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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.
Chen et al. (Fri,) studied this question.