United States Department of Transportation
i
Bi-objective Optimization for Battery Electric Bus Deployment Considering Cost and Environmental Equity [supporting datasets]
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2021-02-28
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[PDF-166.03 KB]
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Alternative Title:Data Files: Bi-Objective Optimization for Battery Electric Bus Deployment Considering Cost and Environmental Equity
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Abstract:Public transit, compared with passenger cars, can effectively help conserve energy, reduce air pollution, and optimize flow on roadways. In recent years, Battery Electric Bus (BEB) is receiving an increasing amount of attention from the transit vehicle industry and transit agencies due to recent advances in battery technologies and the direct environmental benefits it can offer (e.g., zero emissions, less noise). However, limited efforts have been attempted on the effective deployment planning of the BEB system due to the unique spatiotemporal features associated with the system itself (e.g., driving range, bus scheduling). In this project, the authors developed an innovative spatiotemporal analytical framework and web-based visualization platform to assist transit agencies in identifying the optimal deployment strategies for the BEB system by using a combination of mathematical programming methods, Geographic Information System (GIS)-based analysis, and multi-objective optimization techniques. The framework allows transit agencies to optimally phase in BEB infrastructure and deploy the BEB system in a way that can minimize the capital and operational cost of the BEB system while maximizing its environmental benefits (i.e., emission reduction). The authors engaged two transit agencies - the Utah Transit Authority (UTA) and TriMet, both in the planning phase of BEB deployment - to evaluate the usability of the platform. The web-based visualization platform operationalizes the framework and makes it accessible to transit planners, decision makers and the public. This project fits the National Institute for Transportation and Communities (NITC) theme on increasing access to opportunities, improving multimodal planning, and developing data, models, and tools for better decision making. The research could help transit agencies develop optimal deployment strategies for BEB systems, allowing planners and decision makers to create transportation systems that better serve livable and sustainable communities.
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