Development of a Safety Prediction Method for Arterial Roads Based on Big-Data Technology and Stacked AutoEncoder-Gated Recurrent Unit

Wei Hao, Donglei Rong, Zhaolei Zhang, Qiyu Wu, Young Ji Byon, Kefu Yi, Jinjun Tang, Nengchao Lyu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Modern complexities associated with an arterial traffic makes existing safety prediction methods insufficient to meet desired standards required by recent developmental needs. This paper proposes an enhanced active safety prediction method based on big-data approach and Stacked AutoEncoder-Gated Recurrent Unit. Firstly, the big-data technology is used to construct a dynamic identification model to recognize real-time operation state and risk state. Secondly, the Stacked AutoEncoder-Gated Recurrent Unit is used to predict a level of safety based on associated recognition results. This paper uses data from working days of Sunset Boulevard, California, from January 1 st, 2020, to February 28 th, 2020. The results of analysis show that the accuracy of the proposed dynamic recognition model reaches 98.92%, which is better than existing models such as random forest, K-nearest neighbor, and naïve Bayes models. In addition, it is found that the Stacked AutoEncoder-Gated Recurrent Unit can achieve a prediction accuracy of 95.157% and has significant advantages in terms of efficiency. The proposed methods will provide feasible solutions for actively monitoring safety levels.

Original languageBritish English
Pages (from-to)20110-20122
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Volume23
Issue number11
DOIs
StatePublished - 1 Nov 2022

Keywords

  • Active safety
  • big-data
  • risk prediction
  • Stacked AutoEncoder-Gated Recurrent Unit
  • state recognition

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