Regional Transportation Modeling for Equitable Electric Vehicle Charging Infrastructure Design
Ismaeel Babur, Jane Macfarlane
TL;DR
The paper addresses equitable BEV charging network design by applying regional, cross-jurisdiction transportation modeling to capture mobility patterns and grid considerations. It combines high-fidelity Mobiliti simulations of over 19 million Bay Area trips with spline-based energy interpolation, ROM and Granular energy estimates, and RouteE validation to map charging demand and environmental justice impacts. It quantifies emission reductions in Equity Priority Communities through Monte Carlo BEV adoption scenarios, reporting fuel savings and CO2 reductions derived from RouteE-based fuel estimates. The findings demonstrate that regional, equity-focused planning can optimize charging siting, reduce range anxiety, and guide grid coordination for a just and sustainable transition to electric mobility.
Abstract
The widespread adoption of battery electric vehicles (BEVs) holds promise for mitigating emission-related health impacts, particularly for low-income communities disproportionately affected by exposure to traffic-related air pollution. However, designing effective charging infrastructure necessitates a regional modeling approach that accounts for the inherent cross-jurisdictional nature of mobility patterns. This study underscores the importance of regional modeling in optimizing charging station deployment and evaluating the environmental justice implications for equity priority communities. We present a large-scale regional transportation modeling analysis leveraging Mobiliti, a cloud-based platform that employs parallel discrete event simulation to enable rapid computation. Our approach identifies the spatial demand density for charging infrastructure by analyzing over 19 million trips in the San Francisco Bay Area and determining the threshold points where BEVs may require charging across a typical day. By transitioning these trips that originate outside equity priority communities to BEVs, we quantify the potential emission reductions within these vulnerable areas. The regional modeling framework captures the complex interactions between travel behavior, vehicle characteristics, and charging needs, while accounting for the interconnectivity of infrastructure across municipal boundaries. This study demonstrates the critical role of regional modeling in designing equitable BEV charging networks that address environmental justice concerns. The findings inform strategies for deploying charging infrastructure that maximizes accessibility, minimizes range anxiety, and prioritizes the health and well-being of communities disproportionately burdened by transportation emissions.