Vehicle automation is expected to reduce the cost of shared demand-responsive transport (DRT) services. In this context, questions regarding the conditions under which fixed-route public transport can be replaced with shared on-demand services have emerged. The expected increase in competitiveness between fixed-route and on-demand services requires the development of frameworks that enable the analysis of transportation costs of alternative modes. In this research, we develop a total cost minimization model for demand-responsive door-to-door shared transportation services, including operator and user costs. Optimization variables are vehicle size and fleet size for operation with human-driven and automated vehicles. A hybrid approach is used in which the relevant variables are analytically and numerically modeled, using data from a large-scale agent-based simulation applied to the city of Munich. We compare the case in which all trip requests must be served with the case in which request rejections are allowed, based on waiting and travel times. Different demand levels and alternative scenarios for vehicle automation are analyzed. The results indicate that the performance of door-to-door on-demand shared systems depends on the operational scheme selected. For the DRT setup and vehicle assignment strategy studied, we find that if the system is forced to have no trip rejections, economies of scale are not present, and the high user costs hinder the system’s competitiveness even under the assumption of automated vehicles. In contrast, in a system that allows for trip rejections, economies of scale are present, and vehicle automation can especially reduce operator costs, increasing the system’s competitiveness against other transportation modes. Therefore, in our setting, the efficiency of the demand-responsive service depends on the ability to reject customers, which is against the spirit of a truly public transportation service. On scenario analysis, we show that a theoretical improvement in the performance of the real-time vehicle assignment strategy can significantly reduce total cost, with economies of scale under no-rejection operation. Future research needs to address whether the actual application of more complex vehicle assignment strategies can indeed make DRT systems more cost competitive while serving all trip requests.

车辆自动化有望降低共享需求响应运输 (DRT) 服务的成本。在这种情况下，出现了关于固定路线公共交通可以被共享的按需服务取代的条件的问题。固定路线和按需服务之间竞争力的预期提高需要开发能够分析替代模式的运输成本的框架。在这项研究中，我们为响应需求的门到门共享运输服务开发了一个总成本最小化模型，包括运营商和用户成本。优化变量是用于人类驾驶和自动驾驶车辆操作的车辆规模和车队规模。使用混合方法，其中相关变量被分析和数值模拟，使用来自应用于慕尼黑市的大规模基于代理的模拟的数据。我们根据等待时间和旅行时间，比较了必须处理所有行程请求的情况与允许拒绝请求的情况。分析了车辆自动化的不同需求水平和替代方案。结果表明，门到门按需共享系统的性能取决于所选的操作方案。对于所研究的 DRT 设置和车辆分配策略，我们发现如果系统被迫没有行程拒绝，则不存在规模经济，即使在假设自动车辆的情况下，高用户成本也会阻碍系统的竞争力。相反，在允许拒绝行程的系统中，存在规模经济，车辆自动化尤其可以降低运营成本，提高系统相对于其他交通方式的竞争力。因此，在我们的设置中，需求响应服务的效率取决于拒绝客户的能力，这与真正公共交通服务的精神背道而驰。在情景分析中，我们表明实时车辆分配策略性能的理论改进可以显着降低总成本，在无拒绝操作下具有规模经济。未来的研究需要解决更复杂的车辆分配策略的实际应用是否确实可以使 DRT 系统在满足所有出行请求的同时更具成本竞争力。需求响应服务的效率取决于拒绝客户的能力，这与真正公共交通服务的精神背道而驰。在情景分析中，我们表明实时车辆分配策略性能的理论改进可以显着降低总成本，在无拒绝操作下具有规模经济。未来的研究需要解决更复杂的车辆分配策略的实际应用是否确实可以使 DRT 系统在满足所有出行请求的同时更具成本竞争力。需求响应服务的效率取决于拒绝客户的能力，这与真正公共交通服务的精神背道而驰。在情景分析中，我们表明实时车辆分配策略性能的理论改进可以显着降低总成本，在无拒绝操作下具有规模经济。未来的研究需要解决更复杂的车辆分配策略的实际应用是否确实可以使 DRT 系统在满足所有出行请求的同时更具成本竞争力。我们表明，实时车辆分配策略性能的理论改进可以显着降低总成本，并在无拒绝操作下实现规模经济。未来的研究需要解决更复杂的车辆分配策略的实际应用是否确实可以使 DRT 系统在满足所有出行请求的同时更具成本竞争力。我们表明，实时车辆分配策略性能的理论改进可以显着降低总成本，并在无拒绝操作下实现规模经济。未来的研究需要解决更复杂的车辆分配策略的实际应用是否确实可以使 DRT 系统在满足所有出行请求的同时更具成本竞争力。