基于模糊时间知识推理的高速铁路列车运行图冲突预测

摘要

Trains are prone to delays and deviations from train operation plans during their operation because of internal or external disturbances. Delays may develop into operational conflicts between adjacent trains as a result of delay propagation, which may disturb the arrangement of the train operation plan and threaten the operational safety of trains. Therefore, reliable conflict prediction results can be valuable references for dispatchers in making more efficient train operation adjustments when conflicts occur. In contrast to the traditional approach to conflict prediction that involves introducing random disturbances, this study addresses the issue of the fuzzification of time intervals in a train timetable based on historical statistics and the modeling of a high-speed railway train timetable based on the concept of a timed Petri net. To measure conflict prediction results more comprehensively, we divided conflicts into potential conflicts and certain conflicts and defined the judgment conditions for both. Two evaluation indexes, one for the deviation of a single train and one for the possibility of conflicts between adjacent train operations, were developed using a formalized computation method. Based on the temporal fuzzy reasoning method, with some adjustment, a new conflict prediction method is proposed, and the results of a simulation example for two scenarios are presented. The results prove that conflict prediction after fuzzy processing of the time intervals of a train timetable is more reliable and practical and can provide helpful information for use in train operation adjustment, train timetable improvement, and other purposes.%列车在运行过程中，由于受到系统内外的干扰，容易发生晚点并偏离计划列车运行线，而晚点传播将进一步扩大运行干扰的影响，造成后续列车潜在的运行冲突，这些冲突可能影响后续列车运行计划的安排。因此，可靠的冲突预测结果能够更好地辅助当前运行调整策略的制定，提高运行图实施效果。相比于既有研究中基于随机干扰的冲突预测方法，本文基于历史运营数据对计划列车运行图中的时间区间进行模糊化处理，并基于赋时Petri网建立高速铁路列车运行图模型。为了全面度量冲突预测结果，本文将冲突划分为确定冲突和潜在冲突并给出判定标准。同时提出了单列车运行线平均偏离度和相邻列车作业间的冲突可能性两个冲突评价指标，并给出了计算方法。基于调整后的模糊时间知识推理算法，本文提出了一种新的高速铁路列车运行冲突预测方法，应用于两个不同情境下的仿真算例中。仿真算例结果表明，列车运行图内时间区间模糊化处理后的冲突预测在可靠性和可操作性方面更强，并可为列车运行图调整、优化等提供决策支持。