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Bus Bunching Detection by Mining Sequences of Headway Deviations

机译:挖掘车头间距偏差序列的公交车群检测

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In highly populated urban zones, it is common to notice headway deviations (HD) between pairs of buses. When these events occur in a bus stop, they often cause bus bunching (BB) in the following bus stops. Several proposals have been suggested to mitigate this problem. In this paper, we propose to find BBS (Bunching Black Spots) - sequences of bus stops where systematic HD events cause the formation of BB. We run a sequence mining algorithm, named PrefixSpan, to find interesting events available in time series. We prove that we can accurately model the BB trip usual pattern like a frequent sequence mining problem. The subsequences proved to be a promising way of identify the route' schedule points to adjust in order to mitigate such events.
机译:在人口稠密的城市地区,通常会注意到成对的公交车之间的车距偏差(HD)。当这些事件发生在公交车站时,它们通常会在随后的公交车站中引起公交车集中(BB)。已经提出了一些建议来减轻这个问题。在本文中,我们建议找到BBS(Bunching Black Spots),这是系统性HD事件导致BB形成的公交车站序列。我们运行一个名为PrefixSpan的序列挖掘算法,以查找时间序列中可用的有趣事件。我们证明,我们可以像频繁的序列挖掘问题一样准确地建模BB跳闸的常规模式。事实证明,子序列是一种确定路线时间表以进行调整以减轻此类事件的有前途的方法。

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