Spatial-temporal graph convolution network model with traffic fundamental diagram information informed for network traffic flow prediction

Author:

Liu ZhaoORCID,Ding FanORCID,Dai Yunqi,Li Linchao,Chen Tianyi,Tan Huachun

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference42 articles.

1. A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting;Cai;Transportation Research Part C: Emerging Technologies,2016

2. Freeway performance measurement: Mining loop detector data;Chao;Transportation Research Record,2001

3. A novel reinforced dynamic graph convolutional network model with data imputation for network-wide traffic flow prediction;Chen;Transportation Research Part C: Emerging Technologies,2022

4. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling;Chung,2014

5. A Novel Spatial-Temporal Multi-Scale Alignment Graph Neural Network Security Model for Vehicles Prediction;Diao;IEEE Transactions on Intelligent Transportation Systems,2023

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