Efficient missing data imputing for traffic flow by considering temporal and spatial dependence
Efficient missing data imputing for traffic flow by considering temporal and spatial dependence
复制标题
通过考虑时间和空间依赖性，对交通流进行有效的缺失数据插补
DOI：
10.1016/j.trc.2013.05.008
复制
发表时间：
2013-09-01
影响因子：
8.3
通讯作者：
Li, Zhiheng
中科院分区：
文献类型：
--
作者：
Li, Li;Li, Yuebiao;Li, Zhiheng
缺失数据问题仍然是各种交通应用（例如交通流预测和交通模式识别）中的一个难题。为了解决这个问题，在过去的十年中，已经提出了许多算法来估算丢失的数据。然而，现有的研究很少充分利用相邻检测点的交通流信息来提高估算性能。基于概率主成分分析（PPCA）的估算方法是一种不依赖时间和空间信息的有效估算方法，本文将其推广到利用多点信息。我们系统地研究了多点数据融合的潜在好处，并研究了测量时间滞后的可能影响。实验结果表明，隐藏的时空相关性是非线性的，基于核概率主成分分析（KPPCA）的方法比PPCA方法能更好地恢复时空相关性。比较结果表明，如果适当考虑时空相关性，可以显著减少估算误差。(C)2013爱思唯尔有限公司保留所有权利。
The missing data problem remains as a difficulty in a diverse variety of transportation applications, e.g. traffic flow prediction and traffic pattern recognition. To solve this problem, numerous algorithms had been proposed in the last decade to impute the missed data. However, few existing studies had fully used the traffic flow information of neighboring detecting points to improve imputing performance. In this paper, probabilistic principle component analysis (PPCA) based imputing method, which had been proven to be one of the most effective imputing methods without using temporal or spatial dependence, is extended to utilize the information of multiple points. We systematically examine the potential benefits of multi-point data fusion and study the possible influence of measurement time lags. Tests indicate that the hidden temporal-spatial dependence is nonlinear and could be better retrieved by kernel probabilistic principle component analysis (KPPCA) based method rather than PPCA method. Comparison proves that imputing errors can be notably reduced, if temporal-spatial dependence has been appropriately considered. (C) 2013 Elsevier Ltd. All rights reserved.