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Predictability analysis on expressway vehicle mobility using electronic toll collection data

机译:电子收费数据对高速公路车辆机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:本文利用电子收费系统(ETC)数据记录评估了北京高速公路系统中单个车辆的机动性的可预测性。通过使用熵检查运动的不确定性,同时考虑车辆轨迹的频率和顺序相关性,我们得出的结论是,高速公路车辆移动性的可预测性的平均极限为91%。此外,我们得出的结论是,个体属性与其移动性属性(例如访问站,平均行进距离和回转半径)负相关。最后,我们应用了基于马尔可夫链(MC)的模型来预测可预测性的实际准确性,发现MC(1)模型足以产生良好的结果,并且扩展模型的阶数不会带来可观的收益,因此,第一个证明了高速公路车辆轨迹的马尔可夫性质。我们的研究结果表明,高速公路系统中单个车辆的机动性不是随机的,而是高度依赖于其历史轨迹。

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