Bus travel time prediction using a time-space discretization approach
Bus travel time prediction using a time-space discretization approach
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DOI：
10.1016/j.trc.2017.04.002
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发表时间：
2017-06-01
影响因子：
8.3
通讯作者：
Subramanian, Shankar C.
中科院分区：
文献类型：
--
作者：
Kumar, B. Anil;Vanajakshi, Lelitha;Subramanian, Shankar C.
提供给乘客的旅行时间信息的准确性对任何先进公共交通系统(APTS)应用的成功起着关键作用。为了提高这类应用的准确性，人们应该仔细开发一种预测方法。大多数现有的预测方法都考虑了旅行时间在空间或时间上的变化。本研究开发了一种预测方法，该方法同时考虑了旅行时间的时间和空间变化。首先利用交通流模型将车辆关于流量和密度的守恒方程以速度的形式重写为偏微分方程式。然后，利用Godunov格式对建立的基于速度的方程进行离散化，并将其用于基于卡尔曼滤波的预测方案。结果表明，该方法比历史平均法、回归法和人工神经网络方法以及仅考虑时间和空间变化的方法具有更好的性能。最后，建立了一个公式来检验支路对预测精度的影响，发现在基于位置的数据方面的额外要求并没有导致预测精度的显著变化。这清楚地表明，基于车辆跟踪数据的方法对于考虑应用于公交车行程时间预测是足够好的。(C)2017爱思唯尔有限公司。保留所有权利。
The accuracy of travel time information given to passengers plays a key role in the success of any Advanced Public Transportation Systems (APTS) application. In order to improve the accuracy of such applications, one should carefully develop a prediction method. A majority of the available prediction methods considered the variation in travel time either spatially or temporally. The present study developed a prediction method that considers both temporal and spatial variations in travel time. The conservation of vehicles equation in terms of flow and density was first re-written in terms of speed in the form of a partial differential equation using traffic stream models. Then, the developed speed based equation was discretized using the Godunov scheme and used in the prediction scheme that was based on the Kalman filter. From the results, it was found that the proposed method was able to perform better than historical average, regression, and ANN methods and the methods that considered either temporal or spatial variations alone. Finally, a formulation was developed to check the effect of side roads on prediction accuracy and it was found that the additional requirement in terms of location based data did not result in an appreciable change in the prediction accuracy. This clearly demonstrated that the proposed approach based on using vehicle tracking data is good enough for the considered application of bus travel time prediction. (C) 2017 Elsevier Ltd. All rights reserved.