Studying Differences of Household Weekday and Weekend Activities

A desired activity-based travel demand modeling framework should be able to address both weekday and weekend activities. However, a literature review shows that previous research efforts have mostly focused on weekday, not weekend, activities, and that little or no research exists to quantify the differences between the two. The best knowledge to date is limited to weekday and weekend activities that start at different times of the day and have different participation rates. This paper aims to fill the gap by studying the differences between weekday and weekend activities in Calgary, Canada, in terms of participation rates, starting times, duration, and inferred location choices. First, statistics related to these attributes were computed for 10 types of weekday and weekend activities (these were found to differ). Second, log-rank and Wilcoxon tests were used to prove further that common types of weekday and weekend activities tend to follow different survival functions. Third, best-fit duration models were explored for each type of weekday and weekend activity and compared with each other. It was found that Weibull and log-normal were chosen as the best-fit models for nearly all weekday and weekend activities. The best-fit duration models for the same types of weekday and weekend activities (e.g., shopping) were found to be different in either underlying distribution or estimated parameters. This study clearly shows that the weekend activities differ from their weekday counterparts and suggests that they be treated separately in activity-based modeling frameworks.

[1]  R. Pendyala,et al.  A structural equations analysis of commuters' activity and travel patterns , 2001 .

[2]  R. Kitamura APPLICATIONS OF MODELS OF ACTIVITY BEHAVIOR FOR ACTIVITY BASED DEMAND FORECASTING , 1997 .

[3]  Chandra R. Bhat,et al.  DURATION MODELING. IN: HANDBOOK OF TRANSPORT MODELLING , 2000 .

[4]  Chandra R. Bhat,et al.  An Exploratory Analysis of Weekend Activity Patterns in the San Francisco Bay Area , 2004 .

[5]  Chandra R. Bhat,et al.  An Analysis of Weekend Work Activity Patterns in the San Francisco Bay Area , 2007 .

[6]  Gordon Johnston,et al.  Statistical Models and Methods for Lifetime Data , 2003, Technometrics.

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

[8]  Dick Ettema,et al.  A SIMULATION MODEL OF ACTIVITY SCHEDULING BEHAVIOUR , 1992 .

[9]  P. Lewis,et al.  Distribution of the Anderson-Darling Statistic , 1961 .

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

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

[12]  You-Lian Chu,et al.  Modeling Workers' Daily Nonwork Activity Participation and Duration , 2005 .

[13]  John Douglas Hunt,et al.  Nature of Weekend Travel by Urban Households , 2005 .

[14]  A. M. Lockwood,et al.  Exploratory Analysis of Weekend Activity Patterns in the San Francisco Bay Area, California , 2005 .

[15]  Fred L. Mannering,et al.  Occurence, frequency, and duration of commuters' work-to-home departure delay , 1990 .

[16]  A. M. Lockwood,et al.  On Distinguishing Between Physically Active and Physically Passive Episodes and Between Travel and Activity Episodes: An Analysis of Weekend Recreational Participation in the San Francisco Bay Area , 2004 .

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

[18]  Fred L. Mannering,et al.  HAZARD-BASED DURATION MODELS AND THEIR APPLICATION TO TRANSPORT ANALYSIS. , 1994 .

[19]  Chandra Bhat,et al.  Modeling the Commute Activity-Travel Pattern of Workers: Formulation and Empirical Analysis , 2001, Transp. Sci..