A multidimensional mixed ordered-response model for analyzing weekend activity participation

https://doi.org/10.1016/j.trb.2004.04.002Get rights and content

Abstract

The objective of this paper is to examine the frequency of participation of individuals in out-of-home non-work and non-school episodes over the weekend. A multivariate mixed ordered response formulation accommodating the effects of explanatory variables and capturing the dependence among the propensity to participate in different activity types is presented and applied using a San Francisco Bay area travel survey conducted in 2000. The results indicate the important effects of household sociodemographics (income, household structure, and bicycle ownership), individual sociodemographics (age, employment status, gender, and availability of driver's license), internet use, location effects, and day of week/seasonal effects. Interestingly, the results show that motorized vehicle ownership and urban form characteristics of the individual's neighborhood (land-use mix and density) do not have a statistically significant effect on stop-making propensity for any of the activity purposes. The lack of effects of these variables may be due to self-selection of individuals and households into neighborhoods based on their travel preferences. That is, individuals and households may locate themselves based on their motorized vehicle ownership preferences and mobility preferences. In addition to the effect of several variables on stop-making, the model also reveals substitution and complementarity effects among different activity types due to unobserved factors.

Introduction

The last decade has seen the emergence of the activity-based modeling approach as not only a behaviorally sound paradigm to analyze travel behavior, but also as a viable and implementable approach to forecasting travel demand (see Bhat and Koppelman, 1999; Pendyala and Goulias, 2002, and Arentze and Timmermans, 2004). Specifically, several operational analytic frameworks within the activity analysis paradigm have been formulated, and some metropolitan areas have even implemented these frameworks (Waddell et al., 2002; Castiglione et al., 2003).

While there has been substantial progress in the development and implementation of activity-based travel analysis efforts, almost all (if not all) of these efforts have focused on weekday activity-travel patterns. Even within the context of weekday activity-travel patterns, much emphasis has been placed on the patterns of workers (for example, see Bhat and Singh, 2000; Hamed and Mannering, 1993; Strathman et al., 1994; Mahmassani et al., 1997; Pendyala et al., 2002). However, the recognition that the analysis of non-worker activity-travel behavior also provides important input to transportation planning has led to an increasing focus on the activity-travel behavior of non-workers. For example, the frameworks of Bowman and Ben-Akiva (2000) and Kitamura and Fujii (1998) are applicable to both workers and non-workers. Bhat and Singh (2000), Bhat and Misra (2001), and Misra et al. (2003), on the other hand, emphasize the fundamental differences in the underlying factors and mechanisms influencing the activity and travel-related decisions of workers and non-workers, and propose exclusive frameworks for modeling the activity-travel decisions of workers and non-workers. But all these frameworks have examined worker and non-worker activity-travel behavior only on weekdays.

The objective of this paper is to examine the activity travel patterns of individuals on weekend days. To our knowledge, this is the first study to adopt an activity-based model framework to examine weekend activity episode participation. Bhat and Gossen (2004) analyzed weekend activity participation behavior, but restricted their attention to only recreational episodes. Besides, their analysis was rather specific and focused on the substitutions between in-home and out-of-home recreational activities. Parsons Brinckerhoff Quade and Douglas (PBQD) Inc. (2000) analyzed the dimensions of weekend travel, and compared and contrasted weekend and weekday travel. While providing several useful insights into weekend travel, the PBQD study was focused on a descriptive examination of travel patterns and not on modeling the activity-travel patterns as a function of relevant attributes of the activity-travel environment and individual/household demographics.

The motivation for the focus on weekend non-work and non-school activity-travel patterns in this paper is multifold. First, weekend travel has been increasing over time and constitutes approximately 26% of total trips during the week (Federal Highway Administration and Bureau of Transportation Statistics, 1995). Thus, the average percentage of total weekly trips during a weekend day (=26/2 or 13%) is about the same as the average percentage of total weekly trips during a weekday (=74/5 or 15%). This conclusion is also corroborated by the PBQD study in the New York metropolitan area, which found that the household daily person trip rate during the weekends (about 8 trips/household) is not substantially lower than that during the weekdays (between 8 and 9 trips/household). As expected, the PBQD study also found that the non-work person trip rate is higher on weekends than on the average weekday. Second, the PBQD study observed that about half of all weekend trips are undertaken during the midday period (10 a.m.–4 p.m.), compared to only about a third of all weekday trips undertaken during the same period. In an analysis of weekend activity-travel patterns in the San Francisco Bay area, Lockwood et al. (2003) also found that the volume of trips is consistently high and spread out during the midday hours of the weekend day. Such a high, sustained, level of traffic can contribute to traffic congestion and air quality problems, especially to the latter because vehicle starts during periods of high temperatures can lead to high emission rates. Third, the average trip distance is longer on weekend days compared to weekdays. According to the PBQD study in the New York metropolitan area, the average weekend day trip distance is 7.8 miles, while the average weekly trip distance is 7.1 miles. This, along with the trip rates discussed earlier, suggests a person miles of travel rate of 63.2/household on the average weekend day compared to 58.9–63.2/household on an average weekday. Thus, in terms of daily person miles of travel, each weekend day contributes more than or about the same as a weekday. Though this result in terms of person miles of travel does not translate to vehicle miles of travel (VMT), the important point is that weekend activities and travel also warrant careful attention for transportation planning and air quality analysis (Lockwood et al., 2003 estimate the weekend day VMT to be about 80% of the weekday VMT; the lower weekend day VMT is because of higher automobile occupancy rates for weekend trips).

To summarize, the analysis of weekend activities and their associated travel will facilitate the good design/planning of transportation systems, and the reliable evaluation of urban management policies directed toward traffic congestion alleviation and air quality improvement. The analysis of weekend activity-travel patterns is particularly important because the congested network links during the weekends may not be the same as the congested links during the weekdays. Further, and perhaps not unrelated to the above point, air quality violations for ozone are extending into the weekend days in many metropolitan areas. The focus on weekend activity-travel patterns in the current paper is motivated by the above considerations.

The rest of this paper is structured as follows. The next section discusses a representation and analysis framework for weekend activity travel patterns. Section 3 develops the mathematical formulation for one component of the weekend activity travel analysis framework focusing on the frequency of weekend day stop-making. Section 4 discusses the data and sample used in the empirical analysis of the paper. Section 5 presents the estimation results. Finally, Section 6 concludes the paper by summarizing the findings and identifying future research directions.

Section snippets

Representation scheme

The representation analysis framework proposed here for weekend activity-travel patterns is based on Bhat and Misra's (2001) framework for non-workers on weekdays. The framework has the following salient characteristics: (1) It considers all the relevant activity-travel attributes of individual weekend patterns, (2) It includes both the generation and scheduling of activity episodes, (3) It considers time as an all-encompassing continuous entity within which individuals make activity/travel

Mathematical formulation

For presentation ease, we develop the mathematical formulation for stop generation with only three activity purposes. Extension to any number of activity purposes is conceptually and mathematically straightforward. It has to be noted that the estimation and empirical results in the current paper are associated with stop generation for seven activity purposes and not three.

Let q be an index for individuals, and let l, m, and n be the indices for number of stops for each of the three activity

Data sources

The primary data source used for this analysis is the San Francisco Bay Area Travel Survey (BATS) conducted in 2000. This survey was designed and administered by MORPACE International Inc. for the Bay Area Metropolitan Transportation Commission. The survey collected information on all activity and travel episodes undertaken by individuals from over 15,000 households in the Bay Area for a two-day period (see MORPACE International, 2002 for details on survey, sampling, and administration

Variable specification

Several types of variables were considered in the weekend number of episodes model. These included household sociodemographics, individual sociodemographics, internet-use characteristics, location variables, and day of week/seasonal effects. The household sociodemographic characteristics considered in the specifications included household income, household structure, presence and number of children, number of household vehicles, number of bicycles in the household, household income, and

Summary and conclusions

This paper has examined the activity participation behavior of individuals over the weekend in the spirit of an activity-based travel modeling approach. Specifically, the paper has modeled the frequency of participation of individuals in seven out-of-home activity purposes over the weekend: physically active recreation, physically inactive recreation, maintenance shopping, other shopping, personal business, community activities, and pick-up/drop-off activities. The focus on weekend activity

Acknowledgements

The authors would like to thank Ken Vaughn and Chuck Purvis of the Metropolitan Transportation Commissions (MTC) in Oakland for providing help with data related issues. This research was funded, in part, by a grant from the Bureau of Transportation Statistics (BTS). The authors are grateful to Lisa Weyant for her help in typesetting and formatting this document. Six anonymous reviewers provided helpful comments on an earlier version of the paper.

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