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Predictability Analysis on Expressway Vehicle Mobility Using Electronic Toll Collection Data

机译:电子收费数据高速公路车辆移动性的可预测性分析

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This paper assesses the predictability of individual vehicle's mobility in Beijing expressway system using Electronic Toll Collection (ETC) data records. By examining the uncertainties of movements using entropy, considering both the frequencies and sequential correlations of vehicles' trajectories, we draw to the conclusion that the average limit of predictability of expressway vehicles mobility is 91%. Furthermore, we concluded that the individual property is negatively correlated to its mobility property such as visited station, average travel distance and radius of gyration. Finally, we applied Markov chain (MC) based models to predict the actual accuracy of predictability and found that MC(1) model is adequate to produce a good result and extending the order of the model won't give substantial bonus, thus the first-order Markov property of expressway vehicles' trajectory is proved. Our findings indicate that individual vehicles' mobility in expressway system is far from random and highly dependent on its historical trajectory.
机译:本文评估了使用电子收费(ETC)数据记录的北京高速公路系统在北京高速公路系统中的可预测性。通过检查使用熵的运动的不确定性,考虑到车辆轨迹的频率和顺序相关性,我们得出结论,高速公路车辆移动性的平均可预测性限制为91%。此外,我们得出结论,个体财产与其流动性呈负相关,如访问站,平均旅行距离和血管半径。最后,我们应用了基于Markov链(MAC)的模型来预测预测性的实际精度,发现MC(1)模型足以产生良好的结果,并扩展模型的顺序不会给出大量奖励,因此是第一个证明了高速公路车辆轨迹的Markov属性。我们的调查结果表明,高速公路系统中的个别车辆的流动远离随机,高度依赖于其历史轨迹。

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