面向高速公路大数据的短时流量预测方法

针对高速公路传统的短时交通流预测方法适用数据规模小、全网预测效率较低、数据的时空关系被忽视等问题,提出一种结合了K近邻(KNN)模型且面向高速大数据的短时交通流预测方法。首先,对模型的K值和距离度量进行调优,利用交叉验证进行模型参数的对比实验;然后,考虑数据内在的业务时空关联,建模基于时空特性的特征向量;最后,在大数据环境下建立回归预测模型,以最优参数的模型实现预测。实验结果表明,与传统时间序列模型相比,所提方法一次可预测出全站点的流量,单次运行速度快,效率提高了77%,平均绝对百分比误差(MAPE)和绝对百分比误差中位数(MDAPE)均有明显减低,且具有良好的水平扩展性。

Short-term traffic prediction method on big data in highway domain

Aiming at the problems that traditional short-time traffic flow prediction method in highway domain is suitable for small scale data, which limits the efficiency on massive data, and the spatio-temporal relationship of data is neglected, a short-term traffic flow prediction method for big data with combining K-Nearest Neighbors (KNN) in highway domain was proposed. Firstly, the K value and distance metric of model were tuned, and the model parameters were compared by using cross validation. Secondly, considering inherent spatio-temporal association of data, feature vectors based on spatio-temporal characteristics were modeled. Finally, a regression prediction model was established under big data environment, and the prediction was realized with the model of optimal parameters. The experimental results show that compared with traditional time series model, the proposed model works on all toll stations at one time, has high speed of single running and improves the efficiency by 77%. The method significantly reduces Mean Absolute Percentage Error (MAPE) and Median Absolute Percentage Error (MDAPE) and it also has good horizontal expansibility.