Unreliability in ridesharing systems: Measuring changes in users’ times due to new requests

On-demand systems in which several users can ride simultaneously the same vehicle have great potential to improve mobility while reducing congestion. Nevertheless, they have a significant drawback: the actual realization of a trip depends on the other users with whom it is shared, as they might impose extra detours that increase the waiting time and the total delay; even the chance of being rejected by the system depends on which travelers are using the system at the same time. In this paper we propose a general description of the sources of unreliability that emerge in ridesharing systems and we introduce several measures. The proposed measures are related to two sources of unreliability induced by how requests and vehicles are being assigned, namely how users’ times change within a single trip and between different realizations of the same trip. We then analyze both sources using a state-of-the-art routing and assignment method, and a New York City test case. Regarding same trip unreliability, in our experiments for different fixed fleet compositions and when reassignment is not restricted, we find that more than one third of the requests that are not immediately rejected face some change, and the magnitude of these changes is relevant: when a user faces an increase in her waiting time, this extra time is comparable to the average waiting time of the whole system, and the same happens with total delay. Algorithmic changes to reduce this uncertainty induce a trade-off with respect to the overall quality of service. For instance, not allowing for reassignments may increase the number of rejected requests. Concerning the unreliability between different trips, we find that the same origin-destination request can be rejected or served depending on the state of the fleet. And when it is served the waiting times and total delay are rarely equal, which remains true for different fleet sizes. Furthermore, the largest variations are faced by trips beginning at high-demand areas.Learning & Autonomous Contro