Kim, Y., Wang, P. and Mihaylova, L. orcid.org/0000-0001-5856-2223 (2019) Structural recurrent neural network for traffic speed prediction. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2019). IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 12-17 May 2019, Brighton, UK. IEEE ISBN 9781479981311
Abstract
Deep neural networks have recently demonstrated the traffic prediction capability with the time series data obtained by sensors mounted on road segments. However, capturing spatio-temporal features of the traffic data often requires a significant number of parameters to train, increasing compu- tational burden. In this work we demonstrate that embedding topological information of the road network improves the process of learning traffic features. We use a graph of a ve- hicular road network with recurrent neural networks (RNNs) to infer the interaction between adjacent road segments as well as the temporal dynamics. The topology of the road network is converted into a spatio-temporal graph to form a structural RNN (SRNN). The proposed approach is validated over traffic speed data from the road network of the city of Santander in Spain. The experiment shows that the graph- based method outperforms the state-of-the-art methods based on spatio-temporal images, requiring much fewer parameters to train
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Traffic prediction; recurrent neural network; structural recurrent neural network; spatio-temporal graph |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number European Commission - Horizon 2020 688082 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Feb 2019 14:52 |
Last Modified: | 17 Apr 2020 00:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICASSP.2019.8683670 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:142718 |