In conventional railway planning processes, stop-skipping decisions are often made at the line planning stage, which is executed prior to train timetabling and platform assignment. However, stop-skipping can shorten passenger journey time and also save on train operating costs. Hence, integrating train timetabling, stop-skipping, and platform choice decisions can help generate train timetables with improved passenger convenience and higher train operating efficiency. Integrating these decisions is a challenging task, as these decisions affect passenger train transfer behavior, which in turn affects the entire passenger flow. This study is a first attempt at integrating these decisions while simultaneously taking into account the passenger flow. We consider a train timetabling problem on a single, one-way track with stop-skipping, platform choice, and passenger flow considerations, and we formulate it as a constrained minimum-cost multi-commodity network flow problem on a time–space network. We analyze the problem’s complexity and develop a Lagrangian relaxation heuristic to solve the problem. We conduct a computational study with randomly generated data that captures the characteristics of the Beijing–Shanghai high-speed railway line. The computational results report the effectiveness of our Lagrangian relaxation heuristic and how the railway’s service capacity and passenger traffic intensity affect the solution.

在传统的铁路规划流程中，停站决策通常是在线路规划阶段做出的，该阶段在列车时刻表和站台分配之前执行。然而，停站可以缩短乘客的旅程时间，也可以节省列车运营成本。因此，整合列车时刻表、停站和站台选择决策可以帮助生成具有更高乘客便利性和更高列车运行效率的列车时刻表。整合这些决策是一项具有挑战性的任务，因为这些决策会影响旅客列车换乘行为，进而影响整个客流。本研究首次尝试整合这些决策，同时考虑客流。我们考虑在单一单向轨道上的列车时刻表问题，其中包含停站、站台选择、和客流考虑，我们将其表述为时空网络上的约束最小成本多商品网络流问题。我们分析问题的复杂性并开发拉格朗日松弛启发式来解决问题。我们使用随机生成的数据进行计算研究，这些数据捕捉了京沪高速铁路线的特征。计算结果报告了我们的拉格朗日松弛启发式的有效性以及铁路的服务能力和客运强度如何影响解决方案。我们使用随机生成的数据进行计算研究，这些数据捕捉了京沪高速铁路线的特征。计算结果报告了我们的拉格朗日松弛启发式的有效性以及铁路的服务能力和客运强度如何影响解决方案。我们使用随机生成的数据进行计算研究，这些数据捕捉了京沪高速铁路线的特征。计算结果报告了我们的拉格朗日松弛启发式的有效性以及铁路的服务能力和客运强度如何影响解决方案。