Research on prediction of abnormal overtime behavior of expressway vehicles

Lingyu Huo, YuYang Lei, Jinsong Ye, Lei Zhou
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Abstract

In the expressway network, a large number of abnormal vehicle overtime behaviors occur every day. Currently, there is no efficient detection method. To solve this problem, this paper presents a vehicle full probability prediction optimization model, which can calculate the abnormal probability of other vehicles affected by the event after one vehicle is identified as abnormal vehicle. Further on, this paper establishes multiple events linear estimation model for two probability vehicle types. Finally, this paper presents a probability algorithm of abnormal overtime driving behavior to calculate the abnormal probability of various probability vehicle types.
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高速公路车辆非正常加班行为预测研究
在高速公路网中,每天都有大量异常车辆超时行为发生。目前尚无有效的检测方法。针对这一问题,本文提出了一种车辆全概率预测优化模型,该模型可以在识别出一辆车辆为异常车辆后,计算出受该事件影响的其他车辆的异常概率。在此基础上,建立了两种概率车辆的多事件线性估计模型。最后,本文提出了一种异常超时驾驶行为的概率算法,用于计算各种概率车型的异常概率。
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