基于时空关联和异构图卷积的车道级流量预测

Lane-level traffic flow prediction based on spatiotemporal correlation and heterogeneous graph convolution

摘要:

为细化交通流量预测的空间粒度,解决传统方法忽略车道差异作用机理带来的局限性,以车道为研究对象,提出了一种精准实时的车道级交通流量预测方法,考虑车道位置导致的流量时序特征差异性,以软动态时间规整作为度量方法,利用k均值聚类算法对车道断面进行分类,分析各类型车道流量时序特征；引入逐步变分模态分解降低序列波动性,去除噪声干扰,将得到的分量作为预测模型的输入,融入斯皮尔曼相关性分析获取车道相关性关系；在双向门控循环单元嵌入多头自注意力机制,结合车道间距和车道相关性的双重边关系构建异构时空图卷积循环神经网络模型对各类车道流量进行预测。应用山西省某高速公路一个月流量数据进行测试,结果表明:所选区域车道类型分为4类最佳,分别为密集型车道、稀疏型车道、早高峰型车道和晚高峰型车道；对所有车道分别进行流量预测,与自回归移动平均、长短时记忆网络以及时空图卷积神经网络等模型相比,5 min时间粒度下,所提模型的平均绝对误差和均方根误差分别降低了11.21%~24.05%、8.89%~24.43%,决定系数可达0.962；车道分类后的预测精度进一步提升；当15 min粒度下步长为12以内预测时,所提模型平均绝对误差和均方根误差最高增加了15.82%和11.99%。研究成果可为道路规划和智能交通的发展提供依据。

Abstract:

A lane-level traffic flow prediction method was proposed to refine the spatial granularity of traffic flow prediction and address the limitations in traditional prediction methods which overlooked the interaction mechanisms brought by the lane differences. The soft-dynamic time warping was chosen as the measurement method to consider the variations in traffic flow time series caused by the lane positions. Then, the k-means algorithm was aggregated in the proposed algorithm to classify the lane cross-sections, which was utilized to analyze the temporal characteristics of flow at the selected lane cross-section type. The sequential variational mode decomposition was introduced to reduce the volatility of time series flow data and remove its noise, whose results can serve as inputs for the prediction model. Based on the previous results, the lane correlations can be determined by incorporating the Spearman correlation analysis in the proposed method. The heterogeneous spatiotemporal graph convolutional recurrent neural network model for predicting the flow of various lane types can be established by embedding the multi-head attention mechanism into the bidirectional gated recurrent unit and combining the lane correlations and their distance. The traffic count data from several freeways in Shanxi, China was selected to assess the effectiveness of the proposed method. The results indicate that the freeway′s lanes within the selected area can be classified to four categories: dense type, sparse type, morning peak type, and evening peak type, respectively. Compared to the autoregressive integrated moving average model, long-short term memory network model, and spatio-temporal graph convolutional network models, the proposed model reduces the mean absolute error and root mean square error by 11.21%-24.05% and 8.89%-24.43%, with the r-squared coefficient as 0.962 at the 5-minute granularity flow prediction for all the lanes. This result indicates the accuracy of the flow prediction can be further improved by considering the lane classification. When the prediction was accomplished at the 5-minute granularity with the step size within 12, the mean absolute error and root mean square error of the proposed model increased by 15.82% and 11.99% at most. The findings provide a basis for road planning and the development of intelligent transportation.