Accident Diagnosis and Evaluation System in Parking Lots Using Multisource Data Based on Bayesian Networks

Table 3

The MI and SI in the parking lot Bayesian network.

Parent node	Child node	MI	SI
VEHUSE	VEHTYPE	1.0000	0.9994
VEHMOVE	FRST_EVNT_CAUS_CD	0.8530	0.3181
MAXSPEEDLMT	POINTIMP	0.8294	0.1569
POINTIMP	FRST_EVNT_CAUS_CD	0.8154	0.3435
LGHTCOND	PROPERTY	0.7990	0.6197
TIME	VEHMOVE	0.7968	0.3869
MAXSPEEDLMT	VEHMOVE	0.7451	0.3444
VEHTYPE	POINTIMP	0.6828	0.1716
RTESGNCD	LANDUSE	0.6223	0.3601
VEHMOVE	PROPERTY	0.6221	0.5098
DAYOWEEK	TOT_OF_PERS_NUM	0.5828	0.5573
VEHSPEED	VEHDMTYP	0.5383	0.4229
TIME	LGHTCOND	0.5146	0.4436
LANDUSE	TIME	0.5115	0.3717
DAYOWEEK	TIME	0.4956	0.5597
TOT_OF_PERS_NUM	VEHDMTYP	0.4699	0.4542
VEHDMTYP	POINTIMP	0.4004	0.1716
TIME	VEHSPEED	0.2842	0.494
URBSIZE	RTESGNCD	0.2420	0.113
TIME	VEHUSE	0.1728	0.3661
WEATCOND	LGHTCOND	0.0790	0.3329
RTESGNCD	VEHUSE	0.0001	0.3267
URBSIZE	LANDUSE	0.0001	0.2752