Exploring Risk Factors with Crash Severity on China Two-Lane Rural Roads Using a Random-Parameter Ordered Probit Model

Table 2

Descriptive statistics of explanatory variables for analysing two-lane rural road crash severity.

Variables	Description	Frequency/average	Percentage/SD
Accident attributes
Form of collision (CR_FORM)	1 if the collision belongs to single-vehicle accident (CR_FORM1)	142	9.53%
	2 if the collision belongs to vehicle-vehicle crash vehicle (CR_FORM2)	1152	77.32%
	3 if the collision belongs to vehicle-fixed object crashes (CR_FORM3)	99	6.64%
	4 if the collision belongs to vehicle–pedestrian crashes (CR_FORM4)	97	6.51%
Driver attributes
Driver gender (DR_GE)	0 if male	1294	86.85%
	1 if female	195	13.09%
Driver age (DR_AG)	—	16∼65	9.5
Local driver (DR_LO)	0 if true	1056	70.87%
	1 otherwise	434	29.13%
Behaviour of driver (DR_BE)	0 if exist illegal driving behaviours	835	56.04%
	1 otherwise	655	43.96%
Vehicle attributes
Type of vehicle (VEH_TYPE)	1 if the collision only involved passenger cars (VEH_TYPE1)	1054	70.74%
	2 if the collision related to motorcycle (VEH_TYPE2)	315	21.14%
	3 if the collision regard with motorcycles (VEH_TYPE3)	121	8.12%
Road attributes
Road horizontal alignment (RD_HA)	0 otherwise	641	43.02%
	1 if curve	849	56.98%
Road vertical alignment (RD_VA)	0 otherwise	926	62.15%
	1 if downhill or uphill	564	37.85%
Access segment (RD_AS)	0 if access segment	1100	73.83%
	1 otherwise	390	26.17%
Village segment (RD_VS)	0 if village segment	915	61.41%
	1 otherwise	575	38.59%
The surface of road (RD_SU)	0 otherwise	1420	95.30%
	1 if wet, icy, and snowy	70	4.70%
Environment attributes
The light of crash (EN_LA)	0 otherwise	967	64.90%
	1 if light poor	513	34.43%
The weather of crash (EN_WA)	0 otherwise	1391	93.36%
	1 if rain, snow and fog	99	6.64%
The day of weekday (EN_TA)	0 otherwise	1040	69.80%
	1 if weekend or holiday	450	30.20%