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Title	Enhanced Road Information Representation In Graph Recurrent Network For Traffic Speed Prediction
ID_Doc	23668
Authors	Chang L.; Ma C.; Sun K.; Qu Z.; Ren C.
Year	2023
Published	IET Intelligent Transport Systems, 17, 7
DOI	http://dx.doi.org/10.1049/itr2.12334
Abstract	Correctly capturing the spatial-temporal correlation of traffic sequences will benefit to make accurate predictions of the future traffic states. In the paper, the methods of enhancing road spatial and temporal information representation are proposed. Firstly, the parameter matrix of each road is constructed to represent the road-specific traffic patterns for the graph convolution neural network and the recurrent neural network. Then, the node embedding, and matrix factorization are used to reduce the scale of the parameter matrix. Secondly, the node embedding-based Data Adaptive Graph Generation model was introduced to infer the indirect relationship of each node, and the gating mechanism is designed to control the weights of the direct spatial information and the indirect spatial information. Thirdly, to enhance the traffic sequence representation, the time tag and peak tag for the sequences are designed at each sampling moment. At last, the Enhanced Road Information Representation in Graph Recurrent Network (En-GRN) is proposed to predict traffic speed, and the prediction performance is tested on SZ-taxi and Los-loop dataset. The experimental results show that the presented works are effective for improving traffic prediction accuracy. © 2023 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Author Keywords	enhanced road information representation in graph recurrent network (En-GRN); management and control; neural nets; recurrent neural nets; road traffic; smart cities; spatial dependence; temporal dependence; time series; traffic forecasting; traffic modelling

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