LEAD: Towards Learning-Based Equity-Aware Decarbonization in Ridesharing Platforms

Abstract

Ridesharing platforms such as Uber, Lyft, and DiDi have grown in popularity due to their on-demand availability, ease of use, and commute cost reductions, among other benefits. However, not all ridesharing promises have panned out. Recent studies demonstrate that the expected drop in traffic congestion and reduction in greenhouse gas (GHG) emissions have not materialized. This is primarily due to the substantial distances traveled by the ridesharing vehicles without passengers between rides, known as deadhead miles. Recent work has focused on reducing the impact of deadhead miles while considering additional metrics such as rider waiting time, GHG emissions from deadhead miles, or driver earnings. However, most prior studies consider these environmental and equity-based metrics individually despite them being interrelated. In this paper, we propose a Learning-based Equity-Aware Decarabonization approach, LEAD, for ridesharing platforms. LEAD targets minimizing emissions while ensuring that the driver's utility, defined as the difference between the trip distance and the deadhead miles, is fairly distributed. LEAD uses reinforcement learning to match riders with drivers based on the expected future utility of drivers and the expected carbon emissions of the platform without increasing the rider waiting times. Extensive experiments based on a real-world ridesharing dataset show that LEAD improves the defined notion of fairness by 150% when compared to emission-aware ride-assignment and reduces emissions by 14.6% while ensuring fairness within 28--52% of the fairness-focused baseline. It also reduces the rider wait time, by at least 32.1%, compared to a fairness-focused baseline.

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