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Anomalous trajectory detection from taxi GPS traces using combination of iBAT and DTW

机译:使用IBAT和DTW组合的出租车GPS痕迹的异常轨迹检测

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The competition between taxi companies triggers the occurrence of taxi driving frauds. The traces of driving frauds often significantly deviate from normal ones. It is possible to automatically detect the anomalous trajectory by mining historical GPS traces. Uniform grids are used to represent the trajectory in isolation-Based Anomalous Trajectory (iBAT) method and used to address the issue of GPS traces. However, the uniform grid has its own challenges where the trajectories having similar patterns can generate different sequence of grid cells. This situation allows a normal trajectory to be detected as an anomaly. In this study, we proposed the combination of iBAT and similarity measurement like DTW. By combining iBAT and DTW return the lowest false alarm rate (FAR), which is 0,027. Based on the result, the proposed method is proven to be able to minimize normal trajectory is detected as an anomaly.
机译:出租车公司之间的竞争致力于出租车驾驶欺诈的发生。驾驶欺诈的痕迹通常偏离正常的迹象。可以通过挖掘历史GPS迹线自动检测异常轨迹。均匀网格用于代表基于隔离的异常轨迹(IBAT)方法中的轨迹,并用于解决GPS迹线的问题。然而,均匀的网格具有其自身的挑战,其中具有类似模式的轨迹可以产生不同的网格细胞序列。这种情况允许被检测为异常的正常轨迹。在这项研究中,我们提出了IBAT和相似性测量的组合,如DTW。通过组合IBAT和DTW返回最低的误报率(远),即0,027。基于该结果,已被证明能够将拟议的方法最小化正常轨迹被检测为异常。

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