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Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction.pdf (1.61 MB)

Spatial-temporal feature extraction and evaluation network for citywide traffic condition prediction

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journal contribution
posted on 2023-10-17, 10:35 authored by Liang Chu, Zhuoran Hou, Jingjing JiangJingjing Jiang, Jun YangJun Yang, Yuanjian ZhangYuanjian Zhang

Traffic condition prediction is crucial for executing traffic control and scheduling tasks within intelligent transportation systems. With diversified data sources, effectively modeling the complex spatial-temporal dependencies in the whole traffic network and predicting nonlinear general traffic condition changes become primary challenges for intelligent transportation systems. In this paper, a double-layer spatial-temporal feature extraction and evaluation (DL-STFEE) model is proposed, aimed at accurately predicting the traffic condition transferring of the whole traffic network. Firstly, a public traffic dataset is processed to extract the spatial and temporal features of vehicles, as well as the clusters of traffic conditions of the entire traffic network. Secondly, a double-layer deep learning traffic condition predictor is proposed. The model incorporates a spatial-temporal feature extraction layer, which leverages the graph convolution network (GCN) and attention mechanism to obtain spatiotemporal features across the whole traffic network. Additionally, a spatial-temporal combination layer employs a high-dimensional self-attention mechanism to integrate features across spatial-temporal combinations, bolstering prediction accuracy. Finally, through rigorous experiments, the contributions of neural network structures on the spatial-temporal feature extraction are comprehensively analyzed and the experiments also validate the effectiveness of traffic condition prediction by the proposed DL-STFEE model.


State Scholarship Funding of CSC under Grant 202206170067

Changsha Automotive Innovation Research Institute Innovation Project-Research on Intelligent Trip Planning System of Pure Electric Vehicles Based on Big Data under Grant CAIRIZT20220105

Science and technology planning project in Yibin city under Grant 2020GY001

Science and technology planning project in Tianjin city under Grant 20YFZCGX00770



  • Aeronautical, Automotive, Chemical and Materials Engineering


  • Aeronautical and Automotive Engineering

Published in

IEEE Transactions on Intelligent Vehicles


1 - 16


Institute of Electrical and Electronics Engineers (IEEE)


  • AM (Accepted Manuscript)

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Dr Jingjing Jiang. Deposit date: 16 October 2023