| Abstract: |
Tele-driving refers to a novel concept where drivers can remotely operate vehicles (without being physically in the vehicle). By putting the human back “in the loop,” tele-driving has emerged recently as a more viable alternative to fully automated vehicles, with ride-hailing (and other on-demand transportation-enabled services) being an important application. Because remote drivers can be operated as a shared resource (any driver can be assigned to any customer regardless of trip origin or destination), it may be possible for such services to deploy fewer drivers than vehicles without significantly reducing service quality. In this paper, we examine the extent to which this is possible. Using a spatial queueing model that captures the dynamics of both pick up and trip times, we show that the impact of reducing the number of drivers depends crucially on system workload relative to the number of vehicles. In particular, when workload is sufficiently high relative to the number of vehicles, we show that, perhaps surprisingly, reducing the number of drivers relative to the number of vehicles can actually improve service level (e.g., as measured by the amount of demand fulfilled in the case of impatient customers). When workload is sufficiently low relative to the number of vehicles, we show that it is possible to significantly reduce the number of drivers without significantly reducing service level. In systems where customers are patient and willing to queue up for the service, we show that reducing the number of drivers can stabilize a system that would otherwise be unstable. In general, relative to a system where the number of vehicles equals the number of drivers (as in a system with in-vehicle drivers), a system with remote drivers can result in savings in the number of drivers either without significantly degrading performance or while actually improving performance. We discuss how these results can, in part, be explained by the interplay of two counteracting forces: (1) having fewer drivers increasing “service rate” and (2) having fewer drivers reducing
the number of “servers,” with the relative strength of these forces depending on system workload.
Keywords: tele-driving, ride-hailing, spatial queueing systems, capacity optimization
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Biography:
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I am a Postdoctoral Fellow in the Rotman School of Management at University of Toronto, under the supervision of Ming Hu. Before joining UofT, I received my Ph.D. degree from University of Minnesota – Twin Cities, advised by Saif Benjaafar. My research interests are in the area of operations management with a focus on on-demand service platforms, smart mobility, and the sharing economy.
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