Prioritizing Bus Routes for Electrification: GIS-Based Multi-Criteria Analysis Considering Operational, Environmental, and Social Benefits and Costs

Document Type

Journal Article

Publication Date

2022

Subject Area

place - north america, place - urban, planning - methods, planning - integration, planning - environmental impact, planning - route design, infrastructure - vehicle, technology - emissions, technology - geographic information systems, technology - alternative fuels

Keywords

Bus routes, Costs, Electric vehicles, Environmental impacts, Geographic information systems, Social benefit

Abstract

Society today is enjoying an unprecedented level of human mobility but is also confronting environmental degradation resulting from fossil fuel consumption and greenhouse gas emissions. The electrification of bus transit systems is recognized as one of the practical solutions to mitigate air pollution and other externalities of increased mobility. However, the implementation of an e-bus system requires the purchase of e-buses and the development of charging infrastructure. To reduce costs and maximize benefits, it is crucial to develop an integrated strategy during the planning stage. This study applies a GIS-based multi-criteria decision analysis approach to determine the candidate bus routes to convert from diesel-powered to electric-driven. This framework appraises not only the characteristics of bus routes but also the possibility of deploying charging infrastructures in bus terminals. Fourteen common criteria are used to evaluate the main considerations of bus electrification, including economic, environmental, and social benefits and costs. The analytic hierarchy process and the technique of order preference similarity to the ideal solution are employed to determine the criteria weights and the route ranking, respectively. The bus network of Twin Cities, MN, U.S., is used as a study case to present the proposed approach. Sensitivity analysis is included to identify the overall top 10 bus routes. The result shows that this method can use widely available open data to select top candidate routes that meet multiple criteria.

Rights

Permission to publish the abstract has been given by SAGE, copyright remains with them.

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