基于历史列车交通记录的列车交通仿真算法

S. Watanabe, Y. Mori, Y. Takatori, Kazushige Yonemoto, N. Tomii
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引用次数: 6

摘要

为了精确地模拟列车延误的发生和传播,我们开发了一个列车交通模拟器。我们所说的“情况”是指天气、一周中的某一天、季节等等。众所周知,根据这些情况,延迟的发生和传播是不同的。因此,有必要开发一种能够准确模拟延迟发生差异的仿真算法,以便使用模拟器来评估减少延迟措施的有效性。我们的模拟器的基本思想是使用历史火车交通记录。通过从历史列车交通记录中构造回归树,我们可以知道每种情况下列车的停留时间、运行时间和间隔时间。将回归树的结果与我们基于最长路径算法构建的宏观仿真器相结合,我们可以构建一个准确模拟反映“情境”的列车交通仿真器。我们使用实际数据对模拟器进行了评估,并证实我们的方法非常有前途。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TRAIN TRAFFIC SIMULATION ALGORITHM BASED ON HISTORICAL TRAIN TRAFFIC RECORDS
We have developed a train traffic simulator in order to precisely simulate the delay occurrence and propagation which must be different depending on particular “situations.” By “situation” we mean the weather, a day of the week, season and so on. It is well known that occurrence and propagation of delays are different depending on these situations. Thus, it is necessary to develop a simulation algorithm which can exactly simulate the difference in occurrence of delays in order to use the simulator to evaluate effectiveness of delay reduction measures. The basic idea of our simulator is to use the historical train traffic records. By constructing regression trees from the historical train traffic records, we can know dwell times, running times and intervals of trains in each situation. By incorporating the results obtained from the regression tree to our macroscopic simulator constructed based on the longest path algorithm, we can construct a train traffic simulator which exactly simulate train traffic reflecting the “situation.” We have evaluated the simulator using the actual data and we have confirmed our approach is very promising.
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