Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models

Table 1

Variables and statistics based on survey data.

Factors	Variables	Values	Percentage (%)
Accident severity	Number of fatalities: Nof	0 : 1	89.59
		≥1 : 2	10.41
	Number of injuries: Noi	0 : 1	9.86
		[1, 3) : 2	85.89
		[3, 11) : 3	4.14
		≥11 : 4	0.11
	Property damage (Yuan): Pd	<1000 : 1	61.18
		[1000, 30000) : 2	37.19
		≥30000 : 3	1.63
Accident characteristics	Time of day: Tod	day [6:00, 18:00) : 1	69.12
		night [18:00, 6:00) : 2	30.88
	Location-Motor vehicle lanes: L-Mvl	Yes: 1	71.68
		No: 2	28.32
	Location-Crosswalk: L-C	Yes: 1	3.42
		No: 2	96.58
	Location-Regular road section: L-Rrs	Yes: 1	60.01
		No: 2	39.99
	Location-Intersection: L-I	Yes: 1	38.90
		No: 2	61.10
Vehicle characteristics	Motorcycle involved: Mi	Yes: 1	16.97
		No: 2	83.03
	Bus or truck involved: Bti	Yes: 1	95.30
		No: 2	4.70
	Vehicle condition: Vc	Good: 1	73.79
		Poor: 2	26.21
Environmental factors	Weather condition: Wc	Sunny: 1	89.48
		Other: 2	10.52
	Visibility distance (meter): Vd	<50 : 1	8.90
		[50, 100) : 2	22.70
		[100, 200) : 3	19.86
		≥200 : 4	48.54
Roadway characteristics	Pavement condition: Pc	Asphalt or cement: 1	99.80
		Other: 2	0.20
	Roadway surface condition: Rsc	Dry: 1	85.16
		Other: 2	14.84
	Road geometrics: Rg	Flat and straight: 1	98.57
		Hill or bend: 2	1.43
	Traffic signal control: Tsc	Yes: 1	17.46
		No: 2	82.54