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. Note to Practitioners—This paper was motivated by the Train Timetable Rescheduling problem of complex high-speed railway networks of multiple lines connected via hub stations, in which the delays caused by high-wind speed restrictions on one line may easily affect trains on other lines in the network. Thus the impacts of a local-line TTR decision on other parts of the HSR network should be evaluated appropriately in the sense of precision and real-time, to assist the local dispatcher in making a network-wise decision. However, the incomplete and imbalanced historical data may not accurately capture how the system behaves during disruptions. In order to address these challenges, this paper proposes a data-driven rescheduling optimization framework to allow network-wise optimal decision-making. The proposed framework consists of an on-demand iROSE system, a surrogate model representing the operation performance of the whole railway network, and a heuristic method responsible for the traffic rescheduling of partial HSR lines. The realistic iROSE simulator is able to compensate the imbalanced actual history operation data by giving a precise evaluation of the network’s performance. Then a multi-surrogate search method and a knowledge transfer method are developed to avoid the time-consuming caused by expensive simulation. The developed surrogate model is able to capture the insights of delay propagation in a multi-line HSR network and enable the dispatchers to have a quick and comprehensive evaluation of how a TTR rescheduling decision made for one line affects other lines in the network. As a result, a network-wise better decision on train rescheduling can be made.