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

Table 4

Parameter learning results of the property damage forecasting model.

	No.		1	2	3	4
Variables	L-Rrs		1	1	2	2
	Vc		1	2	1	2
Estimation results	Pd < 1000	Bayesian	0.7905	0.6802	0.4402	0.4641
		Test	0.7917	0.6803	0.4405	0.4641
		Absolute error	0.0012	0.0001	0.0003	0.0000
		Relative error	0.0015	0.0001	0.0006	0.0000
	1000 ≤ Pd < 30000	Bayesian	0.1983	0.3072	0.5470	0.5093
		Test	0.1979	0.3072	0.5476	0.5093
		Absolute error	0.0004	0.0000	0.0006	0.0000
		Relative error	0.0019	0.0000	0.0011	0.0000
	Pd ≥ 30000	Bayesian	0.0113	0.0126	0.0129	0.0266
		Test	0.0104	0.0125	0.0119	0.0266
		Absolute error	0.0009	0.0001	0.0010	0.0000
		Relative error	0.0782	0.0048	0.0772	0.0002
𝑁			1123