Exploring the dynamics of dynamic ride-sharing: insights from a sensitivity analysis with an agent-based simulation

Abstract 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.