基于时间图注意力的交通流量预测模型

交通状况预测是智能交通系统的一个重要组成部分,而车流量是交通状况最直接的体现,因而对交通流量进行预测具有重要的应用价值。一方面,城市中的道路本身带有空间拓扑性质,另一方面车流量随时间动态变化。因此交通流量预测问题的关键在于对数据中存在的时间和空间依赖进行建模。针对这一特性,使用神经网络模型和注意力机制来探索交通流量数据中的时空依赖关系,提出基于时间图注意力的交通流量预测模型。空间依赖方面,使用图卷积网络与注意力结合的学习算法对不同影响程度节点分配不同的权重,加入节点自适应学习,有效提取空间特征;时间依赖方面,使用时序卷积网络对时间特征进行提取,通过扩张卷积扩大感受域从而捕获较长时间序列数据的特征。由图注意力网络和时间卷积网络构成一个时空网络层,最终连接到输出层输出预测结果。该模型使用图卷积神经网络和注意力机制结合的方式提取空间特征,充分考虑了道路间的空间关系,利用时序卷积网络捕获时间特征。在两个真实的数据集上进行实验后发现,在未来15 min、30 min、60 min的时间段内该模型都有良好表现,结果优于现有基准模型。

Traffic flow prediction model based on attention mechanism of temporal graph

Traffic condition prediction is an important part of intelligent transportation system, and traffic flow is the most direct embodiment of traffic condition. Therefore, traffic flow prediction has important application value. On the one hand, the roads in the city have spatial topological properties, on the other hand, the traffic flow changes dynamically with time. Therefore, the key to the prediction of traffic flow is to model the time and space dependence in the data. In view of this characteristic, this paper used neural network model and attention mechanism to explore the temporal and spatial dependence relationship in traffic flow data, and proposed a traffic flow prediction model based on time map attention. In terms of spatial dependence, it used a learning algorithm combining graph convolution network and attention to assign different weights to nodes with diffe-rent influence degrees, and added node adaptive learning to effectively extract spatial features. In terms of time dependence, it used the temporal convolution network to extract the temporal features, and expanded the sensing domain by expanding convolution, so as to capture the features of longer time series data. A spatial-temporal network layer was composed of graph attention network and time convolution network, which was finally connected to the output layer to output the prediction results. The model used the combination of graph convolution neural network and attention mechanism to extract spatial features, fully considered the spatial relationship between roads, and used temporal convolution network to capture temporal features. After experiments on two real datasets, it is found that it has good performance in the next 15, 30 and 60 minutes, and the results are better than the existing baselines.