An Improved Deep Learning Model for Traffic Crash Prediction

Table 3

The functional network between the input variables and the feature representations.

Variable	Traffic feature representation	Geometric feature representation	Pavement feature representation	Environmental feature representation
Traffic factor
The logarithm of AADT per lane	-0.529	-0.007	-0.016	-0.034
Truck traffic percentage	-0.659	-0.002	-0.024	-0.010
Posted speed limits	-0.030	-0.010	0.175	-0.047
Geometric factors
Segment length (miles)	0.007	-0.580	-0.007	-0.003
Degree of horizontal curvature	0.002	-0.918	-0.006	-0.009
Median widths	0.002	0.826	0.068	0.015
Outside shoulder widths	0.005	0.419	0.016	0.064
Number of through lanes
3 lanes	-0.021	-0.891	-0.006	-0.026
2 lanes	-0.015	-0.864	-0.022	-0.060
Lane widths
12 ft	0.004	0.776	0.005	0.001
11 ft	0.009	0.592	0.076	0.004
Number of left-turn lanes
2 left-turn lanes	-0.003	-0.212	-0.019	-0.017
1 left-turn lane	0.002	-0.783	0.028	0.016
Median type
Non-traversable median	0.013	0.247	0.006	0.007
Shoulder type
Pavement	-0.035	-0.144	-0.027	-0.016
Gravel	0.005	-0.743	0.011	0.060
Pavement factor
International roughness index (in./mile)	0.101	0.007	-0.183	-0.001
Rut depth (in.)	-0.004	-0.004	-0.341	-0.038
Environmental factor
Terrain type
Mountainous	-0.014	-0.011	-0.010	-0.954
Land use type
Residential	0.042	0.015	0.011	0.847
Commercial	0.058	0.001	0.006	-0.915
Indicator for lighting
Lighting exists on roadway	0.018	0.008	0.027	0.781