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Model on discriminating risk causes and consequence severity of urban traffic emergencies
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Bosong FAN1, Chunfu SHAO2, Jingsheng WANG1, Dong LIU1
China Safety Science Journal | 2025, 35(2) : 220 - 226
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China Safety Science Journal | 2025, 35(2): 220-226
Public safety
Model on discriminating risk causes and consequence severity of urban traffic emergencies
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Bosong FAN1, Chunfu SHAO2, Jingsheng WANG1, Dong LIU1
Affiliations
  • 1 School of Traffic Management,People's Public Security University of China,Beijing 100038,China
  • 2 School of Transportation Engineering,Xinjiang University,Urumqi Xinjiang 830046,China
Published: 2025-02-28 doi: 10.16265/j.cnki.issn1003-3033.2025.02.0676
Outline
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In order to improve the accuracy of emergency consequence severity assessment,clarify the correlation between the risk causes and consequence severity in urban traffic emergencies,the improved discrimination model of emergency consequence severity (IDM-ECS) was constructed and experimentally verified. First,based on the IFSA,the risk causes of emergencies were screened to obtain the important risk causes such as train fulfillment rate,punctuality rate,and daily network passenger volume and so on. Secondly,the improved hybrid restricted Boltzmann machine(HRBM) model was used to calculate the relationship between different risk causes and the consequence severity,and the discriminative relationship between risk causes and the consequence severity was obtained by comparing the probability values. Finally,the dataset of rail transit emergencies was used as an experimental sample for validation. The performance was compared with four models,including Generating Restricted Boltzmann Machines (GRBM),Random Forest (RF),Deep Forest (DF),and Light Gradient Boosting Machine (LightGBM),in terms of recall,precision,and F1 value. The results show that train fulfillment rate,punctuality rate,daily network passenger volume,line 5 section full load rate,line 10 section full load rate,signal failure,and vehicle failure are the seven optimal risk causes. The IDM-ECS model has an average recall of 90.55%,precision of 91.89%,and F1 value of 91.06%,all of which are better than those of the comparison models.

urban transit  /  emergency  /  risk causes  /  consequence severity  /  discrimination model  /  improved feature selection algorithm (IFSA)
Bosong FAN, Chunfu SHAO, Jingsheng WANG, Dong LIU. Model on discriminating risk causes and consequence severity of urban traffic emergencies[J]. China Safety Science Journal, 2025 , 35 (2) : 220 -226 . DOI: 10.16265/j.cnki.issn1003-3033.2025.02.0676
Year 2025 volume 35 Issue 2
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.02.0676
  • Receive Date:2024-09-11
  • Online Date:2025-07-05
  • Published:2025-02-28
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  • Received:2024-09-11
  • Revised:2024-11-14
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Affiliations
    1 School of Traffic Management,People's Public Security University of China,Beijing 100038,China
    2 School of Transportation Engineering,Xinjiang University,Urumqi Xinjiang 830046,China
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小菇属 Mycena 11 5.26
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红菇属 Russula 17 8.13
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