Inferring Intersection Traffic Patterns With Sparse Video Surveillance Information: An ST-GAN Method

Traffic patterns of urban road intersections are important in traffic monitoring and accident prediction, thus play crucial roles in urban traffic management. Although real-time traffic information is consistently provided by surveillance cameras equipped at road intersections, the sparsity of surveillance distribution poses great challenges in performing a complete real-time traffic pattern analysis. To tackle that, existing works either assume that the traffic patterns are static, or assume a multi-variant distribution model for intersection traffic volumes. The former assumption neglects the temporal features of traffic patterns, and the latter is limited in capturing fine-grained spatiotemporal dependencies. To tackle the problem, the authors propose a novel framework, SpatioTemporal-Generative Adversarial Network (ST-GAN), that exploits deep spatiotemporal features of urban networks and offers accurate traffic pattern inferences with incomplete surveillance information. The ST-GAN framework incorporates a modified GCN network wired with the encoder-decoder mechanism and an LSTM network, which are further boosted by an iterative adversarial training process. Comprehensive experiments on real datasets show that ST-GAN achieves better inference accuracies than state-of-the-art solutions.

• Record URL:
  http://dx.doi.org/10.1109/TVT.2022.3176653
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Wang, Pengkun
  • Zhu, Chaochao
  • Wang, Xu
  • Zhou, Zhengyang
  • Wang, Guang
  • Wang, Yang
• Publication Date: 2022-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 9840-9852
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 71
  • Issue Number: 9
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Intersections; Traffic characteristics; Traffic surveillance; Video
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01861715
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 20 2022 10:23AM