- By:
- Pan, Meiyu ; Li, Wan ; Wang, Chieh
- Page Number:
- 265-275
- Book Title:
- International Conference on Transportation and Development 2024: Transportation Planning, Operations, and Transit
- Publication Date:
- March 12, 2026
- Publisher Location:
- American Society of Civil Engineers, Virginia, United States of America
- Conference Name:
- International Conference on Transportation and Development (ICTD)
- Conference Location:
- Atlanta, Georgia, United States of America
- Conference Sponsor:
- American Society of Civil Engineers
- View DOI Listing:
- https://doi.org/10.1061/9780784485521.024
Abstract
The accelerating adoption of electric vehicles (EVs) poses challenges to the power grid, necessitating precise representation of mobility patterns for effective infrastructure upgrades. Traditional simulation-based charging demand estimation faces limitations in generating trip chains reflective of actual travel patterns without complex network modeling. Hence, an innovative agent-based trip chain generation model is introduced to overcome these challenges. Drawing from the National Household Travel Survey (NHTS) and the NextGen NHTS origin-destination add-on data for Clarke County, Georgia, this study proposes a simulation method capturing both temporal and spatial mobility patterns without relying on extensive network topology data. The resulting trip chains predict EV charging load at the Census Block Group level, validated with a 1.03 correlation to actual trip counts, affirming their reflective accuracy. Two charging scenarios, residential-only and charging-everywhere, reveal distinct demand profiles. The charging-everywhere scenario aligns closely with the trip profile, while the residential-only scenario exhibits an afternoon peak slightly surpassing the former. This study contributes a data-driven charging demand estimation methodology, offering critical insights for grid resiliency planning amid the evolving landscape of EV adoption.
