An analysis of weekly out-of-home discretionary activity participation and time-use behavior

Abstract

Activity-travel behavior research has hitherto focused on the modeling and understanding of daily time use and activity patterns and resulting travel demand. In this particular paper, an analysis and modeling of weekly activity-travel behavior is presented using a unique multi-week activity-travel behavior data set collected in and around Zurich, Switzerland. The paper focuses on six categories of discretionary activity participation to understand the determinants of, and the inter-personal and intra-personal variability in, weekly activity engagement at a detailed level. A panel version of the Mixed Multiple Discrete Continuous Extreme Value model (MMDCEV) that explicitly accounts for the panel (or repeated-observations) nature of the multi-week activity-travel behavior data set is developed and estimated on the data set. The model also controls for individual-level unobserved factors that lead to correlations in activity engagement preferences across different activity types. To our knowledge, this is the first formulation and application of a panel MMDCEV structure in the econometric literature. The analysis suggests the high prevalence of intra-personal variability in discretionary activity engagement over a multi-week period along with inter-personal variability that is typically considered in activitytravel modeling. In addition, the panel MMDCEV model helped identify the observed socioeconomic factors and unobserved individual specific factors that contribute to variability in multi-week discretionary activity participation.

Building a validation measure for activity-based transportation models based on mobile phone data

“…This motivates the development of separate representations for these two types of individuals' behavior. In this study, only the activitytravel behavior of workers is analyzed, and the representation of their daily sequences described in the research by Spissu et al (2009) is adopted. In this representation (see Fig.…”

Stated response and multiple discrete-continuous choice models: Analyses of residuals

“…The impact of demographics, built environment attributes, vehicle characteristics and gasoline prices on the same issue are analyzed in . Pinjari et al (2009) analyzed residential self-selection effects in time-use models and Spissu et al (2009) presented an analysis of weekly out-of-home activity participation. Copperman and Bhat (2007) analyzed the determinants of childrens week end activity participation.…”

Annotating mobile phone location data with activity purposes using machine learning algorithms

“…This generates a high level of spatial stability and temporal periodicities in activity-travel behavior (e.g. Hannes et al, 2010;Spissu et al, 2009) as well as a certain sequential order of the activities (e.g. Wilson, 2008).…”

“…Leisure activities Leisure activities are often carried out in various places and at a flexible time schedule (e.g. Spissu et al, 2009); they have the lowest level of spatial and temporal regularities and thus are the most challengeable to predict. An examination into the falsely predicted leisure locations points out two representative cases.…”