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Related Experiment Video

Updated: Jul 1, 2025

Driving Under the Influence: How Music Listening Affects Driving Behaviors
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High-risk event prone driver identification considering driving behavior temporal covariate shift.

Ruici Zhang1, Xiang Wen2, Huanqiang Cao2

  • 1College of Transportation Engineering, Tongji University, Shanghai, 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, 4800 Cao'an Road, 201804, Shanghai, China.

Accident; Analysis and Prevention
|March 3, 2024
PubMed
Summary

This study introduces a new model to predict risky driving behavior by identifying time-invariant features, improving driver safety. The approach enhances identification of high-risk drivers, boosting traffic safety management.

Keywords:
Domain GeneralizationDriving Behavior Temporal Covariate ShiftHigh-risk Event Occurrence Probability EstimationHigh-risk Event Prone Driver IdentificationImbalanced Data Learning

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Area of Science:

  • Traffic Safety
  • Machine Learning
  • Data Science

Background:

  • Frequent high-risk driving events (e.g., hard braking) endanger traffic safety.
  • Existing models often assume data independence, ignoring temporal shifts in driving behavior.

Purpose of the Study:

  • To develop a method for identifying time-invariant driving behavior features.
  • To establish relationships between these features and the probability of high-risk events.
  • To improve the accuracy of predicting and managing risky driving.

Main Methods:

  • Proposed a generalized modeling framework with Distribution Characterization (DC) and Distribution Matching (DM) modules.
  • Utilized Gated Recurrent Unit (GRU) for time-invariant feature mining.
  • Introduced modified loss functions to handle imbalanced data from rare high-risk events.

Main Results:

  • The proposed framework achieved 7.2% higher average precision than traditional methods.
  • Modified loss functions further improved performance by 3.8%.
  • Demonstrated a 33.34% enhancement in identifying drivers prone to high-risk events.

Conclusions:

  • The developed framework effectively addresses temporal covariate shift in driving behavior data.
  • The approach significantly improves the precision of identifying high-risk drivers.
  • Offers practical benefits for enhancing driver management programs and overall traffic safety.