01610437

Component

https://doi.org/10.3141/2572-05

The market potential of a fleet of shared autonomous electric vehicles (SAEVs) is explored by using a multinomial logit mode choice model in an agent-based framework and different fare settings. The mode share of SAEVs in the simulated midsize city (modeled roughly after Austin, Texas) is predicted to lie between 14% and 39% when the SAEVs compete with privately owned, manually driven vehicles and city bus service. The underlying assumptions are that SAEVs are priced between $0.75/mi and $1.00/mi, which delivers significant net revenues to the fleet owner–operator under all modeled scenarios; that they have an 80-mi range and that Level 2 charging infrastructure is available; and that automation costs are up to $25,000 per vehicle. Various dynamic pricing schemes for SAEV fares indicate that specific fleet metrics can be improved with targeted strategies. For example, pricing strategies that attempt to balance available SAEV supply with anticipated trip demand can decrease average wait times by 19% to 23%. However, trade-offs exist within this price setting: fare structures that favor higher revenue-to-cost ratios—by targeting travelers with a high value of travel time (VOTT)—reduce SAEV mode shares, while those that favor larger mode shares—by appealing to a wider VOTT range—produce lower payback.

01588891

16-2281

English

pp 37–46

2016

Transportation Research Record: Journal of the Transportation Research Board

9780309370059

Print

Figures (3) ; References (24) ; Tables (5)

Electric vehicle charging; Electric vehicles; Fleet management; Infrastructure; Intelligent vehicles; Pricing; Vehicle operations; Vehicle sharing

Economics; Energy; Environment; Highways; Vehicles and Equipment

PRP, TRIS, TRB, ATRI

Jan 12 2016 5:01PM

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• Exploring the Decision to Adopt a High-End Battery Electric Vehicle: Role of Financial and Nonfinancial Motivations
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• Integrating Climate Change into Scenario Planning: Can Mitigation Measures Also Make a Region More Resilient?
• Introducing a New Emissions Certification Procedure for European Light-Duty Vehicles: Monte Carlo Simulation of the Potential Effect on Fleet Carbon Dioxide Emissions
• Modeling Charging Choices of Small-Battery Plug-In Hybrid Electric Vehicle Drivers by Using Instrumented Vehicle Data
• Modeling the Charging Choices of Battery Electric Vehicle Drivers by Using Stated Preference Data