Citation:

Wei, H., T. Zuo, H. Liu, AND J. Yang. Integrating Land Use and Socioeconomic Factors into Scenario-Based Travel Demand and Carbon Emission Impact Study. Urban Rail Transit. Springer International Publishing AG, Cham (ZG), Switzerland, 3(1):3-14, (2017).

Impact/Purpose:

Communication of research results to scientific and technical community. The research results are about a newly developed modeling methodology on land use and traffic optimization in urban planning and development

Description:

Urban sprawl continues since the last century, leading to a rapid increase in automobile ownership and vehicle travel demand, while resulting in more traffic congestion and automobile emissions. Land use, serving as a source of travel demand, can significantly impact travel behavior. Certain land use policies (e.g. compact development) have been adopted in practice to alter travel behavior and reduce vehicle travel and emission through shaping and directing spatial distributions of population and employment. To develop robust land use policies, scenario-based land use-transportation planning is increasingly being used to enhance the capability of a policy selection. Application of scenario-based approach is challeng-ing due to a range of possibilities in future population and employment distributions, yet is essential for planners and decision makers to determine the optimal land use pattern for planning goals. To address the subject, this paper proposes a bi-level land use optimization model integrated with the travel demand modeling. The upper level of the model framework is formulated to minimize the vehicle travel for given population and employment distributions under a land use policy; the lower level is based on a tour-based travel demand model to represent travel choices. The resulting bi-level optimization is solved by genetic algorithm incorporated with the Method of Successive Average (MSA). The implementation of the proposed method is demonstrated through a case study of Hamilton County, Ohio, U.S.A. The case study results indicate that the proposed optimization model can identify a highly-efficient distribution in land use to reduce vehicle travel and other transportation-related problems.

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