This presentation introduces an innovative spatiotemporal analytical framework and web-based visualization platform developed by researchers at the University of Utah to assist transit agencies in identifying optimal deployment strategies for a battery-electric bus (BEB) system by using a combination of mathematical programming methods, 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).
KEY LEARNING OUTCOMES
--Introduction to a bi-objective spatiotemporal optimization model for the strategic deployment of BEBs to minimize the cost of purchasing BEBs, on-route and in-depot charging stations, and to maximize environmental equity for disadvantaged populations.
--The optimization considers the unique constraints imposed by BEB operations in a spatiotemporal fashion.
--We used empirical data to offer a potential framework that can be adopted or expanded by transit agencies to optimally deploy BEBs by accommodating multiple goals and objectives that the transit agencies set forth.
--The research could help transit agencies develop optimal deployment strategies for battery-electric bus systems, allowing planners and decision makers to create transportation systems that better serve livable and sustainable communities.