This study delves into the potential of dynamic ride-sharing (DRS) systems utilizing the agent-based simulation framework MATSim. Through a comprehensive sensitivity analysis across various scenarios, we investigate the efficacy of a newly developed dynamic ride-sharing extension and unveil key insights. Our findings underscore the pivotal role of user willingness in driving DRS utilization, emphasizing the necessity of flexible departure times to accommodate diverse user preferences. Furthermore, we advocate for the inclusion of short trips within DRS options and highlight the efficacy of incentivizing DRS drivers, albeit with caution regarding unintended consequences such as modal shifts. Despite observing an increase in Vehicle Kilometers Traveled after DRS implementation, our study elucidates the nuanced nature of this increase, particularly regarding unmatched DRS drivers. In a “maximum scenario”, we identify the utmost potential for DRS adoption, shedding light on its viability under conducive circumstances and offering valuable insights for future transportation planning and policy-making.

本研究深入探讨了利用基于智能体的仿真框架 MATSim 的动态拼车 （DRS） 系统的潜力。通过对各种场景的全面敏感性分析，我们调查了新开发的动态拼车扩展的有效性，并揭示了关键见解。我们的研究结果强调了用户意愿在推动 DRS 利用率方面的关键作用，强调了灵活的出发时间以适应不同用户偏好的必要性。此外，我们主张将短途旅行纳入 DRS 选项，并强调激励 DRS 司机的有效性，尽管对模式转换等意外后果持谨慎态度。尽管观察到 DRS 实施后车辆行驶里程数有所增加，但我们的研究阐明了这种增加的细微差别，特别是关于无与伦比的 DRS 司机。在“最大情景”中，我们确定了 DRS 采用的最大潜力，阐明了其在有利情况下的可行性，并为未来的交通规划和政策制定提供了有价值的见解。