Poster: Data-driven Algorithms for Reducing the Carbon Footprint of Ride-sharing Ecosystems

Mahsa Sahebdel, Ali Zeynali, Noman Bashir, Mohammad Hajiesmaili, Jimi Oke

Abstract:

Urban mobility contributes 40% of CO2 emissions from road trans-
port, which is projected to double by 2050 [6]. Ride-sharing services
like Uber and Lyft have transformed urban mobility by providing
convenient and on-demand personal transportation through smart-
phone applications. However, their success has resulted in an in-
crease in traffic and congestion on roads—a type of rebound effect.
For example, in New York City, ride-sharing accounts for over 50%
of road traffic. Recent studies estimate that a typical ride-sharing
trip is less efficient than a personal car trip, mainly due to “dead-
head” miles traveled by a ride-share vehicle between consecutive
hired rides, resulting in 36-45% higher distance travelled and upto
47% higher CO2 emissions compared to a private car ride [3 ]. As a
result, there is a need to develop emission-aware ride-assignment
algorithms that reduce emissions from deadhead miles.
Recent work has used theoretical as well as data-driven and
machine learning (ML) approaches to improve the performance
of ride-sharing platforms. For example, Abkarian et al. [ 1 ] present
a model that aims to balance the tradeoff between waiting times
and deadhead mileage driven by the vehicles in the fleet. Ke et
al. [4] propose a novel spatio-temporal deep learning approach that
uses a convolutional neural network (CNN) to model the spatial
distribution of demand and a long short-term memory (LSTM)
network to model the temporal patterns in ride demand. While
these studies focus on improving the performance of ride-sharing
services, they do not explicitly target reducing deadhead miles.
The most relevant work to ours targets reducing deadhead miles
for individual trips [ 5 ]. Authors combine demand predictions with
a heuristic approach to driver assignment to demonstrate up to 82%
reduction in trip-level deadhead miles. However, their approach may
not effectively reduce system-wide deadhead miles and emissions,
which depend on factors like fuel efficiency and traffic conditions.
Furthermore, they neither consider EVs nor do they take equity
into account. Our work takes a holistic approach toward designing
multi-objective ride assignment optimizations, aiming to reduce
emissions from deadhead miles, incorporate equity considerations,
and account for EVs in ride-sharing fleets. In this paper, we present
a preliminary study illustrating the benefits of emission-aware ride
assignment and propose combining data-driven algorithms and
machine learning to enhance online decision-making processes.