Table 4.

Parameter estimates for full and restricted and nonlinear least squares models (L_eq) and ordinary least squares models (L_peak and L_max)

	Full model		Restricted model
	Parameter estimate (SD)	p valuea	Parameter estimate (SD)	p valuea
y: Leq (dBA)
	82.9 (1.05)	<0.001	83.0 (1.04)	<0.001
	0.489 (0.070)	<0.001	0.516 (0.077)	<0.001
(break at 53 km/h)	−0.430 (0.124)	0.001	−0.462 (0.122)	<0.001
	6.78 (2.00)	0.001	5.06 (0.680)	<0.001
	0.260 (0.145)	0.094
	−0.278 (0.193)	0.169
	1.98 (0.658)	0.004	1.81 (0.856)	0.003
	−0.706 (1.06)	0.503
RMSE	5.030		5.095
R2	0.479		0.457
y: Lpeak (dB)
	118 (5.98)	<0.001	113 (0.507)	<0.001
	−0.352 (0.297)	0.351
	−5.92 (5.96)	0.387
	0.425 (0.298)	0.305
	1.07 (0.517)	0.118
	−0.121 (0.889)	0.889
RMSE	4.632		4.766
R2	0.073		0.000
y: Lmax (dBA)
	88.2 (2.07)	<0.001	88.2 (2.07)	<0.001
	−0.115 (0.102)	0.310	−0.114 (0.107)	0.258
	2.51 (2.12)	0.310	2.50 (2.11)	0.258
	0.309 (0.104)	0.006	0.309 (0.104)	0.005
	1.56 (0.104)	<0.001	1.52 (0.412)	0.001
	−0.375 (0.858)	0.660
RMSE	3.505		3.499
R2	0.4708		0.462

All models accounted for clustering in line segments, thereby estimating robust standard errors. N = 266 for all models. Both the full and restricted nonlinear least squares models of L_eq converged in 11 iterations

^aAll p values were corrected for multiple comparisons using the false discovery rate⁴¹, using p.adjust() in R version 2.9