Xianfeng Yang2025-01-222025-01-222025-01-13Tarafdar, S., Zhang, Y., Gong, Y., & Yang, X. (2024). Integrating Residential and Commercial Electric Vehicle Charging Infrastructure: A Bi-Level Optimization Approach. Sustainable Mobility and Accessibility Regional Transportation Equity Research Center.http://hdl.handle.net/11603/37336To model the impacts of residential charging on commercial charging demand, the study employs Monte Carlo Simulation (MCS). This technique models stochastic EV charging demand using static travel demand and EV profiles, addressing the complexity and stochasticity arising from diverse travel patterns, EV characteristics, and charging infrastructure capacity. Before the data processing begins, the Round 10 Cooperative Forecast data and RITIS data are integrated with QGIS by matching the Traffic Analysis Zone (TAZ) IDs of both datasets to ensure spatial consistency. The key datasets include: Travel Demand Data, EV Profile Data, and Charging Infrastructure Capacity Data.This report focuses on the data and methodologies utilized to evaluate the impact of residential charging facilities on the demand for commercial charging infrastructure. The study uses the Baltimore-Columbia-Towson Metropolitan Statistical Area (MSA) as the study site, comprising six counties and one independent city (Anne Arundel, Baltimore City and County, Carroll, Harford, Howard, and Queen Anne’s), with a combined population of approximately 3 million.en-USAttribution 3.0 United Stateshttp://creativecommons.org/licenses/by/3.0/us/electric vehiclesDataset