交通流缺失数据处理方法比较分析

针对交通数据的缺失问题,采用基于时间相关性、空间相关性和时空相关性的多种数据修复方法对缺失数据进行处理.基于时间相关性的修复方法包括历史数据法、移动平均法、指数平滑法和线性回归法等.基于空间相关性的修复方法利用相邻车道和相邻检测器所采集的数据对缺失值进行处理.基于时空相关性的数据修复方法结合交通流的时间相关性与空间相关性对缺失数据进行修复.基于美国加州I-880高速公路交通流数据的实验结果表明,平滑系数α=0.1时的指数平滑法和利用相邻车道数据加权平均法得到的缺失值修复结果最优.

A Comparative Analysis of Data Imputation Methods for Missing Traffic Flow Data

To deal with the missing data problem in traffic flow datasets,a variety of missing data estimation meth-ods,including temporal correlation based methods,spatial correlation based methods,and spatial-temporal correlation based methods,are studied in this paper.The temporal correlation based methods include historical data based method, moving average method,exponential smoothing method,and linear regression method.The spatial correlation based method uses data collected from adjacent lanes and detectors to complete the missing data,while the spatial-temporal cor-relation based method considers both temporal and the spatial correlation of traffic flow.These methods are evaluated by actual traffic data collected from the freeway I-880 in California,USA.The results show that the method of exponential smoothing with smooth coefficient α=0.1,and the weighted average method based on the data of adjacent lanes outper-formed others.