduling

This paper presents a model for the choice of activity-type and timing, incorporating the dynamics of scheduling, estimated on a six-week travel diary. The main focus of the study is the inclusion of past history of activity involvement and its influence on current activity choice. The econometric formulation adopted, explicitly accounts both for correlation across alternatives and for state dependency. The results indicate that behavioral variables are superior to socio-economic variables and that consideration of the correlation pattern over alternatives clearly improves the fit of the model. This is a first but significant contribution to changing the current static demand models into dynamic activity based ones. The availability of other multi-week travel surveys and the progress made recently on advanced econometric techniques should encourage the transferability of this study to different regions or model scale.

[2]  C. Cirillo,et al.  Mixed Logit Mode Choice Model Using Panel Data: Accounting for Systematic and Random Variations in Responses and Preferences , 2008 .

[3]  Chandra R. Bhat,et al.  A generalized multiple durations proportional hazard model with an application to activity behavior during the evening work-to-home commute , 1996 .

[4]  R. Schlich Verhaltenshomogene Gruppen in Längsschnitterhebungen , 2004 .

[5]  E. I. Pas Weekly travel-activity behavior , 1988 .

[6]  Joffre Swait,et al.  Distinguishing taste variation from error structure in discrete choice data , 2000 .

[7]  Harry Timmermans,et al.  Albatross version 2: A learning-Based Transportation Oriented Simulation System , 2005 .

[8]  Kai Nagel,et al.  Parallel implementation of the TRANSIMS micro-simulation , 2001, Parallel Comput..

[9]  Chris Caplice,et al.  DAILY VARIATION OF TRIP CHAINING, SCHEDULING, AND PATH SELECTION BEHAVIOR OF WORK COMMUTERS , 1991 .

[10]  S. Srinivasan,et al.  A multidimensional mixed ordered-response model for analyzing weekend activity participation , 2005 .

[11]  Joan L. Walker Extended discrete choice models : integrated framework, flexible error structures, and latent variables , 2001 .

[12]  E. I. Pas,et al.  Socio-demographics, activity participation and travel behavior , 1999 .

[13]  Chandra R. Bhat,et al.  A comprehensive daily activity-travel generation model system for workers , 2000 .

[14]  K. Train,et al.  Mixed Logit with Repeated Choices: Households' Choices of Appliance Efficiency Level , 1998, Review of Economics and Statistics.

[15]  E. I. Pas,et al.  Intrapersonal variability in daily urban travel behavior: Some additional evidence , 1995 .

[16]  Eric J. Miller,et al.  ILUTE: An Operational Prototype of a Comprehensive Microsimulation Model of Urban Systems , 2005 .

[17]  P. Toint,et al.  Application of an Adaptive Monte Carlo Algorithm to Mixed Logit Estimation , 2006 .

[18]  K. Srinivasan,et al.  Longer-term changes in mode choice decisions in Chennai: a comparison between cross-sectional and dynamic models , 2007 .

[19]  Hani S. Mahmassani,et al.  Experiments with departure time choice dynamics of urban commuters , 1986 .

[20]  Moshe Ben-Akiva,et al.  PII: S0965-8564(99)00043-9 , 2000 .

[21]  Paulina Greeven,et al.  Econometric Calibration of the Joint Time Assignment-Mode Choice Model , 2008, Transp. Sci..

[22]  H. M. Zhang A mathematical theory of traffic hysteresis , 1999 .

[23]  Thomas F. Golob,et al.  A Simultaneous Model of Household Activity Participation and Trip Chain Generation , 1999 .

[24]  M. Clarke,et al.  The significance and measurement of variability in travel behaviour , 1988 .

[25]  Ta Theo Arentze,et al.  Experiences with developing ALBATROSS: a learning-based transportation oriented simulation system , 1998 .

[26]  Hjp Harry Timmermans,et al.  A learning-based transportation oriented simulation system , 2004 .

[27]  Gitakrishnan Ramadurai,et al.  Dynamics and Variability in Within-Day Mode Choice Decisions: Role of State Dependence, Habit Persistence, and Unobserved Heterogeneity , 2006 .

[28]  K. Axhausen,et al.  Evidence on the distribution of values of travel time savings from a six-week diary , 2006 .

[29]  Konstadinos G. Goulias,et al.  Application of Poisson Regression Models to Activity Frequency Analysis and Prediction , 1999 .

[30]  Khandker Nurul Habib,et al.  Modelling daily activity program generation considering within-day and day-to-day dynamics in activity-travel behaviour , 2008 .

[31]  Kay W. Axhausen,et al.  Fatigue in long-duration travel diaries , 2007 .

[32]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .

[33]  Moshe Ben-Akiva,et al.  Dynamic Model of Weekly Activity Pattern , 1986, Transp. Sci..

[34]  Satoshi Fujii,et al.  FAMOS: The Florida activity mobility simulator , 2005 .

[35]  K. Axhausen,et al.  Observing the rhythms of daily life: A six-week travel diary , 2002 .

[36]  Chandra R. Bhat,et al.  Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns , 2004 .

[37]  S. Hanson,et al.  Systematic variability in repetitious travel , 1988 .

[38]  James J. Heckman,et al.  ECONOMETRIC ANALYSIS OF LONGITUDINAL DATA , 1986 .

[39]  Fred Mannering,et al.  Modeling Travelers' Postwork Activity Involvement: Toward a New Methodology , 1993, Transp. Sci..

[40]  Kenneth E. Train,et al.  Discrete Choice Methods with Simulation , 2016 .