Simultaneous Generation of Optimum Pavement Clusters and Associated Performance Models

Table 4

Estimated model parameters using the proposed CLR approach.

Parameters	(2,279)		(1,959)		(2,169)		(2,094)		(1,883)		(1,936)		(2,317)
	Coeff.	Std. err.	Coeff.	Std. err.	Coeff.	Std. err.	Coeff.	Std. err.	Coeff.	Std. err.	Coeff.	Std. err.	Coeff.	Std. err.
intercept	4.145	0.312	6.242	0.369	2.7910	0.311	6.7280	0.358	7.7810	0.394	12.1400	0.401	3.8730	0.331
age	-0.035	0.002	-0.040	0.003	-0.0350	0.003	-0.0327	0.003	-0.0464	0.003	-0.0392	0.004	-0.0498	0.003
	-0.006	<0.001	-0.004	<0.001	-0.0262	<0.001	-0.0028	<0.001	-0.0078	<0.001	-0.0053	<0.001	-0.0334	<0.001
	0.0002	<0.001	0.0205	<0.001	0.0190	<0.001	-0.0151	<0.001	0.0306	<0.001	0.0557	<0.001	0.0752	<0.001
	0.0066	<0.001	0.0182	<0.001	-0.0352	<0.001	-0.1079	<0.001	-0.1418	<0.001	-0.0131	<0.001	0.1060	<0.001
precip	-0.0037	0.008	-0.0118	0.009	-0.0037	0.008	-0.0248	0.009	0.0094	0.011	-0.0518	0.012	-0.0252	0.008
min_temp	-0.030	0.009	0.0129	0.010	-0.0092	0.009	0.0497	0.010	-0.0321	0.011	-0.0447	0.013	0.0532	0.009
max_temp	0.025	0.007	-0.030	0.008	0.0249	0.007	-0.0568	0.008	-0.0124	0.009	-0.0554	0.010	-0.0311	0.007
wet_days	0.005	0.002	-0.010	0.002	0.0115	0.002	0.0031	0.002	-0.0028	0.002	0.0004	0.003	-0.0061	0.002
freeze_	-1.697	<0.001	1.513	<0.001	-0.2029	<0.001	1.8550	<0.001	-1.4100	0.001	-13.7900	0.001	4.2370	<0.001
rut_depth	-0.632	0.115	-1.002	0.115	-1.1060	0.095	-0.5614	0.117	-0.8408	0.119	-0.3999	0.143	-0.9900	0.099
lane=2	-0.371	0.029	-0.166	0.027	0.0121	0.027	-0.1216	0.028	-0.5574	0.032	-0.2745	0.032	0.0258	0.026
lane≥3	-0.325	0.044	-0.195	0.046	-0.1713	0.052	-0.3215	0.045	-0.3974	0.052	-0.2511	0.054	0.0391	0.046
nhs	-0.442	0.079	-0.407	0.172	0.7454	0.138	-0.2811	0.072	-0.2639	0.150	-0.4300	0.085	1.4320	0.138
stp	-0.802	0.094	-0.487	0.180	0.6121	0.153	-0.3452	0.091	-0.1346	0.131	-0.3379	0.104	1.1350	0.151
f_class=2	0.521	0.115	0.0940	0.165	-0.9383	0.136	0.4118	0.091	0.7050	0.140	0.9474	0.115	-1.5090	0.149
f_class=3	0.382	0.100	0.3377	0.174	-0.8551	0.139	0.3270	0.080	0.3107	0.148	0.4025	0.101	-1.3770	0.136
f_class=4	0.619	0.112	0.2964	0.181	-0.6925	0.151	0.2945	0.094	0.0189	0.137	0.5468	0.117	-1.0330	0.146
f_class=5	0.618	0.113	-0.0703	0.182	-0.6471	0.152	-0.7763	0.098	-0.6250	0.143	0.3015	0.119	-1.5590	0.148
f_class=6	0.472	0.118	-0.419	0.188	-1.2170	0.159	-0.6566	0.108	-0.7955	0.145	0.0582	0.124	-1.7880	0.154
f_class=7	0.554	0.204	0.416	0.199	-1.3840	0.173	-0.1375	0.171	NA	0.151	0.7614	0.144	-1.3640	0.169
category=2	-0.306	0.070	-0.0444	0.051	0.0762	0.056	-0.1300	0.045	-0.6077	0.051	-0.1175	0.065	0.0246	0.035
category=3	-0.319	0.073	-0.0156	0.056	-0.0421	0.060	-0.2457	0.051	-0.6000	0.056	-0.2317	0.069	-0.0080	0.040
category=4	-0.468	0.077	-0.298	0.064	-0.3665	0.065	-0.1535	0.062	-0.6331	0.066	-0.5050	0.077	-0.3982	0.052
category=5	-0.463	0.077	-0.356	0.064	-0.7165	0.065	-0.1899	0.063	-0.8446	0.067	-0.6001	0.076	-0.4145	0.052
BIC	136		338		238		216		496		857		271

Note: the quantity included in parentheses represents the total number of observations in a cluster.
variable value in thousands.
coefficient with p value > 0.05.
NA = not applicable.