Critical Factors Analysis of Severe Traffic Accidents Based on Bayesian Network in China

Table 9

Risk degree raking result in multivehicle traffic accidents.

Factor variables	Severity analysis result of the number of deaths			Severity analysis result of the number of injuries
	Expectation	Ranking vector	Ranking	Expectation	Ranking vector	Ranking
Overload	(19.920, 34.111)	0.1037	2	(7.620, 20.265)	0.0960	10
Mislane use	(19.873, 32.713)	0.1008	8	(8.904, 20.529)	0.0995	7
Commuter bus involved	(19.985, 34.202)	0.1040	1	(8.390, 19.925)	0.0969	9
Heavy truck involved	(19.793, 33.930)	0.1030	6	(9.087, 20.744)	0.1005	6
Poor braking	(19.864, 34.029)	0.1034	3	(9.178, 20.850)	0.1009	5
Straight alignment	(17.387, 28.545)	0.0858	10	(9.317, 21.014)	0.1017	2
Nonphysical separation	(19.842, 34.008)	0.1033	4	(9.251, 20.937)	0.1013	4
Night	(19.833, 33.994)	0.1033	5	(9.450, 21.171)	0.1023	1
Weekend	(18.563, 29.329)	0.0901	9	(9.292, 20.986)	0.1015	3
Poor weather	(19.727, 33.834)	0.1027	7	(8.853, 20.649)	0.0993	8