A Holistic Approach for Equity-aware Carbon Reduction of the Ridesharing Platforms

Mahsa Sahebdel, Ali Zeynali, Noman Bashir, Prashant J. Shenoy, Mohammad Hajiesmaili

Abstract: In recent years, ridesharing services have revolutionized personal mobility, offering convenient on-demand transportation anytime. While early proponents of ridesharing suggested that these services would reduce the overall carbon emissions of the transportation sector, recent studies reported a type of rebound effect showing substantial carbon emissions of ridesharing platforms, mainly due to their deadhead miles traveled by a ride-share car between two consecutive rides. However, reducing deadhead miles’ emissions can incur longer waiting times for riders and starvation of ride assignments for some drivers. Therefore, any efforts towards reducing the carbon emissions from ridesharing platforms must consider the impact on the quality of service, e.g., waiting time, and on the fair and equitable distribution of rides across drivers. This paper proposes a holistic approach to reduce the carbon emissions of ridesharing platforms while minimizing the degradation in user waiting times and equitable ride assignments across drivers. Towards this end, we decompose the global carbon reduction problem into two related sub-problems: carbon- and equity-aware ride assignment and fuel-efficient routing. For the ride assignment problem, we consider the trade-off between the amount of carbon reduction and the rider’s waiting time and propose simple yet efficient algorithms to handle the conflicting trade-offs. For the routing problem, we analyze the impact of fuel-efficient routing in reducing the carbon footprint, trip duration, and driver efficiency of ridesharing platforms using route data from Google Maps. Our comprehensive trace-driven experimental results show substantial emissions reduction of our proposed algorithms with only a graceful increase in riders’ waiting times. Finally, we release “E2-RideKit”, a toolkit that allows researchers to augment ridesharing datasets with emissions and equity information, enabling further research on emissions analysis and improvement of ridesharing platforms.