基于有向图卷积神经网络的交通预测与拥堵管控

为解决城市快速路正面临的日益严重的交通拥堵问题,提出了一种针对城市快速路的基于有向图卷积神经网络的交通预测与拥堵管控方法,该方法能够有效利用海量交通数据进行交通预测,实现拥堵的主动管控.首先,基于交通路网的空间有向性和交通流的时空特性,定义了有向的距离影响矩阵、修正欧式距离矩阵和自由流可达矩阵,构建出有向的图卷积算子,...

Traffic Prediction and Congestion Control Based on Directed Graph Convolution Neural Network

In order to solve the increasingly serious traffic congestion problem that urban expressways are facing, a traffic prediction and congestion control method based on a directed graph convolution neural network for urban expressways was proposed. It can effectively use massive traffic data to predict traffic and realize active congestion control. Firstly, based on the spatial directionality of the traffic road network and the spatial-temporal characteristics of traffic flow, a directed distance influence matrix, a modified Euclidean distance matrix and a free flow reachable matrix were defined. A directed graph convolution operator was constructed and applied in the Long Short-term Memory Networks (LSTM). After those settings, the Directed Graph Convolution-LSTM (DGC-LSTM) which was used to predict traffic flow status was established. Next, the congestion bottleneck was identified as the object of congestion control based on the traffic prediction results. Then, the approach of controlling the on-ramp vehicles to enter the mainstream of the expressway was selected as the basic measure, and the specific stepwise congestion management and control strategy for the whole circles by time period was designed according to the temporal and spatial characteristics of the bottleneck. Finally, based on the speed, flow and occupancy recorded by the 2 712 detectors deployed on the Shanghai expressway network at intervals of 5 minutes in 122 working days, a case study was carried out to test the prediction accuracy of the DGC-LSTM model and the effectiveness of the control strategy. Results show that the DGC-LSTM model has higher prediction accuracy and can reduce the Mean Absolute Error (MAE) and error Standard Deviation (SD) of speed prediction by about 38% and 20%, respectively, compared with the traditional Recurrent Neural Network (RNN) and LSTM models; the stepwise congestion management and control strategy can raise the speed at the bottleneck by more than 14 km·h^-1, and shorten the duration of congestion by 40%, which means that it can mitigate congestion spreading on a large scale from the congestion bottleneck, and reduce the congestion degree of the entire road network effectively.