Anticipatory rebalancing of RoboTaxi systems

This study considers a RoboTaxi service, a futuristic transportation system whereby passengers can place transportation requests in real-time for a private ride. The passengers are picked up from their origins as soon as possible after placing a request and travel directly to their destination, from which the RoboTaxi may immediately continue to serve other customers, remain idle, or move to another location in the city in anticipation of future demand. The operation of RoboTaxi systems requires online decisions regarding vehicle dispatching and rebalancing. Dispatching assigns cars to requests. Rebalancing is the initiation of empty trips of idle cars to reduce future requests’ waiting time and take advantage of off-peak traffic hours. This study proposes an advanced method for rebalancing while assuming a simple yet effective dispatching policy. The optimal operation of a RoboTaxi fleet aims to minimize two competing values: the passengers waiting time between requests and pickups and the total distance traveled by empty vehicles. In a RoboTaxi system, vehicles may travel empty either when deadheading to pick up a passenger or while making rebalancing trips. There is a trade-off between the rebalancing effort, which includes various types of costs, and the passengers’ waiting time, representing the quality of the service provided by the system.