交通事故后的交通运行风险状态等级预测方法

doi:10.13229/j.cnki.jdxbgxb20200731

Abstract

Abstract:

The prediction of traffic operation risk state level after traffic accident is the technical support for real-time risk control and emergency treatment. Based on the accident data， this paper studies the post event risk state， and establishes a traffic operation risk level prediction model based on the improved deep forest algorithm. Firstly， we analyze the importance of accident characteristics， establish the accident feature screening mechanism based on extreme gradient boosting algorithm， introduce Bayes parameter optimization and ten-fold cross validation method to realize the super parameter optimization of deep forest model. Then， we design the cyclic multi granularity scanning method and weighted cascade forest structure， obtain the mapping relationship between the traffic operation risk status level and the accident characteristics， and propose the traffic operation risk level prediction method based on the improved deep forest model. In order to verify the effectiveness of the proposed method， it is compared with SVM， RF and other methods. The experimental results show that the prediction accuracy of the model is 90.80%， and the auc value is 0.99， which show good prediction performance and generalization ability. Compared with the control experiment， the proposed model has obvious advantages. In the actual effect test， it also achieved good prediction effect.

Key words: traffic operation risk level, improved deep forest model, accident feature screening, super parameter optimization

CLC Number:

U491

Zhi-hui LI,Ya-qian SUN,Peng-fei TAO,Hai-tao LI,Xin LIU. Prediction method of traffic operation risk level after traffic accident[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 127-135.

Figures/Tables 14

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Table 1

Confusion matrix of prediction results of each risk level type of references"

实际情况	预测结果
实际情况	正类	负类
正类	$T P$	$F N$
负类	$F P$	$T N$

Table 1

Fig.7

Fig.8

Table 2

Table 3

Characteristics of major accidents"

事故特征		特征变量
交通运行风险状态等级		0、1、2、3
天气状况	温度 $f 2$	-
	风寒 $f 3$	-
	湿度 $f 4$	-
	气压 $f 5$	-
	能见度 $f 6$	-
	风速 $f 8$	-
	降雨量 $f 9$	-
	天气 $f 10$	0、1、…、9
照明情况	日出日落 $f 25$	夜晚0、白天1

Table 3

Table 4

Experimental result"

模型	$p e c$	$r e c$	$F 1$	$a c c$ /%
ad?gcF	0.92	0.91	0.91	90.80
ad?gcF 2	0.89	0.87	0.87	87.28
RF	0.86	0.86	0.86	86.34
SVM	0.81	0.82	0.81	81.55
ad?gcF 1	0.79	0.80	0.79	79.50

Table 4

Fig.9

Fig.10

References 16

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事故特征	阈值	特征个数	准确度/%
风寒f₃	0.1662	1	46.21
温度f₂	0.1420	2	57.88
风速f₈	0.1063	3	67.42
天气f₁₀	0.1022	4	70.91
能见度f₆	0.0912	5	74.70
湿度f₄	0.0859	6	73.03
降雨量f₉	0.0741	7	79.09
气压f₅	0.0666	8	80.39
日出日落f₂₅	0.0576	9	80.81
风向f₇	0.0334	10	79.64
交通信号f₂₃	0.0127	11	79.70
人行道f₁₄	0.0104	12	78.64
交叉口f₂₆	0.0076	13	79.55

Prediction method of traffic operation risk level after traffic accident

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