Estimating Changes in Parking Capacity and Urban Form From Vehicle Automation

Samaras, Constantine; Mersky, Avi C. · 2020 · ROSA P / Mobility21, Carnegie Mellon University

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This study investigates how higher levels of vehicle automation (Levels 4 and 5) impact the siting, utilization, and cost of electric vehicle (EV) charging infrastructure. The research addresses a gap in existing literature regarding how autonomous capabilities can optimize charging infrastructure by increasing charger utilization and relaxing spatial constraints on charger placement. The primary motivation is to determine the potential efficiencies and energy impacts of automation, specifically asking how Level 4 (autonomous within controlled domains) and Level 5 (fully autonomous) vehicles affect infrastructure costs and peak electrical demand compared to traditional Levels 0–3 vehicles. The authors developed a least total cost optimization model using Seattle, WA, as a case study. The model minimizes the combined costs for charging station owners (real estate, equipment capital, and maintenance) and drivers (walking costs for Levels 0–4; additional vehicle operation costs for Level 5). Data was derived from the Puget Sound 2014 Household Travel Survey, filtering approximately 3,500 work-related trips. The study assumed 100% EV adoption for these trips and utilized King County real estate assessment data to estimate land costs. A Monte Carlo simulation estimated a maximum charger utilization rate of 31% for autonomous scenarios, where vehicles queue for charging rather than occupying spaces for their entire parking duration. The optimization models accounted for Level 2 charging equipment costs ($10,000 per charger) and calculated driver costs based on local income and electricity prices. The results demonstrate significant reductions in infrastructure needs and energy demand with increased automation. Moving from Levels 0–3 to Level 4 automation reduced the optimal number of chargers by 65% and total costs by 46%. Advancing to Level 5 automation decreased the optimal number of chargers by 84% and total costs by 69%. Furthermore, automation smoothed peak electrical loads. Compared to Levels 0–3, Level 4 automation reduced peak EV charging load by approximately 31%, while Level 5 automation reduced it by 68%. Under Level 5, electric demand remained steady at just under 700 kWh during peak hours, whereas non-automated scenarios saw peaks exceeding 2,000 kWh between 8 and 9 a.m. These findings indicate that automated queueing and the ability of vehicles to travel to distant charging locations significantly lower infrastructure deployment costs and mitigate grid stress. The study concludes that highly automated vehicle technology can substantially reduce the financial burden of EV recharging infrastructure and decrease peak electrical demand. By enabling higher charger utilization and allowing vehicles to park further from destinations, automation relaxes the spatial limitations that currently drive up real estate and equipment costs. These implications suggest that municipalities and infrastructure planners should consider automation levels when designing future EV charging networks, as doing so can lead to more efficient, cost-effective, and grid-friendly infrastructure deployment.

Key finding

Moving from levels 0-3 to level 5 automation decreases the optimal number of electric vehicle chargers by 84% and reduces peak electrical load by 68%.

Methodology

modeling

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• driverless ads