Toward urban traffic scenarios and more: a spatio-temporal analysis empowered low-rank tensor completion method for data imputation

Abstract

Existing traffic monitoring approaches cannot completely cover all road segments in real-time, leading to massive amounts of missing traffic data, which limits the implementation of intelligent transportation systems. Most existing methods lack deep mining of the unique spatiotemporal characteristics of traffic flows, resulting in difficulty in application to urban traffic with complex topologies and variable states. In this paper, we propose a novel Spatio-Temporal constrained Low-Rank Tensor Completion (ST-LRTC) method, which adopts a manifold embedding approach to depict the local geometric structure of spatiotemporal domains. Specifically, under the low-rank assumption, the method introduces temporal constraints based on the continuity and periodicity of traffic flow and a spatial constraint matrix reflecting the traffic flow transmission mechanism. We embed low-dimensional spatiotemporal constraint matrices into the low-rank tensor completion solving process to fully utilize the global features and local spatiotemporal characteristics of the traffic tensor. Experiments were performed using traffic data from Xi’an, China, and the results indicated that ST-LRTC outperformed state-of-the-art methods under various missing rates and patterns. Thorough experiments have demonstrated that the incorporation of spatiotemporal analysis can enhance the adaptability of the tensor completion model to complex urban scenarios, which guarantees better monitoring, diagnosis, and optimization of urban traffic states.

Keywords:

• Geographical information system for transportation (GIS-T)
• missing data imputation
• low-rank tensor completion
• spatial-temporal analysis
• urban traffic scenarios

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available in ‘figshare.com’ with the identifier https://doi.org/10.6084/m9.figshare.20289078.

Notes

1 https://sts.didiglobal.com

Additional information

Funding

This research was supported by the Fundamental Research Funds for the Central Universities, and the National Natural Science Foundation of China [grant numbers 41971405, 41671442, 42271449, 41901394].

Notes on contributors

Zilong Zhao

Zilong Zhao received his B.E. degree from Wuhan University, Wuhan, China, in 2021, where he is currently pursuing an M.S. degree at the State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing. His research interests include spatiotemporal data mining, tensor imputation, and intelligent transportation systems. Contributions: Methodology, Software, Writing - original draft.

Luliang Tang

Luliang Tang received his Ph.D. degree from Wuhan University, Wuhan, China, in 2007. He is currently a Professor at Wuhan University. His research interests include spatiotemporal GIS, GIS for transportation, and change detection. Contributions: Writing - Review & Editing, Funding acquisition.

Mengyuan Fang

Mengyuan Fang received his M.E. degree from Wuhan University, Wuhan, China, in 2020. His research interests include spatiotemporal data mining and geographic information science. Contributions: Conceptualization, Writing - Review & Editing.

Xue Yang

Xue Yang received her Ph.D. degree from Wuhan University, Wuhan, China, in 2018. She is currently an associate professor at China University of Geosciences, Wuhan. Her research interests include intelligent transportation systems, spatiotemporal data analysis, and information mining. Contributions: Conceptualization, Funding acquisition.

Chaokui Li

Chaokui Li received his Ph.D. degree from Central South University, Changsha, China, in 2001. He is currently a Professor at Hunan University of Science and Technology. His research interests include 3D Geographic Modeling and Geographic Information Systems. Contributions: Formal analysis, Resources.

Qingquan Li

Qingquan Li received his Ph.D. degree in geographic information system (GIS) and photogrammetry from Wuhan Technical University of Surveying and Mapping, Wuhan, China, in 1998. He is currently a Professor at Shenzhen University, Guangdong, China. His research interests include dynamic data modeling in GIS, surveying engineering, and intelligent transportation system. Contributions: Resources, Supervision.