Estimating Changes in Parking Capacity and Urban Form From Vehicle Automation

This project developed a method to characterize the impact of privately-owned autonomous electric vehicles on electric vehicle charger placement, distribution, utilization, and power demand. Using Seattle, WA as a case study, a least total cost optimization for charging station owner and driver costs is conducted for vehicle automation levels 0-3, 4, and 5. Moving from levels 0-3 to level 4 and level 5 automation reduces the peak electrical load for EV charging by approximately 31% and 68%, respectively. Moving from levels 0-3 to level 4 automation decreased the optimal number of chargers by 65%, lowered total cost by 46%. Moving from levels 0-3 automation to level 5 automation decreased the optimal number of chargers by 84% and total costs by 69%. Additional vehicle miles traveled and operating costs incurred by drivers for drop off and pick up were estimated with level 5 automation. The results suggest that highly automated vehicle technology used in privately-owned electric vehicles could reduce the cost of deployment for recharging infrastructure and reduce peak electrical demand associated with recharging. The results were published as a Technical Report (Mersky and Samaras, 2020, included in this document), and are under review at an academic journal. Several government, professional, and academic presentations were also completed.

• Record URL:
  https://ppms.cit.cmu.edu/media/project_files/M21_Final_Report_202.pdf
• 
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Mobility21

  Carnegie Mellon University
  Pittsburgh, PA  United States 

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Samaras, Constantine
  • 0000-0002-8803-2845
  • Mersky, Avi C
  • 0000-0003-4243-9655
• Publication Date: 2020-12

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Research Report
• Features: Figures; References; Tables;
• Pagination: 34p

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Built environment; Case studies; Costs; Demand; Driving automation levels; Electric vehicle charging; Electricity; Location; Optimization; Parking
• Geographic Terms: Seattle (Washington)
• Subject Areas: Energy; Highways; Planning and Forecasting; Terminals and Facilities; Vehicles and Equipment;

Filing Info

• Accession Number: 01762055
• Record Type: Publication
• Contract Numbers: 69A3551747111
• Files: UTC, TRIS, ATRI, USDOT
• Created Date: Dec 21 2020 3:05PM