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

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.

Figures (11)

• Figure 1: Ride assignment with the goal of minimizing deadhead miles yields lower such miles and increment in waiting time. We can reduce deadhead miles by assigning passenger $N_1$ to driver $M_2$ with the cost of increase in waiting time.
• Figure 2: Opportunity analysis: Comparison of emissions from deadhead miles and waiting time for the default ride assignment and our offline emission‑aware ride assignment.
• Figure 3: Average increase in distance (a), emission (b), and duration (c) for different routing algorithms to the optimal routes for short, medium, and long-distance trips for three trip categories in our analysis.
• Figure 4: The high-level architecture, various components, and workflow of E$^2$-RideKit.
• Figure 5: Percentage reduction in deadhead miles emissions on $y$-axis compared to the (a) default assignment in the RideAustin dataset and (b) shortest distance driver assignment, as a function of $\phi$ on the $x$-axis for different LEV fractions. Lower values of $\phi$ generally yield higher reduction.