A Holistic Approach for Equity-aware Carbon Reduction of Ridesharing Platforms (2402.01644v2)

Abstract: 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 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 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 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 significant emissions reduction with a minor increase in riders' waiting times. Finally, we release E$^2$-RideKit, a toolkit that enables researchers to augment ridesharing datasets with emissions and equity information for further research on emission analysis and platform improvement.

• The paper presents innovative algorithms that reduce carbon emissions while balancing rider wait times and driver equity.
• It introduces fuel-efficient routing strategies leveraging Google Maps data to optimize trip durations and decrease environmental impact.
• Simulation results and the E²-RideKit toolkit demonstrate that sustainable ridesharing can significantly cut emissions with only minor increases in waiting times.

The paper "A Holistic Approach for Equity-aware Carbon Reduction of Ridesharing Platforms" provides a comprehensive strategy to mitigate the carbon emissions associated with ridesharing services, which have shown to contribute significantly to environmental degradation due to deadhead miles—miles traveled without passengers between consecutive rides. The authors recognize that merely focusing on reducing these deadhead miles can negatively impact rider waiting times and create inequity in ride assignments, potentially leading to driver "starvation."

To tackle these multifaceted challenges, the authors present an integrated method that addresses both carbon reduction and equity in service distribution. They divide the overarching problem into two interconnected sub-problems:

1. Carbon- and Equity-aware Ride Assignment:
   • The authors propose novel algorithms that balance the reduction of carbon emissions with maintaining reasonable waiting times for riders.
   • These algorithms are designed to manage the inherent trade-offs between minimizing environmental impact and ensuring a fair distribution of rides among drivers.
2. Fuel-efficient Routing:
   • The paper incorporates fuel-efficient routing strategies, analyzing their effectiveness in decreasing the carbon footprint, trip duration, and enhancing driver efficiency by using route data sourced from Google Maps.

The research includes extensive experimental evaluations using simulated datasets. Their findings demonstrate that significant reductions in carbon emissions can be achieved with only a minor increase in rider waiting times, suggesting an effective compromise between sustainability and service quality.

Furthermore, the authors introduce E$^2$-RideKit, a versatile toolkit designed to support further research and development in this domain. E$^2$-RideKit enables researchers to enhance existing ridesharing datasets with detailed emissions and equity information, fostering continued innovation in emission analysis and ridesharing platform improvements.

Overall, this paper not only provides actionable insights and methods for reducing the environmental impact of ridesharing services but also emphasizes the importance of maintaining service quality and fairness, making it a valuable contribution to the field of sustainable urban mobility.