基于MIMIC与机器学习的出租车驾驶员交通事故诱因分析

doi:10.13229/j.cnki.jdxbgxb20210614

Abstract

Abstract:

Through the questionnaire， 2391 taxi drivers' personal attributes， physical fatigue perception， work load， risk driving behaviors and road traffic accident experience were collected. Multiple indicators and multiple causes（MIMIC） model is used to explore the induced effects of physical fatigue perception， risky driving behavior on traffic accidents， and to verify the effects of causal variable for gender， age and work pressure on physical fatigue perception and distracted driving behavior. Four machine learning algorithms， including logistic regression， naive Bayes， support vector machine and random forest， are selected to predict the taxi accident. The results show that： the increase of physical fatigue perception， errors driving behavior， radical driving behavior and distracted driving behavior can lead to the growth of accident rate， and gender, age and work load can affect the physical fatigue perception and frequency of distracted driving behavior. The accident prediction model based on machine learning is very effective， and the prediction effect of random forest is the highest， and the prediction accuracy of single characteristic variables 'risky driving behavior', 'distracted driving behavior' and 'physical fatigue perception' is acceptable. When the personal attribute indicators of 'working load'， 'age'， and 'gender' are introduced， the prediction accuracy can be further improved.

Key words: engineering of communications and transportation safety, professional taxi driver, road traffic accidents, MIMIC model, path analysis, moderation effect, machine learning

CLC Number:

U491

Heng-yan PAN,Wen-hui ZHANG,Ting-ting LIANG,Zhi-peng PENG,Wei GAO,Yong-gang WANG. Inducement analysis of taxi drivers' traffic accidents based on MIMIC and machine learning[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 457-467.

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构面	问题	0	1	2	3	4	5	6
PF （身体疲劳感知）	SLEE	33.08	25.93	18.53	15.27	6.11	1.09	0
PF （身体疲劳感知）	FATI	16.22	17.94	22.04	23.13	12.13	8.41	0.13
RDBs （风险驾驶行为）	BELT	31.2	20.83	21.33	16.48	7.53	2.63	0
	REDL	33.84	28.02	17.23	10.92	7.4	2.59	0
	ARRG	23.38	24.89	23.8	17.98	7.28	1	0
	OVTA	35.3	23.67	21.54	13.63	5.31	0.54	0
	HONK	49.1	26.56	15.81	6.27	2.26	0	0
	LAMP	43.37	27.44	17.98	8.53	2.68	0	0
	PARK	31.87	24.17	24.17	12.25	6.15	1.38	0
	MERG	44.75	23.76	16.98	9.91	3.68	0.92	0
DDs （分心驾驶行为）	HAND	32.2	23.3	21.62	13.93	7.24	1.71	0
DDs （分心驾驶行为）	PHONE	64.12	20.91	11.38	2.72	0.88	0	0

构面	Cronbach's α	PF	RTCS	RDBS	DDs	WL	题项
PF	0.536	1	-	-	-	-	SLEE、FATI
RTCS	0.361	0.710^**	1	-	-	-	PDO、PI
RDBS	0.914	0.763^**	0.861^**	1	-	-	LAMP、HONK、REDL、MERG、 OVTA、BELT、PARK、AGGR
DDs	0.730	0.641^**	0.779^**	0.807^**	1	-	HAND、PHONE
WL	0.475	0.273^**	0.069^**	0.327^**	0.247^**	1	HOUD、ODUT、DCOM、MFEE

类别	路径	参数估计	Bias-corrected percentile method （-95%）			Percentile method （95%）
类别	路径	参数估计	Lower	Upper	p	Lower	Upper	p
中介效果	PF-Radical-RTCs	0.135	0.109	0.165	0.001	0.108	0.164	0.001
	PF-Error-RTCs	0.112	0.108	0.116	0.001	0.109	0.116	0.001
	PF-DD-RTCs	0.321	0.284	0.363	0.001	0.284	0.363	0.001
	DD-Radical-RTCs	0.176	0.142	0.216	0.001	0.141	0.214	0.001
	DD-Error-RTCs	0.112	0.108	0.116	0.001	0.109	0.116	0.001
远程中介效果	PF-DD-Radical-RTCS	0.099	0.079	0.123	0.001	0.078	0.122	0.001
远程中介效果	PF-DD-Error-RTCS	0.063	0.059	0.067	0.001	0.059	0.067	0.001
总效果	PF-RTCs	1.171	1.118	1.230	0.001	1.118	1.228	0.001
总效果	DD-RTCs	0.860	0.776	0.953	0.001	0.775	0.953	0.001
效果差异	Radical-RTCs to Error-RTCs	0.107	0.052	0.166	0.001	0.051	0.165	0.001
	PF-RTCs to DD-RTCs	0.310	0.126	0.570	0.001	0.202	0.414	0.001
	Gender-PF to Gender-DD	0.343	0.126	0.57	0.002	0.126	0.569	0.002
	WL-PF to WL-DD	0.174	0.111	0.232	0.001	0.114	0.235	0.001
	Age-PF to Age-DD	0.296	0.229	0.359	0.001	0.229	0.359	0.001

分类器	特征变量	AUC值	标准误差	渐近概率	95% LCL	95% UCL	分类器	特征变量	AUC值	标准误差	渐近概率	95% LCL	95% UCL
LR	S1	0.886	0.007	4.51×10^-226	0.873	0.899	NB	S1	0.886	0.007	5.43×10^-227	0.874	0.899
	S2	0.877	0.007	1.12×10^-216	0.864	0.891		S2	0.878	0.007	8.53×10^-217	0.864	0.891
	S3	0.959	0.003	0	0.953	0.966		S3	0.954	0.004	0	0.946	0.961
	S1~S3	0.964	0.003	0	0.958	0.970		S1~S3	0.953	0.004	0	0.945	0.961
	S1~S4	0.972	0.003	0	0.966	0.977		S1~S4	0.954	0.004	0	0.946	0.962
	均值	0.932						均值	0.925
SVM	S1	0.851	0.008	2.71×10^-187	0.835	0.866	RF	S1	0.894	0.006	1.15×10^-235	0.882	0.906
	S2	0.836	0.009	5.42×10^-172	0.819	0.853		S2	0.882	0.007	7.59×10^-222	0.869	0.895
	S3	0.946	0.005	0	0.937	0.955		S3	0.994	0.001	0	0.992	0.995
	S1~S3	0.959	0.004	0	0.951	0.967		S1~S3	0.996	0.001	0	0.995	0.997
	S1~S4	0.977	0.002	0	0.972	0.982		S1~S4	0.997	0.001	0	0.996	0.998
	均值	0.914						均值	0.952

Inducement analysis of taxi drivers' traffic accidents based on MIMIC and machine learning

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