دانلود رایگان مقاله پیش بینی جدول زمانی درگیری سرعت بالا راه آهن قطار بر اساس استدلال دانش زمانی فازی

Abstract

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.

5. Conclusions and future work

Compared to random disturbances, the fuzzy processing of time intervals in a train timetable based on historical time statistics is closer to the actual conditions. This is the foundation for obscuring the train timetable and predicting PCs based on the fuzzy temporal knowledge reasoning method. The simulation experiment was designed under two different scenarios and, according to the result comparison and analysis, we can draw the following conclusions. (1) A fuzzy train timetable may have PCs, and the indexes proposed by this paper will help traffic management units to know the quality of the planned train timetable and what the causes of conflict are. This knowledge will assist in timetabling, especially in the preset buffer time distribution. (2) Conflict prediction based on fuzzy temporal knowledge reasoning provides more available information for the dispatchers, compared to the in-use method, which deviates from the actual state by assuming that the subsequent train plan will not be disturbed at all. The conflict prediction simulated by this new method will help the dispatcher to master the comprehensive influence of the new operational circumstance and to evaluate the adjustment effect by traversing the prediction algorithm. (3) An interesting finding during the simulation result analysis was that additional delay may eliminate a PC; this finding goes against the common-sense assumption that a delay is always a bad thing and the chief culprit in reducing the flexibility and robustness of a train timetable. This finding provides us with the insight that a certain amount of habitual delay can somehow be incorporated into the preset buffer time in order to avoid or resolve headway conflicts. Future research is recommended in the following directions: First, an impact analysis should be performed on the size and network distribution of the time allowance and the time intervals on train delay propagation; and second, an examination should be done on how to use fuzzy temporal knowledge reasoning results to effectively support train rescheduling in real time.