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Lane-Changing Risk Prediction on Urban Expressways: A Mixed Bayesian Approach for Sustainable Traffic Management

Author

Listed:
  • Quantao Yang

    (Department of Public Security, Shaanxi Police College, Xi’an 710021, China
    These authors contributed equally to this work.)

  • Peikun Li

    (College of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
    These authors contributed equally to this work.)

  • Fei Yang

    (Department of Public Security, Shaanxi Police College, Xi’an 710021, China)

  • Wenbo Lu

    (School of Transportation, Southeast University, Nanjing 214135, China)

Abstract

This study addresses critical safety challenges in sustainable urban mobility by developing a probabilistic framework for lane-change risk prediction on congested expressways. Utilizing unmanned aerial vehicle (UAV)-captured trajectory data from 784 validated lane-change events, we construct a Bayesian network model integrated with an I-CH scoring-enhanced MMHC algorithm. This approach quantifies risk probabilities while accounting for driver decision dynamics and input data uncertainties—key gaps in conventional methods like time-to-collision metrics. Validation via the Asia network paradigm demonstrates 80.5% reliability in forecasting high-risk maneuvers. Crucially, we identify two sustainability-oriented operational thresholds: (1) optimal lane-change success occurs when trailing-vehicle speeds in target lanes are maintained at 1.0–3.0 m/s (following-gap < 4.0 m) or 3.0–6.0 m/s (gap ≥ 4.0 m), and (2) insertion-angle change rates exceeding 3.0°/unit-time significantly elevate transition probability. These evidence-based parameters enable traffic management systems to proactively mitigate collision risks by 13.26% while optimizing flow continuity. By converting behavioral insights into adaptive control strategies, this research advances resilient transportation infrastructure and low-carbon mobility through congestion reduction.

Suggested Citation

  • Quantao Yang & Peikun Li & Fei Yang & Wenbo Lu, 2025. "Lane-Changing Risk Prediction on Urban Expressways: A Mixed Bayesian Approach for Sustainable Traffic Management," Sustainability, MDPI, vol. 17(15), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:15:p:7061-:d:1717183
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    References listed on IDEAS

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    1. Chen, Tianyi & Shi, Xiupeng & Wong, Yiik Diew, 2021. "A lane-changing risk profile analysis method based on time-series clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
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