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

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.

KEYWORDS:

• Spatio-temporal modeling
• road network
• deep learning
• residual neural network

Acknowledgments

The authors thank Didi Chuxing for providing the experiment data source. The authors thank Dr. May Yuan, Dr. Huanfa Chen, and the anonymous reviewers for their insightful comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is part of the Consumer Data Research Centre (CDRC) project supported by Economic and Social Research Council (ES/L011840/1). It is supported by the Science and Technology Project of Qingdao under Grant number 16-6-2-61-NSH; The first author’s joint Ph.D. research and the third author’s Ph.D research are funded by the China Scholarship Council (CSC). The CSC is a non-profit institution with legal person status affiliated with the Ministry of Education in China

Notes on contributors

Yibin Ren

Yibin Ren is a Ph.D. student of Ocean University of China, majoring in GIS. His research interests include spatio-temporal data mining and high-performance geo-computing. He visited the SpaceTimeLab for Big Data Analytics, University College London during Oct. 2017-Oct. 2018 as a jointly PhD student. Currently he works on integrating deep learning algorithms to model spatio-temporal series data such as crowd flow and traffic flow.

Tao Cheng

Tao Cheng is a professor in Geoinformatics at Department of Civil, Environmental and Geomatic Engineering, University College London. She is the Founder and Director of SpaceTimeLab for Big Data Analytics. Her research interests include network complexity, Geocomputation, space-time analytics and Big data mining (modelling, prediction, clustering, visualisation and simulation) with applications in transport, crime, health, social media, and natural hazards.

Yang Zhang

Yang Zhang is a Ph.D. student with SpaceTimeLab for Big Data Analytics, University College London. Her research interests include spatio-temporal data mining, deep learning, urban computing and geo-computation with applications in transport, crime and social media.