Optimization and evaluation for autonomous taxi ride-sharing schedule and depot location from the perspective of energy consumption

• Published in: Applied energy 2022-02, Vol.308, p.118388, Article 118388
• Main Authors: Dai, Rongjian, Ding, Chuan, Gao, Jian, Wu, Xinkai, Yu, Bin
• Format: Article
• Language: English
• Subjects: Autonomous vehicle; Energy efficiency; Reservation system; Ride-sharing; Vehicle dispatching
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2021.118388

•A reservation system for autonomous taxi taxi ride-sharing is designed.•Autonomous taxi ride-sharing schedule and depot location are optimized.•Effectiveness in reduction of vehicle usage and energy consumption is analyzed. Taxi ride-sharing based on autonomous vehicles (AVs) is seen as a new and promising mode of urban mobility to promise a huge social and environmental benefits, such as conserving fuel, mitigating traffic congestions, reducing air pollution. Although a number of studies have focused on taxipooling or taxi ride-sharing, majority of these studies just concerned about the vehicle routing problem. Limited studies were conducted to excavate and understand the potential benefits of this new mobility mode through a comprehensively designed taxi ride-sharing system. To fill this gap, this study tries to design an autonomous taxi ride-sharing system for commuting trips from the perspective of energy consumption, in which schedule (i.e., taxi type, taxi path, feet size) and depot location are optimized within a unified model. Based on this, we further evaluate its effectiveness in reductions of energy consumption and vehicle usage, and analyze the influences of several key factors on the system efficiency. Case studies show that the taxi ride-sharing service mode needs fewer vehicles than the private car travel mode, and outperforms the traditional taxi service mode in terms of fuel consumption. Moreover, it is found that trip density is an important influence factor on the benefits of taxi ride-sharing system. This study aims to provide transportation managers a good understanding of the energy benefits of well-designed autonomous taxi ride-sharing system.