All ETDs from UAB

Advisory Committee Chair

Virginia P Sisiopiku

Advisory Committee Members

Michael D Anderson

Wilbur A Hitchcock

Ian Hosch

Lee Moradi

Virginia P Sisiopiku

Document Type

Dissertation

Date of Award

2016

Degree Name by School

Doctor of Philosophy (PhD) School of Engineering

Abstract

In the recent years, the number of freight truck trips has been growing at a staggering pace all over the world. Traditional travel demand forecasting models do not model truck trips separately, but rather include them implicitly in the non-home-based (NHB) trip category. Little attention is, thus, paid to truck types, trip patterns, trip lengths, or actual truck counts. With a growing realization of the importance of truck traffic both to the overall economy and on urban congestion and pollution levels, there is a new interest in modeling truck movements with greater accuracy and detail. The purpose of this research study is to investigate the feasibility of utilizing a novel tour-based approach to model truck trips in the Birmingham, AL region. Given that trucks often make multiple stops as they travel from their origin to their destination, a tour-based approach for modeling truck travel holds great promise for capturing the actual trip making patterns of truck trips. First, this study provides a comprehensive literature review on various freight transportation forecasting modeling approaches focusing on their strengths and limitations, input needs and requirements, key assumptions and processes, expected outputs, and suitability for application of freight planning. Then, the overall framework of truck tour-based approach at the regional level is presented. Attention is given to the description of the tour-based model framework, data processing requirements, and validation requirements. Moreover, the feasibility of the new tour-based approach is demonstrated using data from the Birmingham region and the Cube Voyager platform. Performance measures obtained from the Birmingham travel demand forecasting model that incorporated truck tour-based model are compared with those obtained from the conventional trip-based model that is currently being used by the Regional Planning Commission of Greater Birmingham (RPCGB). Additionally, the model using the tour-based approach is further investigated and compared to another truck trip generation methodology published in the Quick Response Freight Manual (QRFM). The outputs from both models applied to the Birmingham transportation network are compared and contrasted to field data to determine the level of accuracy of each modeling approach. Given the limited research performed to date on the topic of truck trip generation, the findings of this study are significant as they bridge an existing theory-practice gap of tour-based approach applications in travel demand forecasting. The study results illustrate that the tour-based approach is a feasible and desirable approach as it improves the accuracy of travel demand forecasting of existing models and performs better than alternative options (such as the QRFM) when compared to actual truck traffic counts within the study area. Overall, this study provides a valuable guidance on methods to improve freight truck modeling efforts. It is anticipated that the innovative truck tour-based approach employed in this study will set a foundation for enhancing the current state-of-the-practice of truck demand modeling in small and medium sized communities.

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