Trajectory-Integrated Accessibility Analysis of Public Electric Vehicle Charging Stations

2025-05-17

AI Summary

Existing electric vehicle charging station (EVCS) accessibility assessments rely solely on static residential locations, neglecting users’ dynamic mobility patterns and trip-based charging needs. Method: We propose a Trajectory-Integrated Accessibility metric (TI-acs), the first to embed full-spatiotemporal individual trajectories—derived from one-week mobility data of 6 million residents—into Level 2 (L2) and DC fast-charging (DCFC) facility accessibility modeling. Our approach integrates trajectory mining, multi-source GIS analysis, and census-tract-level equity assessment using the Gini index. Contribution/Results: TI-acs quantifies daily accessible time within a 1-km walking radius across trip contexts (e.g., commuting, work), yielding 7.5 hours for L2 and 5.2 hours for DCFC. It reveals pronounced spatial inequity (Gini = 0.38 for L2; 0.44 for DCFC) and mobility-driven racial disparities. This framework establishes a new paradigm for equitable, behavior-informed EV infrastructure planning, supported by empirical evidence.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: AI for Accessibility
• Application Domains: Mobility, Driving & Flight

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements

Abstract

Electric vehicle (EV) charging infrastructure is crucial for advancing EV adoption, managing charging loads, and ensuring equitable transportation electrification. However, there remains a notable gap in comprehensive accessibility metrics that integrate the mobility of the users. This study introduces a novel accessibility metric, termed Trajectory-Integrated Public EVCS Accessibility (TI-acs), and uses it to assess public electric vehicle charging station (EVCS) accessibility for approximately 6 million residents in the San Francisco Bay Area based on detailed individual trajectory data in one week. Unlike conventional home-based metrics, TI-acs incorporates the accessibility of EVCS along individuals' travel trajectories, bringing insights on more public charging contexts, including public charging near workplaces and charging during grid off-peak periods. As of June 2024, given the current public EVCS network, Bay Area residents have, on average, 7.5 hours and 5.2 hours of access per day during which their stay locations are within 1 km (i.e. 10-12 min walking) of a public L2 and DCFC charging port, respectively. Over the past decade, TI-acs has steadily increased from the rapid expansion of the EV market and charging infrastructure. However, spatial disparities remain significant, as reflected in Gini indices of 0.38 (L2) and 0.44 (DCFC) across census tracts. Additionally, our analysis reveals racial disparities in TI-acs, driven not only by variations in charging infrastructure near residential areas but also by differences in their mobility patterns.

Problem

• Develops trajectory-integrated metric for EV charging accessibility
• Assesses public EVCS access disparities in Bay Area
• Analyzes racial and spatial inequities in charging infrastructure

Innovation

• Trajectory-Integrated Public EVCS Accessibility metric
• Assesses EVCS access using individual trajectory data
• Reveals spatial and racial disparities in charging access