Publication Detail
Modeling the Individual Consideration of Travel-Related Strategies
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UCD-ITS-RR-03-03 Research Report Download PDF |
Suggested Citation:
Cao, Xinyu and Patricia L. Mokhtarian (2003) Modeling the Individual Consideration of Travel-Related Strategies. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-03-03
This report is one of a series of research documents produced by an ongoing study of individuals' adoption and consideration of travel-related strategies in response to congestion.
It is widely recognized that congestion has serious consequences for sustainable development. Governments have been adopting a wide range of measures to alleviate congestion. However, the limited effectiveness of these strategies has been puzzling policy makers. The gap between policy assumptions and individuals' behaviors is believed to greatly affect the effectiveness of such strategies. Also, the dynamic nature of individuals' response to congestion further exacerbates the discrepancy between assumption and reality. Therefore, the primary goal of this report is to develop disaggregate discrete choice models for the consideration of travel-related strategies and examine any patterns emerging across the models, in order to better understand the determinants of individuals' consideration of each strategy, to improve predictions of the effectiveness of proposed policies, and to help design more effective policies. In so doing we also explore the relationship between the earlier adoption of a strategy and its reconsideration, helping us to further understand the dynamic nature of individuals' behavioral response to congestion.
The data for this series of studies come from a 1998 mail-out/mail-back survey of 1,904 residents in three neighborhoods in the San Francisco Bay Area: Concord and Pleasant Hill representing two different kinds of suburban neighborhoods comprising about half the sample, and an area defined as North San Francisco representing an urban neighborhood comprising the remainder. The questions in the survey were classified into 11 categories of variables: objective mobility, subjective mobility, relative desired mobility, travel liking, travel attitudes, personality, lifestyle, excess travel, adoption and consideration of travel-related strategies, mobility constraints, and demographic characteristics. For this study, we chose to focus on commuting workers since they contribute most heavily to peak-period congestion, and are likely to be the most active in the adoption and consideration of travel-related strategies; the subset of 1283 cases that consists of commuting workers with relatively complete responses to key questions is used in this analysis. Binary logit models were developed for the consideration of each individual travel-related strategy. Each dependent variable, consideration of the given strategy, was defined as a binary variable, and the other variables were viewed as potential explanatory variables. Generally, the significance level 0.05 was used to incorporate or release variables in the final "best" model.
It is widely recognized that congestion has serious consequences for sustainable development. Governments have been adopting a wide range of measures to alleviate congestion. However, the limited effectiveness of these strategies has been puzzling policy makers. The gap between policy assumptions and individuals' behaviors is believed to greatly affect the effectiveness of such strategies. Also, the dynamic nature of individuals' response to congestion further exacerbates the discrepancy between assumption and reality. Therefore, the primary goal of this report is to develop disaggregate discrete choice models for the consideration of travel-related strategies and examine any patterns emerging across the models, in order to better understand the determinants of individuals' consideration of each strategy, to improve predictions of the effectiveness of proposed policies, and to help design more effective policies. In so doing we also explore the relationship between the earlier adoption of a strategy and its reconsideration, helping us to further understand the dynamic nature of individuals' behavioral response to congestion.
The data for this series of studies come from a 1998 mail-out/mail-back survey of 1,904 residents in three neighborhoods in the San Francisco Bay Area: Concord and Pleasant Hill representing two different kinds of suburban neighborhoods comprising about half the sample, and an area defined as North San Francisco representing an urban neighborhood comprising the remainder. The questions in the survey were classified into 11 categories of variables: objective mobility, subjective mobility, relative desired mobility, travel liking, travel attitudes, personality, lifestyle, excess travel, adoption and consideration of travel-related strategies, mobility constraints, and demographic characteristics. For this study, we chose to focus on commuting workers since they contribute most heavily to peak-period congestion, and are likely to be the most active in the adoption and consideration of travel-related strategies; the subset of 1283 cases that consists of commuting workers with relatively complete responses to key questions is used in this analysis. Binary logit models were developed for the consideration of each individual travel-related strategy. Each dependent variable, consideration of the given strategy, was defined as a binary variable, and the other variables were viewed as potential explanatory variables. Generally, the significance level 0.05 was used to incorporate or release variables in the final "best" model.