Heatmap-based Decision Support for Repositioning in Ride-Sharing Systems

Abstract

In ride-sharing systems, platform providers aim to distribute the drivers in the city to meet current and potential future demand and to avoid service cancellations. Ensuring such distribution is particularly challenging in the case of a crowdsourced fleet, as drivers are not centrally controlled but are free to decide where to reposition when idle. Thus, providers look for alternative ways to ensure a vehicle distribution that benefits both users and drivers, and consequently the provider. We propose an intuitive means to improve idle ride-sharing vehicles' repositioning: repositioning opportunity heatmaps. These heatmaps highlight driver-specific earning opportunities approximated based on the expected future demand, fleet distribution, and location of the specific driver. Based on the heatmaps, drivers make decentralized yet better-informed repositioning decisions. As our heatmap policy changes the driver distribution, we propose an adaptive learning algorithm for designing our heatmaps in large-scale ride-sharing systems. We simulate the system and generate heatmaps based on previously learned repositioning opportunities in every iteration. We then update these based on the simulation's outcome and use the updated values in the next iteration. We test our heatmap design in a comprehensive case study on New York ride-sharing data. We show that carefully designed heatmaps reduce service cancellations therefore revenue loss for platform and drivers significantly.