Short-term traffic prediction is of great importance to the management of traffic congestion, a pervasive and difficult-to-solve problem in many metropolises all over the world. However, existing studies on traffic prediction contain rough traffic information at the carriageway level that ignore the distinction between different turns in one intersection. With the aim of predicting traffic at road intersections from big trace data on a finer scale, this study proposes a novel method, the fine-grained traffic prediction method (FTPG) with a graph attention network (GAT), which predicts traffic information, including traffic flow speeds, traffic states, and average queue lengths, at the turn level. In the FTPG, a method for estimation of the queue starting point is proposed to improve the accuracy of traffic information detection. Furthermore, the topology is constructed under turn-level conditions, and a GAT-based method, the spatio-temporal residual graph attention network (ST-RGAN), is proposed to improve the prediction accuracy. Experiments are performed using taxi GPS trace data collected in the city of Wuhan and show that the proposed FTPG method can make predictions with fine-grained traffic information for road intersections accurately and robustly.
FTPG: A Fine-Grained Traffic Prediction Method With Graph Attention Network Using Big Trace Data
IEEE Transactions on Intelligent Transportation Systems ; 23 , 6 ; 5163-5175
2022-06-01
7159425 byte
Article (Journal)
Electronic Resource
English
Traffic flow prediction based on graph attention network
SPIE | 2024
|Traffic flow prediction method based on graph attention convolution network
European Patent Office | 2020
|Node adaptive learning graph attention neural network traffic flow prediction method
European Patent Office | 2024
|