Train stop plans and timetables play key roles in railway operation. Previous research has demonstrated that their integration can significantly improve the quality of a train timetable, especially for commuter railways with flexible service frequencies and multiple stop plans. However, solving the dilemma of the mathematical tractability and practicality of the model is still an open challenge. To obtain a high-quality timetable and simultaneously consider more realistic conditions, an integrated combination optimization model of both train stop plans and timetables under time-dependent passenger demand is proposed in this article. More realistic conditions, such as no predefined schedule, a variable total number of trains and oversaturation, are taken into account. The problem is modeled as a mixed-integer nonlinear programming problem (MINLP) to optimize passenger travel efficiency and mainly consists of (1) the total waiting time at stations, (2) the delay time for trains due to a train stop, and (3) the minimization of the total train running time. An extended adaptive large-scale neighborhood search (ALNS) algorithm is developed to solve the problem. A numerical experiment is designed to test the validity of the model and the algorithm. Then, the integrated approach is applied in a real-world case. The results show that the proposed approach can simultaneously reduce the passenger total waiting time and delay time as well as the train running time within a short computation time and demonstrate the effectiveness of the model and the approach.

火车停站计划和时间表在铁路运营中起着关键作用。先前的研究表明，它们的集成可以显着提高火车时刻表的质量，特别是对于具有灵活服务频率和多次停车计划的通勤铁路。然而，解决该模型的数学易处理性和实用性的难题仍然是一个开放的挑战。为了获得高质量的时刻表并同时考虑更现实的条件，本文提出了一个基于时间的旅客需求的列车停靠计划和时刻表的综合组合优化模型。考虑到更现实的条件，例如没有预定义的时间表，火车总数不定和过饱和。该问题被建模为混合整数非线性规划问题（MINLP），以优化旅客出行效率，并且主要包括（1）车站的总等待时间，（2）由于列车停站而导致的火车延迟时间，以及（ 3）减少火车总行驶时间。为了解决该问题，开发了一种扩展的自适应大规模邻域搜索（ALNS）算法。设计了一个数值实验来测试模型和算法的有效性。然后，将集成方法应用于实际案例中。结果表明，所提出的方法可以在较短的计算时间内同时减少乘客的总等待时间和延误时间以及列车运行时间，证明了该模型和方法的有效性。