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

Mahsa Sahebdel, Ali Zeynali, Noman Bashir, Prashant 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 reduc-
ing 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 degrada-
tion in user waiting times and equitable ride assignments across dri-
vers. 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 car-
bon 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.