A Data-Driven Surrogate Modeling for Train Rescheduling in High-Speed Railway Networks Under Wind-Caused Speed Restrictions

In High-Speed Railway (HSR) networks with hub stations connecting multiple HSR lines, Train Timetable Rescheduling (TTR) under disruptions (such as speed restrictions caused by high wind) has been a challenging problem, which requires collaborative consideration of the traffic and impacts on all lines. Compared to the first principle model of complex railway networks, data-driven modeling provides a better solution to describe how the performance of one HSR line is affected by a train rescheduling decision made for another lines, but it faces the challenges of incompleteness, imbalance and lack of comprehensiveness of history data as disruptions in railways (e.g. delays, accidents) are relatively rare compared to normal operations. This paper proposes a multi-line rescheduling framework consisting of an interactive railway operation simulation and experiment (iROSE) system, a surrogate model and a heuristic algorithm to enable network-wise optimal rescheduling of multiple lines. To compensate for the limits of incomplete history data, a relatively low-cost but accurate enough surrogate model is developed from simulation data of the realistic but computation-intensive iROSE simulator. To reduce the demand for data and the time on running the costly simulator, a multi-surrogate search method is developed. A data expansion-based knowledge transfer method and joint distribution adaptation and tradaboost are also adopted to further improve the accuracy of the surrogate model. Our extensive experiments show that the proposed method can obtain higher precision fine search models with few simulations and solve the problem of TTR under wind-caused speed restrictions in complex railway networks with multiple lines.