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Deep spatio-temporal residual neural networks for road-network-based data modeling

Ren, Y; Cheng, T; Zhang, Y; (2019) Deep spatio-temporal residual neural networks for road-network-based data modeling. International Journal of Geographical Information Science , 33 (9) pp. 1894-1912. 10.1080/13658816.2019.1599895. Green open access

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Abstract

Recently, researchers have introduced deep learning methods such as convolutional neural networks (CNN) to model spatio-temporal data and achieved better results than those with conventional methods. However, these CNN-based models employ a grid map to represent spatial data, which is unsuitable for road-network-based data. To address this problem, we propose a deep spatio-temporal residual neural network for road-network-based data modeling (DSTR-RNet). The proposed model constructs locally-connected neural network layers (LCNR) to model road network topology and integrates residual learning to model the spatio-temporal dependency. We test the DSTR-RNet by predicting the traffic flow of Didi cab service, in an 8-km2 region with 2,616 road segments in Chengdu, China. The results demonstrate that the DSTR-RNet maintains the spatial precision and topology of the road network as well as improves the prediction accuracy. We discuss the prediction errors and compare the prediction results to those of grid-based CNN models. We also explore the sensitivity of the model to its parameters; this will aid the application of this model to network-based data modeling.

Type: Article
Title: Deep spatio-temporal residual neural networks for road-network-based data modeling
Open access status: An open access version is available from UCL Discovery
DOI: 10.1080/13658816.2019.1599895
Publisher version: https://doi.org/10.1080/13658816.2019.1599895
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: Spatio-temporal modeling, road network, deep learning, residual neural network
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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/10092053
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