Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing

TL;DR

This paper presents an agent-based simulation platform and heuristic algorithm to quantify how high-capacity ride-sharing reduces traffic congestion and emissions, demonstrating significant environmental benefits.

Contribution

It introduces a novel open-source simulation platform and heuristic method to evaluate high-capacity ride-sharing impacts on traffic and emissions, filling research gaps.

Findings

Ride-sharing with capacity of 2, 4, and 6 reduces traffic congestion by 3%, 4%, and 5%.

Traffic emissions decrease by approximately 30%, 45%, and 50%.

The platform effectively models interactions between traffic flow and emissions.

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 characterize 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.