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Exploration of the application of Grey-Markov models in the causality analysis of traffic accidents in roundabouts

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  • Peijing Li
  • Jian Li

Abstract

We propose a multivariate Grey-Markov model to quantify traffic accident risk from different causality factors in roundabouts that is uniquely suited for the scarce and stochastic traffic crash data from roundabouts. A data sample of traffic crashes occurring in roundabouts in the U.S. State of Michigan from 2016 to 2021 was collected to investigate the capabilities of this modeling methodology. The multivariate grey model (MGM(1,4)) was constructed using grey relational analysis to determine the best dimensions for model optimization. Then, the Markov chain is introduced to address the unfitness of stochastic, fluctuating data in the MGM(1,4) model. Finally, our proposed hybrid MGM(1,4)-Markov model is compared with other models and validated. This study highlights the superior predictive performance of our MGM(1,4)-Markov model in fore-casting roundabout traffic accidents under data-limited conditions, achieving a 3.02% accuracy rate, in contrast to the traditional GM(1,1) model at 8.30% and the MGM(1,4) model at 4.47%. Moreover, incorporating human, vehicle, and environmental risk factors into a multivariate crash system yields more accurate predictions than merely aggregating crash counts.

Suggested Citation

  • Peijing Li & Jian Li, 2023. "Exploration of the application of Grey-Markov models in the causality analysis of traffic accidents in roundabouts," PLOS ONE, Public Library of Science, vol. 18(9), pages 1-21, September.
    • Handle: RePEc:plo:pone00:0287045
    DOI: 10.1371/journal.pone.0287045
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    References listed on IDEAS

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    1. Yaolong Liu & Xiaoli Huang & Jin Duan & Huaming Zhang, 2017. "The assessment of traffic accident risk based on grey relational analysis and fuzzy comprehensive evaluation method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1409-1422, September.
    2. Tongfei Lao & Xiaoting Chen & Jianian Zhu & Dimitri Volchenkov, 2021. "The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application," Complexity, Hindawi, vol. 2021, pages 1-13, February.
    3. Liu, Yuan & Wang, RuiXue, 2016. "Study on network traffic forecast model of SVR optimized by GAFSA," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 153-159.
    4. Chen, Lin & Lin, Weilong & Li, Junzi & Tian, Binbin & Pan, Haihong, 2016. "Prediction of lithium-ion battery capacity with metabolic grey model," Energy, Elsevier, vol. 106(C), pages 662-672.
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