Assessing the impacts of ridesharing services: An agent-based simulation approach. (20th October 2022)

• Record Type: Journal Article
• Authors: Sun, Ruixiao; Wu, Xuanke; Chen, Yuche
• Is Part Of: Journal of cleaner production. Volume 372(2022)
• Publication Date: 2022-10-20
• Subjects: Ridesharing -- Traffic simulation -- SUMO -- Vehicle emission; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.133664
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: A shift from privately owned vehicles to shared mobility services can affect mobility, energy consumption, and vehicle emissions. Existing literature on ridesharing services has focused on evaluating its traffic and economic impacts. In this study, we propose an integrated framework to analyze the efficiency and environmental benefits of ridesharing on a regional scale. The framework utilizes an agent-based traffic simulation package (i.e., SUMO) to replicate traffic activities for commuting trips in a mid-size city, Chattanooga, Tennessee, based on real-world travel-demand data. We construct scenarios representing different ridesharing strategies and penetrations. The simulation and results analysis show that with a ridesharing ratio of 5%–75% over travel demand in a city scale, many (65%–75%) ridesharing travelers will experience up to a 15-min delay. About 80% of drive-alone travelers will arrive earlier compared with no ridesharing scenario. The average early arrival time would be 5.6 min for all drive-alone travelers. The results also show ridesharing services can achieve a 2%–50% reduction in total city-scale vehicle emissions and energy consumption compared with the no ridesharing scenario. The framework and results of this study can be helpful to transportation practitioners to evaluate environmental benefits when implementing ridesharing services on a city scale. Highlights: Develop an agent-based simulation platform to replicate traffic activities. Use real-world OD demand and a heuristic algorithm for matching ridesharing trips. Evaluate trip delays, energy, and environmental impacts for ridesharing scenarios. Demonstrate benefits of ridesharing in congestion reduction and emission mitigation.