Model on discriminating risk causes and consequence severity of urban traffic emergencies

doi:10.16265/j.cnki.issn1003-3033.2025.02.0676

Abstract

Abstract:

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.

Key words: urban transit, emergency, risk causes, consequence severity, discrimination model, improved feature selection algorithm (IFSA)

CLC Number:

X928.03事故预防与预测

FAN Bosong, SHAO Chunfu, WANG Jingsheng, LIU Dong. Model on discriminating risk causes and consequence severity of urban traffic emergencies[J]. China Safety Science Journal, 2025, 35(2): 220-226.

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后果严重程度	取值
特别严重(Extremely Serious,ES)	10
严重(Serious,S)	5
较严重(Relatively Serious,RS)	2
不严重(Not Serious,NS)	1

真实分类	判别结果
真实分类	NS	RS	S	ES
NS	a₁₁	a₁₂	a₁₃	a₁₄
RS	a₂₁	a₂₂	a₂₃	a₂₄
S	a₃₁	a₃₂	a₃₃	a₃₄
ES	a₄₁	a₄₂	a₄₃	a₄₄

模型	不严重	较严重	严重	特别严重	均值
DRBM	87.26	82.87	89.00	75.00	83.53
RF	85.02	82.32	87.12	62.50	79.24
DF	90.22	86.70	89.29	87.50	88.43
LightGBM	90.32	89.15	93.05	87.50	90.00
IDM-ECS	91.63	88.70	94.36	87.50	90.55

模型	不严重	较严重	严重	特别严重	均值
DRBM	93.31	73.82	83.33	75.00	81.36
RF	92.85	70.43	83.15	55.56	75.50
DF	95.13	78.70	86.29	70.00	82.53
LightGBM	95.85	79.94	89.80	87.50	88.27
IDM-ECS	95.73	82.16	89.88	100.00	91.89

模型	不严重	较严重	严重	特别严重	均值
DRBM	90.18	78.08	86.07	75.00	82.34
RF	88.76	75.91	85.09	58.82	77.15
DF	92.61	82.51	87.77	77.78	85.16
LightGBM	93.00	84.29	91.40	87.50	89.05
IDM-ECS	93.64	85.30	91.96	93.33	91.06