Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing
Year: 2024
Type: article
Abstract: Despite the promising benefits that ride-sharing offers, there has been a lack of research on the benefits of high-capacity ride-sharing services. Prior research has also overlooked the relationship between traffic volume and the degree of traffic congestion and emissions. To address these gaps, this study develops an open-source agent-based simulation platform and a heuristic algorithm to quantify the benefits of high-capacity ride-sharing with significantly lower computational costs. The simulation platform integrates a traffic emission model and a speed-density traffic flow model to characterise the interactions between traffic congestion levels and emissions. The experiment results demonstrate that ride-sharing with vehicle capacities of 2, 4, and 6 passengers can alleviate total traffic congestion by approximately 3%, 4%, and 5%, and reduce traffic emissions of a ride-sourcing system by approximately 30%, 45%, and 50%, respectively. This study can guide transportation network companies in designing and managing more efficient and environment-friendly mobility systems.
Authors: Wang Chen, Jintao Ke, Xiqun (Michael) Chen
Institutions: University of Hong Kong, Zhejiang University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4404120436