Estimating Changes in Parking Capacity and Urban Form From Vehicle Automation

US Transportation Collection2020PDFTech ReportSamaras, ConstantineMersky, Avi C.Mobility21, Carnegie Mellon UniversityUnited States. Department of Transportation. University Transportation Centers (UTC) ProgramMobility21, Carnegie Mellon UniversityUnited StatesSeattle (Washington)Autonomous vehiclesBuilt environmentCase studiesCostsDemandDriving automation levelsElectric vehicle chargingElectricityLocationOptimizationParking69A3551747111Final Research ReportThis 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.120