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 efficiency 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 automatedly 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.

翻译：本案例研究旨在比较德国伍珀塔尔市深夜时段，定时巴士与按需拼车出租车的服务质量。为评估拼车相对于巴士服务的效率，并模拟夜间时段的巴士行程，我们通过预测模拟生成出行需求。为此，基于R编程语言构建了一个框架，该框架自动结合广义线性模型进行计数回归，以建模每个巴士站点的需求。同时，我们采用分类模型预测行程目的地。为解决由此产生的动态响应式接驳问题，采用基于混合整数线性规划模型迭代求解的滚动时域算法，并通过可行路径启发式方法提升高需求密度场景下的算法性能。据此可估算：在实现与巴士系统相同或更优整体服务质量的前提下，不同工作日所需的出租车数量。