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Short-Term Traffic Prediction with Vicinity Gaussian Process in the Presence of Missing Data

机译:存在缺失数据的附近高斯过程短期交通量预测

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This paper considers the problem of short-term traffic flow prediction in the context of missing data and other measurement errors. These can be caused by many factors due to the complexity of the large scale city road network, such as sensors not being operational and communication failures. The proposed method called vicinity Gaussian Processes provides a flexible framework for dealing with missing data and prediction in vehicular traffic network. First, a weighted directed graph of the network is built up. Next, a dissimilarity matrix is derived that accounts for the selection of training subsets. A suitable cost function to find the best subsets is also defined. Experimental results show that with appropriately selected subsets, the prediction root mean square error of the traffic flow obtained by the vicinity Gaussian Processes method reaches 18.9% average improvement with lower costs, which is with comparison to inappropriately chosen training subsets.
机译:本文考虑了在缺少数据和其他测量误差的情况下的短期交通流量预测问题。这些可能是由于大型城市道路网络的复杂性而导致的多种因素,例如传感器无法正常工作和通信故障。所提出的被称为邻近高斯过程的方法为车辆交通网络中的数据丢失和预测提供了灵活的框架。首先,建立网络的加权有向图。接下来,导出差异矩阵,该差异矩阵考虑了训练子集的选择。还定义了找到最佳子集的合适成本函数。实验结果表明,通过选择适当的子集,与不适当选择的训练子集相比,通过邻近高斯过程方法获得的交通流的预测均方根误差平均降低了18.9%,成本更低。

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