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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。
In the process of running, the trains are prone to delay and deviate from the planned train running line due to the interference inside and outside the system. The late propagation will further expand the influence of running disturbances and cause potential running conflicts of subsequent trains, which may affect subsequent train running plans s arrangement. Therefore, reliable conflict prediction results can better assist the formulation of the current operation adjustment strategy and improve the implementation effect of the operation diagram. Compared with the conflict prediction method based on stochastic disturbances in existing researches, this paper fuzzified the time interval of the planned train operation map based on the historical operation data and established the train operation diagram model of the high-speed railway based on the time Petri nets. In order to comprehensively measure the conflict prediction results, this paper divides the conflict into the definition of the conflict and the potential conflict and gives the criterion. At the same time, two conflicting evaluation indexes of average deviation of single train running line and possibility of collision between adjacent train jobs are proposed, and the calculation methods are given. Based on the adjusted fuzzy inference knowledge reasoning algorithm, this paper presents a new method of train operation conflict prediction in high-speed railway, which is applied in two different cases of simulation. The simulation results show that the conflict prediction after the fuzzification of the time interval in the train operation map is more reliable and maneuverable, and can provide decision support for the adjustment and optimization of the train operation diagram.