Teleoperated vehicles are a promising concept for increasing the attractiveness of car-sharing services. Such vehicles can be remotely steered by an operator to the location of a customer requesting a vehicle on demand. It therefore eliminates both the need for customers to walk to a car-sharing vehicle and the need for providers to relocate vehicles with drivers on-site to meet the temporal and spatial vehicle demand. The key to a successful teleoperated car-sharing service is to avoid longer service delays by effectively utilizing the fleet of vehicles and the limited number of available operators. The corresponding sequential decision process therefore involves a matching problem deciding which vehicle should be steered next by an available operator in order to fulfill which customer request. This decision is challenging, as both future demand and future availability of vehicles are uncertain, because the rental duration and return location of vehicles are unknown. We propose an approximate dynamic programming approach that combines predictions of future vehicle returns and customer requests with an approximation of the opportunity cost of matching decisions. We demonstrate the merits of our approach in comparison with benchmark policies in a comprehensive computational study based on New York demand data. We derive several important insights, among others, that vehicle predictions are especially valuable, and that a ratio of about one operator to six vehicles is sufficient in our setup. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2025.0206 .
Gottschalg et al. (Mon,) studied this question.