Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing (2308.10512v2)

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.

• The paper introduces an open-source agent-based simulation combined with a heuristic algorithm to quantify ride-sharing benefits.
• The study finds that 2-, 4-, and 6-passenger ride-sharing models reduce congestion by approximately 3%, 4%, and 5%, respectively.
• The research demonstrates emissions reductions of about 30%, 45%, and 50% for 2-, 4-, and 6-passenger ride-sharing modes, supporting sustainable urban mobility.

The paper "Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing," published in August 2023, provides a comprehensive investigation into the benefits of high-capacity ride-sharing services. The authors address two significant gaps in the existing research: the impact of ride-sharing services that accommodate multiple passengers and the interplay between traffic volume, congestion, and emissions.

To explore these aspects, the researchers developed an open-source agent-based simulation platform coupled with a heuristic algorithm. This integrated framework offers a robust yet computationally efficient method to quantify the potential benefits of high-capacity ride-sharing. Notably, the simulation platform combines a traffic emission model with a speed-density traffic flow model, thereby enabling a detailed analysis of how ride-sharing interacts with traffic congestion and emissions.

Key findings from the paper indicate notable benefits associated with ride-sharing vehicles of varying capacities:

• 2-passenger ride-sharing: This mode alleviates total traffic congestion by approximately 3% and reduces emissions by about 30%.
• 4-passenger ride-sharing: This mode provides slightly higher benefits, reducing traffic congestion by approximately 4% and cutting emissions by around 45%.
• 6-passenger ride-sharing: As expected, this mode offers the maximum benefits among the configurations tested, alleviating congestion by around 5% and reducing emissions by about 50%.

These results are promising, as they indicate substantial environmental and operational advantages when adopting high-capacity ride-sharing solutions. The findings can serve as a critical resource for transportation network companies, aiding in the design and management of more efficient and eco-friendly transportation systems. By offering concrete evidence of the benefits of higher-capacity ride-sharing, this research adds significant value to discussions on sustainable urban mobility solutions.