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

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

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 ben-
efits. 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 vehi-
cles 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. Unfortunately, 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 en-
suring that the driver’s utility, defined as the difference be-
tween the trip distance and the deadhead miles, is fairly dis-
tributed. LEAD uses reinforcement learning to match rid-
ers to 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 ride-sharing dataset show that LEAD
improves fairness by 2× when compared to emission-aware
ride-assignment and reduces emissions by 70% while en-
suring fairness within 66% of the fair baseline. It also re-
duces the rider wait time, by at least 40%, compared to var-
ious baselines. Additionally, LEAD corrects the imbalance
in previous emission-aware ride assignment algorithms that
overassigned rides to low-emission vehicles.