拼车与公共巴士服务：比较案例研究
Ridepooling and public bus services: A comparative case-study
AI总结：
本研究比较了德国伍珀塔尔市晚间巴士与按需拼车服务的质量，通过R语言框架结合广义线性模型与分类模型模拟需求，并利用基于MILP的滚动视野算法和可行路径启发式方法求解动态乘车问题，估算了达到同等或更优服务质量所需的出租车数量。
摘要：
本案例研究旨在比较德国伍珀塔尔市晚间时段按时刻表运行的巴士与按需拼车出租车的服务质量。为评估拼车相较于巴士服务的服务质量，并模拟晚间时段的巴士行程，使用预测性模拟生成运输请求。为此，在R语言中创建了一个框架，该框架自动结合用于计数回归的广义线性模型来模拟每个公交站的需求。此外，使用分类模型预测行程目的地。为解决由此产生的动态拨号乘车问题，使用基于混合整数线性规划模型（MILP）迭代求解的滚动视野算法。在高请求密度下，使用可行路径启发式方法来提升算法性能。由此可估算出为实现与巴士系统相同或更优的整体服务质量，所需的出租车数量随工作日的变化情况。
英文摘要：
This case-study aims at a comparison of the service quality of time-tabled buses as compared to on-demand ridepooling cabs in the late evening hours in the city of Wuppertal, Germany. To evaluate the service quality of ridepooling as compared to bus services, and to simulate bus rides during the evening hours, transport requests are generated using a predictive simulation. To this end, a framework in the programming language R is created, which automatically combines generalized linear models for count regression to model the demand at each bus stop. Furthermore, we use classification models for the prediction of trip destinations. To solve the resulting dynamic dial-a-ride problem, a rolling-horizon algorithm based on the iterative solution of Mixed-Integer Linear Programming Models (MILP) is used. A feasible-path heuristic is used to enhance the performance of the algorithm in presence of high request densities. This allows an estimation of the number of cabs needed depending on the weekday to realize the same or a better general service quality as the bus system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
