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:案例研究的目的是比较德国Wuppertal市按时计时公共汽车的服务质量与按需搭乘的计程车在晚间晚上的服务质量。为了对搭乘公车的效率与公车服务进行比较,并模拟晚间搭乘公车的情况,利用预测模拟方法提出了运输请求。为此,创建了一个程序语言R框架,自动将计算回归的通用线性模型与每辆公车车站的需求模型结合起来。此外,我们使用分类模型来预测出行目的地。为了解决由此产生的动态拨号车问题,使用了基于混合-内装线性编程模型迭接式解决方案的滚动旋曲算法。一种可行的偏向超常法用于提高在高要求密度情况下的算法的性能。这样可以估计周日需要的计程数量,以实现与公车系统相同或更好的一般服务质量。