Zhang, Y;
Cheng, T;
Ren, Y;
Xie, K;
(2020)
A novel residual graph convolution deep learning model for short-term network-based traffic forecasting.
International Journal of Geographical Information Science
, 34
(5)
pp. 969-995.
10.1080/13658816.2019.1697879.
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Abstract
Short-term traffic forecasting on large street networks is significant in transportation and urban management, such as real-time route guidance and congestion alleviation. Nevertheless, it is very challenging to obtain high prediction accuracy with reasonable computational cost due to the complex spatial dependency on the traffic network and the time-varying traffic patterns. To address these issues, this paper develops a residual graph convolution long short-term memory (RGC-LSTM) model for spatial-temporal data forecasting considering the network topology. This model integrates a new graph convolution operator for spatial modelling on networks and a residual LSTM structure for temporal modelling considering multiple periodicities. The proposed model has few parameters, low computational complexity, and a fast convergence rate. The framework is evaluated on both the 10-min traffic speed data from Shanghai, China and the 5-min Caltrans Performance Measurement System (PeMS) traffic flow data. Experiments show the advantages of the proposed approach over various state-of-the-art baselines, as well as consistent performance across different datasets.
| Type: | Article |
|---|---|
| Title: | A novel residual graph convolution deep learning model for short-term network-based traffic forecasting |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/13658816.2019.1697879 |
| Publisher version: | https://doi.org/10.1080/13658816.2019.1697879 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Short-term traffic forecasting, spatial-temporal dependency, network topology, graph convolution, residual long short-term memory |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10087270 |
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