Traffic Accident Data Generation Based on Improved Generative Adversarial Networks

For urban traffic, traffic accidents are the most direct and serious risk to people’s lives, and rapid recognition and warning of traffic accidents is an important remedy to reduce their harmful effects. However, research scholars are often confronted with the problem of scarce and difficult-to-collect accident data resources for traffic accident scenarios. Therefore, in this paper, a traffic data generation model based on Generative Adversarial Networks (GAN) is developed. To make GAN applicable to non-graphical data, the authors improve the generator network structure of the model and used the generated model to resample the original data to obtain new traffic accident data. By constructing an adversarial neural network model, the authors generate a large number of data samples that are similar to the original traffic accident data. Results of the statistical test indicate that the generated samples are not significantly different from the original data. Furthermore, the experiments of traffic accident recognition with several representative classifiers demonstrate that the augmented data can effectively enhance the performance of accident recognition, with a maximum increase in accuracy of 3.05% and a maximum decrease in the false positive rate of 2.95%. Experimental results verify that the proposed method can provide reliable mass data support for the recognition of traffic accidents and road traffic safety.

• Record URL:
  http://dx.doi.org/10.3390/s21175767
• Record URL:
  https://www.mdpi.com/1424-8220/21/17/5767
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
• Supplemental Notes:
  • 2021© Zhijun Chen et al.
• Authors:
  • Chen, Zhijun
  • Zhang, Jingming
  • Zhang, Yishi
  • Huang, Zihao
• Publication Date: 2021

Language

• English

Media Info

• Media Type: Digital/other
• Pagination: 5767
• Serial:
  • Sensors
  • Volume: 21
  • Issue Number: 17
  • Publisher: MDPI AG
  • ISSN: 1424-8220
  • Serial URL: http://www.mdpi.com/journal/sensors
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Classification; Crash data; Data analysis; Pattern recognition systems; Traffic safety; Trip generation
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01783272
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 27 2021 9:59AM