Modeling the application of train operation adjustment actions to recover from delays is of great importance to supporting the decision-making of dispatchers. In this study, the effects of two train operation adjustment actions on train delay recovery were explored using train operation records from scheduled and actual train timetables. First, the modeling data were sorted to extract the possible influencing factors under two typical train operation adjustment actions, namely the compression of the train dwell time at stations and the compression of the train running time in sections. Stepwise regression methods were then employed to determine the importance of the influencing factors corresponding to the train delay recovery time, namely the delay time, the scheduled supplement time, the running interval, the occurrence time, and the place where the delay occurred, under the two train operation adjustment actions. Finally, the gradient-boosted regression tree (GBRT) algorithm was applied to construct a delay recovery model to predict the delay recovery effects of the train operation adjustment actions. A comparison of the prediction results of the GBRT model with those of a random forest model confirmed the better performance of the GBRT prediction model.

对列车运行调整动作的应用进行建模以从延迟中恢复的模型对于支持调度员的决策非常重要。在这项研究中，利用计划和实际列车时刻表中的列车运行记录，探索了两种列车运行调整措施对列车延误恢复的影响。首先，对建模数据进行分类，以提取两种典型的列车运行调整动作下可能的影响因素，即车站的列车停留时间的压缩和分段中列车运行时间的压缩。然后采用逐步回归方法来确定与火车延误恢复时间相对应的影响因素的重要性，即延误时间，计划的补给时间，行驶间隔，发生时间，以及两次列车运行调整动作下发生延误的地点。最后，采用梯度增强回归树算法（GBRT）构造了延迟恢复模型，以预测列车运行调整动作的延迟恢复效果。将GBRT模型的预测结果与随机森林模型的预测结果进行比较，证实了GBRT预测模型的较好性能。