Quantifying Environmental Benefits of Ridesplitting based on Observed Data from Ridesourcing Services

Abstract

The increasing emissions from the transportation sector pose substantial hazards to the environment and human health around the world. With the rapid development of information and communication technologies, ridesplitting, a form of ridesourcing service accessed via smartphone applications, enables passengers with similar origins and destinations to be matched to the same driver and share the ride. This is regarded as a promising travel mode that could mitigate air pollution. However, because of a lack of quantitative analysis, the environmental benefits of ridesplitting have not been rigorously justified. As vast amounts of observed data of ridesourcing have become increasingly available, this study quantifies the environmental benefits of ridesplitting based on the global positioning system (GPS) trajectory and trip order datasets of DiDi Chuxing in Chengdu, China. First, the saved distances of ridesplitting are calculated by analyzing the travel distances of both ridesplitting trips and the corresponding trips under non-ridesplitting conditions. Then, the emission factors of CO, NOx, and HC are estimated by a localized MOVES model. Combining the saved distances and emission factors, the emission reductions from each ridesplitting trip can be calculated. The results show that ridesplitting can decrease the travel distance by 22% on average compared with non-ridesplitting. As a consequence, the average emission reductions per ridesplitting trip are 10.601 g of CO, 0.691 g of NOx, and 1.424 g of HC, respectively. This study provides a better understanding of the environmental benefits of ridesplitting and theoretical guidance for the government’s decision-making in green transport planning.

Determinants of on-demand ridesharing: the role of awareness of environmental consequences

“…The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/1477-7835.htm (Furuhata et al, 2013;Shaheen and Cohen, 2019). The practice of ridesharing in any form is interpreted as a sustainable model of transportation , which has several direct and indirect environmental benefits (Cai et al, 2019;Liu et al, 2021;Tikoudis et al, 2021).…”

“…The practice of ridesharing in any form is interpreted as a sustainable model of transportation (Zhang and Zhang, 2018), which has several direct and indirect environmental benefits (Cai et al. , 2019; Liu et al. , 2021; Tikoudis et al.…”

Understanding Ridesplitting Behavior with Interpretable Machine Learning Models Using Chicago Transportation Network Company Data

“…The authors also study three built environment factors (density, economic prosperity, and land-use mix) and find they are correlated with shared-ride trips. Finally, Liu et al used a localized MOVES (EPA's MOtor Vehicle Emission Simulator) model combined with the GPS trajectories of Didi trips in Chengdu, China and showed that ridesplitting can decrease travel distance by 22% compared with private trips, with average reductions of 10.601 g of CO, 0.691 g OF NO x , and 1.424 g of HC per ridesplitting trip (6).…”

“…Previous research (4)(5)(6) on ridesplitting services has shown their impact on decreasing vehicle miles traveled on the road network, thereby relieving congestion and harmful emissions. When congestion levels increase in cities, it is important to analyze and model people's choices of different services provided by TNCs.…”

Spatiotemporal Analysis of the Relationships Between Pricing Strategies and Willingness to Share for Ridesourcing Modes

“…UberPool is said to save 120 tons of carbon dioxide emissions in San Francisco where it accounts for approximately 50% of Uber rides in the city, and to save 7.9 million driving miles in Los Angeles ( 5 ). Evidence on the potential of this shared ridesourcing model was provided by Santi et al ( 6 ) in a simulation-based study using New York City (NYC) taxi trips, and on the environmental benefits by comparing the shared versus not-shared scenarios’ respective emissions using DiDi Chuxing trips in Chengdu, China ( 7 ). Therefore, understanding the behavior of willingness-to-share (WTS) ridesourcing trips is not only pertinent to comprehending the system from the demand side, but also to boosting and operationalizing those benefits as well.…”