High-risk event prone driver identification considering driving behavior temporal covariate shift

Drivers who perform frequent high-risk events (e.g., hard braking maneuvers) pose a significant threat to traffic safety. Existing studies commonly estimated high-risk event occurrence probabilities based upon the assumption that data collected from different time periods are independent and identically distributed (referred to as i.i.d. assumption). Such approach ignored the issue of driving behavior temporal covariate shift, where the distributions of driving behavior factors vary over time. To fill the gap, this study targets at obtaining time-invariant driving behavior features and establishing their relationships with high-risk event occurrence probability. Specifically, a generalized modeling framework consisting of distribution characterization (DC) and distribution matching (DM) modules was proposed. The DC module split the whole dataset into several segments with the largest distribution gaps, while the DM module identified time-invariant driving behavior features through learning common knowledge among different segments. Then, gated recurrent unit (GRU) was employed to conduct time-invariant driving behavior feature mining for high-risk event occurrence probability estimation. Moreover, modified loss functions were introduced for imbalanced data learning caused by the rarity of high-risk events. The empirical analyses were conducted utilizing online ride-hailing services data. Experiment results showed that the proposed generalized modeling framework provided a 7.2% higher average precision compared to the traditional i.i.d. assumption-based approach. The modified loss functions further improved the model performance by 3.8%. Finally, benefits for the driver management program improvement have been explored by a case study, demonstrating a 33.34% enhancement in the identification precision of high-risk event prone drivers.

• Record URL:
  • https://doi.org/10.1016/j.aap.2024.107526
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S000145752400071X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhang, Ruici
  • Wen, Xiang
  • Cao, Huanqiang
  • Cui, Pengfei
  • Chai, Hua
  • Hu, Runbo
  • Yu, Rongjie
• Publication Date: 2024-5-1

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 107526
• Serial:
  • Accident Analysis & Prevention
  • Volume: 199
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Behavior; Crash risk forecasting; Data quality; Driver performance; High risk drivers; Time
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01913863
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 4 2024 4:58PM