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

Table 2

Parameter learning results of the fatality forecasting model.

No.	Variables			Estimation results								𝑁
	L-Rrs	Vc	Noi	Nof ≥ 1				Nof = 0
				Bayesian	Test	Absolute error	Relative error	Bayesian	Test	Absolute error	Relative error
1	1	1	1	0.0052	0.0000	0.0052	1.0000	0.9948	1.0000	0.0052	0.0052	1123
2	1	1	2	0.9995	1.0000	0.0005	0.0005	0.0005	0.0000	0.0005	1.0000
3	1	1	3	0.6622	0.6667	0.0045	0.0068	0.3378	0.3333	0.0045	0.0133
4	1	2	1	0.2628	0.2626	0.0002	0.0008	0.7372	0.7374	0.0002	0.0003
5	1	2	2	0.9855	0.9855	0.0000	0.0000	0.0145	0.0145	0.0000	0.0000
6	1	2	3	0.9994	1.0000	0.0006	0.0006	0.0006	0.0000	0.0006	1.0000
7	2	1	1	0.2012	0.2000	0.0012	0.0060	0.7988	0.8000	0.0012	0.0015
8	2	1	2	0.9369	0.9375	0.0006	0.0006	0.0631	0.0625	0.0006	0.0095
9	2	1	3	0.5000	0.0000	0.5000	1.0000	0.5000	0.0000	0.5000	1.0000
10	2	2	1	0.2601	0.2601	0.0000	0.0000	0.7399	0.7399	0.0000	0.0000
11	2	2	2	0.9575	0.9575	0.0000	0.0000	0.0425	0.0425	0.0000	0.0000
12	2	2	3	0.9087	0.9091	0.0004	0.0004	0.0913	0.0909	0.0004	0.0044