An Analysis of Weekend Work Activity Patterns in the San Francisco Bay Area

The purpose of this study is to examine the spatial and temporal characteristics of weekend work episodes. Specifically, we examine whether individuals work over the weekend and, if they work, whether they work at home or outside the home. We also model the time of day of weekend work. The empirical analysis in the paper is based on the 2000 San Francisco Bay Area Travel Survey. The results indicate the important effects of day of week/seasonal effects, individual demographics, work-related variables, household characteristics, and location variables on weekend work participation characteristics. The models estimated in the paper may be embedded within a larger weekend activity-travel pattern forecasting model system.     

On modeling telecommuting behavior: option, choice, and frequency

The current study contributes to the already substantial scholarly literature on telecommuting by estimating a joint model of three dimensions—option, choice and frequency of telecommuting. In doing so, we focus on workers who are not self-employed workers and who have a primary work place that is outside their homes. The unique methodological features of this study include the use of a general and flexible generalized hurdle count model to analyze the precise count of telecommuting days per month, and the formulation and estimation of a model system that embeds the count model within a larger multivariate choice framework. The unique substantive aspects of this study include the consideration of the “option to telecommute” dimension and the consideration of a host of residential neighborhood built environment variables. The 2009 NHTS data is used for the analysis, and allows us to develop a current perspective of the process driving telecommuting decisions. This data set is supplemented with a built environment data base to capture the effects of demographic, work-related, and built environment measures on the telecommuting-related dimensions. In addition to providing important insights for policy analysis, the results in this paper indicate that ignoring the “option” dimension of telecommuting can, and generally will, lead to incorrect conclusions regarding the behavioral processes governing telecommuting decisions. The empirical results have implications for transportation planning analysis as well as for the worker recruitment/retention and productivity literature.     

A household-level activity pattern generation model with an application for Southern California

This paper develops and estimates a multiple discrete continuous extreme value model of household activity generation that jointly predicts the activity participation decisions of all individuals in a household by activity purpose and the precise combination of individuals participating. The model is estimated on a sample obtained from the post census regional household travel survey conducted by the South California Association of Governments in the year 2000. A host of household, individual, and residential neighborhood accessibility measures are used as explanatory variables. The results reveal that, in addition to household and individual demographics, the built environment of the home zone also impacts the activity participation levels and durations of households. A validation exercise is undertaken to evaluate the ability of the proposed model to predict participation levels and durations. In addition to providing richness in behavioral detail, the model can be easily embedded in an activity-based microsimulation framework and is computationally efficient as it obviates the need for several hierarchical sub-models typically used in extant activity-based systems to generate activity patterns.     

Modeling the choice continuum: an integrated model of residential location,auto ownership,bicycle ownership,and commute tour mode choice decisions

The integrated modeling of land use and transportation choices involves analyzing a continuum of choices that characterize people’s lifestyles across temporal scales. This includes long-term choices such as residential and work location choices that affect land-use, medium-term choices such as vehicle ownership, and short-term choices such as travel mode choice that affect travel demand. Prior research in this area has been limited by the complexities associated with the development of integrated model systems that combine the long-, medium- and short-term choices into a unified analytical framework. This paper presents an integrated simultaneous multi-dimensional choice model of residential location, auto ownership, bicycle ownership, and commute tour mode choices using a mixed multidimensional choice modeling methodology. Model estimation results using the San Francisco Bay Area highlight a series of interdependencies among the multi-dimensional choice processes. The interdependencies include: (1) self-selection effects due to observed and unobserved factors, where households locate based on lifestyle and mobility preferences, (2) endogeneity effects, where any one choice dimension is not exogenous to another, but is endogenous to the system as a whole, (3) correlated error structures, where common unobserved factors significantly and simultaneously impact multiple choice dimensions, and (4) unobserved heterogeneity, where decision-makers show significant variation in sensitivity to explanatory variables due to unobserved factors. From a policy standpoint, to be able to forecast the “true” causal influence of activity-travel environment changes on residential location, auto/bicycle ownership, and commute mode choices, it is necessary to capture the above-identified interdependencies by jointly modeling the multiple choice dimensions in an integrated framework.     

An empirical analysis of children’s after school out-of-home activity-location engagement patterns and time allocation

Children are an often overlooked and understudied population group, whose travel needs are responsible for a significant number of trips made by a household. In addition, children’s travel and activity participation during the post-school period have direct implication for adults’ activity-travel patterns. A better understanding of children’s after school activity-travel patterns and the linkages between parents and children’s activity-travel needs is necessary for accurate prediction and forecasting of activity-based travel demand modeling systems. In this paper, data from the 2002 Child Development Supplement of the Panel Study of Income Dynamics is used to undertake a comprehensive assessment of the post-school out-of-home activity-location engagement patterns of children aged 5–17 years. Specifically, this research effort utilizes a multinomial logit model to analyze children’s post-school location patterns, and employs a multiple discrete–continuous extreme value model to study the propensity of children to participate in, and allocate time to, multiple activity episode purpose-location types during the after-school period. The results show that a wide variety of demographic, attitudinal, environmental, and others’ activity-travel pattern characteristics impact children’s after school activity engagement patterns.