一种基于张量的车辆交通数据缺失估计新方法

New Method of Data Missing Estimation for Vehicle Traffic Based on Tensor

面对当前庞大的智慧交通数据量,收集并统计处理是必要且重要的过程,但无法避免的数据缺失问题是目前的研究重点。文中针对车辆交通数据缺失问题提出一种基于张量的车辆交通数据缺失估计新方法:集成贝叶斯张量分解(Integrated Bayesian Tensor Decomposition,IBTD)。该算法在数据模型构建阶段,利用随机采样原理,将缺失数据随机抽取生成数据子集,并用优化后的贝叶斯张量分解算法进行插补。引入集成思想,将多个插补后的误差结果进行分析排序,考虑时空复杂度,择优平均得到最优结果。通过平均绝对百分比误差之后(Mean Absolute Percentage Error,MAPE)和均方根误差(Root Mean Square Error,RMSE)对提出模型的性能进行评估。实验结果表明,所提新方法能够有效地对不同缺失量的交通数据集进行插补,并能得到很好的插补结果。

In the face of the current huge amount of intelligent traffic data,collecting and statistical processing is a necessary and important process,but the problem of inevitable data missing is the current research focus.Aiming at the problem of vehicle traffic data missing,this paper proposed a new method based on tensor for vehicle traffic data missing estimation,Integrated Bayesian tensor decomposition(IBTD).In the data model construction stage,the random sampling principle was used to randomly extract the missing data to generate a subset of data,and the optimized Bayesian tensor decomposition algorithm was used for interpolation.By introducing the integration idea,the error results after multiple interpolations were analyzed and sorted,consider the spatio-temporal complexity,and choose the optimal average to get the best result.The performance of the proposed model was evalua-ted by mean absolute percentage error(MAPE)and root mean square error(RMSE).Experimental results show that the proposed method can effectively interpolate the traffic datasets with different missing quantities and get good interpolation results.