Transportation plays a significant role in the mobility, economic health, and quality of life of our communities. The transportation planning process is a complex process of developing and evaluating strategies to meet an area’s long-term goals. Planning is inherently a public process that connects us to the future. In the field of transportation planning, travel demand models are often the tool used to make this connection. Travel demand models forecast future travel demand based on forecast input variables related to land use and demographic factors. A fundamental assumption of travel demand models is that model parameters remain stable over time. A violation of this assumption could lead to transportation analyses and travel forecasts that either over- or underestimate travel demand and associated transportation deficiencies, which could in turn lead to poorly allocated investments in transportation infrastructure. Developing a better understanding of the factors that influence travel behavior, the changes in travel behavior over time, and the explanatory variables that best capture these changes may lead to the development of models that are more temporally stable.
This research focuses on advanced statistical data analysis and the development of enhanced trip generation models. The focus of the data analysis is on developing a better understanding of trip making behavior, changes in this behavior over time, and the explanatory variables that contribute to these changes. Model development provides insights into the role that model type and explanatory variables defining life cycle, area type, and accessibility have on temporal stability. Three household travel surveys from Baltimore, Maryland administered in 1977, 1993, and 2001, and two from the Research Triangle region of North Carolina administered in 1995 and 2006 form the basis of the data analysis tasks. The 1995 and 2006 Triangle surveys supplemented with supporting land use and transportation network data form the basis of the model development tasks.
The findings from this analysis show that both the generation choice and cumulative logistic regression models are good models for trip generation. There is also evidence of temporal stability for these two models, perhaps even better than the more widely used cross-classification model based on findings reported in the literature. The introduction of explanatory variables defining life cycle, area type, and accessibility do not noticeably improve model fit, but there is evidence of improved model verification and temporal stability. Finally, analysis shows that trip rates that change over time can have implications for systems and project level planning, resulting in unexpected changes in vehicle miles traveled and associated emissions, transit ridership, traffic forecasts, and localized travel.
This research contributes to the field of travel demand modeling in several ways. It provides important information for model developers and contributes to the conversation on the temporal stability of trip generation models. An underlying assumption of trip generation models is temporal stability, but little information exists on modeling techniques or explanatory variables that improve temporal stability. This research explores three such variables and makes recommendations on their definition and application. This research also explores two modeling techniques for trip generation and makes recommendations regarding application and temporal stability. The findings outlined in this research support the development of advanced trip generation models for medium- and large-sized communities in order to better capture trip making behavior and improve temporal stability. This research also informs the debate on whether it is worth the additional expenditure of time and resources to develop advanced models.
|School:||North Carolina State University|
|School Location:||United States -- North Carolina|
|Source:||DAI-B 73/12(E), Dissertation Abstracts International|
|Subjects:||Civil engineering, Transportation planning|
|Keywords:||Accessibility, Life cycle, Travel demand, Trip generation|
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