Carsharing programs that operate as short-term vehicle rentals (often for one-way trips beforeending the rental) like Car2Go and ZipCar have quickly expanded, with the number of U.S. usersdoubling every one to two years over the past decade. Such programs seek to shift personaltransportation choices from an owned asset to a service used on demand. The advent ofautonomous or fully self-driving vehicles will address many current carsharing barriers,including users’ travel to access available vehicles.This work describes the design of an agent-based model for Shared Autonomous Vehicle (SAV)operations, the results of many case-study applications using this model, and the estimatedenvironmental benefits of such settings, versus conventional vehicle ownership and use. Themodel operates by generating trips throughout a grid-based urban area, with each trip assigned anorigin, destination and departure time, to mimic realistic travel profiles. A preliminary modelrun estimates the SAV fleet size required to reasonably service all trips, also using a variety ofvehicle relocation strategies that seek to minimize future traveler wait times. Next, the model isrun over one-hundred days, with driverless vehicles ferrying travelers from one destination to thenext. During each 5-minute interval, some unused SAVs relocate, attempting to shorten waittimes for next-period travelers.Case studies vary trip generation rates, trip distribution patterns, network congestion levels,service area size, vehicle relocation strategies, and fleet size. Preliminary results indicate thateach SAV can replace around eleven conventional vehicles, but adds up to 10% more traveldistance than comparable non-SAV trips, resulting in overall beneficial emissions impacts, oncefleet-efficiency changes and embodied versus in-use emissions are assessed.
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