Spatial-Temporal Attention Graph Convolution Network on Edge Cloud for Traffic Flow Prediction

Accurate short-term traffic flow prediction plays an important role in providing road condition information in the immediate future. With the information, intelligent vehicles can plan and adjust the route to prevent congestion. As a result, many models for short-term traffic flow forecasting have been proposed to date. However, most of them focus on the prediction of the entire traffic network, which could lead to several problems: (1) the entire traffic network could have a large scale and a complex structure, for which the model training is likely to be time-consuming as well as inefficient; (2) processing a large amount of training data on the central cloud could cause much calculation pressure on the server and increase the risk of privacy leakage. In this paper, we propose a Spatial-Temporal Attention Graph Convolution Network on Edge Cloud model (STAGCN-EC). We first divide the entire traffic network into several parts to reduce its scale and complexity. Then, we allocate each part of the network to a certain Roadside Unit (RSU) for training, thus there is no need to process all data on the central server. Besides, we utilize spatial-temporal attention and features extracting module that fits the low computational power devices like RSUs, to capture spatial-temporal dependence and predict traffic flow. At last, we use two highway datasets from District 7 and District 4 in California to validate our model. Through the experiments, we find out that our model performs well both in predicted precision and efficiency compared with the five baseline methods.