Developing operating speed models for elevated multilane urban arterials using artificial neural networks

Said, Dalia; Reyad, Fady; Talaat, Hoda

doi:10.1186/s44147-023-00288-4

Journal of Engineering and Applied Science

Table 6 Spearman correlation analysis for dataset # 2

From: Developing operating speed models for elevated multilane urban arterials using artificial neural networks

Variables		V_{85 (n)}	L_n	C_n	PS_n	L _(n-1)	V₈₅ _(n-1)	C_(n-1)	L_(n-2)	V₈₅ _(n-2)	C_(n-2)	L_(n-3)	V₈₅ _(n-3)	C_(n-3)	L_(n-4)	V₈₅ _(n-4)	C_(n-4)
V_{85 (n)}	Corr	1	0.094	0.065	.184^a	.331^b	.404^b	.293^b	0.031	.443^b	-0.003	0.12	.280^b	0.126	0.119	.316^b	-0.038
V_{85 (n)}	Sig	1	0.169	0.253	0.03	0	0	0.001	0.377	0	0.486	0.111	0.002	0.1	0.113	0	0.349
L_n	Corr	0.094	1	.493^b	.286^b	-0.04	.249^b	-.278^b	.226^b	0.057	.477^b	-0.086	0.057	-.425^b	.232^b	0.001	.444^b
L_n	Sig	0.169		0	0.001	0.342	0.005	0.002	0.01	0.28	0	0.191	0.282	0	0.008	0.495	0
C_n	Corr	0.065	.493^b	1	.515^b	-.264^b	.253^b	-.661^b	.444^b	-0.048	.791^b	-.374^b	.198^a	-.672^b	.369^b	-0.111	.640^b
C_n	Sig	0.253	0	1	0	0.003	0.004	0	0	0.313	0	0	0.021	0	0	0.128	0
PS_n	Corr	.184^a	.286^b	.515^b	1	0.072	.226^b	-.254^b	.237^b	-0.001	.420^b	-0.061	.171^a	-.258^b	.183^a	-0.05	.214^a
PS_n	Sig	0.03	0.001	0	1	0.23	0.01	0.004	0.007	0.496	0	0.266	0.04	0.004	0.03	0.304	0.014
L _(n-1)	Corr	.331^b	-0.04	-.264^b	0.072	1	0.137	.455^b	-0.005	.271^b	-.264^b	.236^b	0.083	.443^b	-0.061	0.049	-.373^b
L _(n-1)	Sig	0	0.342	0.003	0.23	1	0.081	0	0.478	0.002	0.003	0.007	0.197	0	0.268	0.309	0
V_{85 (n-1)}	Corr	.404^b	.249^b	.253^b	.226^b	0.137	1	0.139	.347^b	.423^b	.275^b	0.043	.451^b	0.052	.167^a	.305^b	.188^a
V_{85 (n-1)}	Sig	0	0.005	0.004	0.01	0.081	1	0.078	0	0	0.002	0.332	0	0.297	0.044	0.001	0.027
C _(n-1)	Corr	.293^b	-.278^b	-.661^b	-.254^b	.455^b	0.139	1	-.216^a	.286**	-.672^b	.439^b	0	.766^b	-.352^b	.250^b	-.661^b
C _(n-1)	Sig	0.001	0.002	0	0.004	0	0.078	1	0.013	0.001	0	0	0.499	0	0	0.005	0
L _(n-2)	Corr	0.031	.226^b	.444^b	.237^b	-0.005	.347^b	-.216^a	1	0.151	.428^b	-0.024	.290^b	-.205^a	.242^b	0.067	.431^b
L _(n-2)	Sig	0.377	0.01	0	0.007	0.478	0	0.013	1	0.062	0	0.404	0.001	0.017	0.006	0.246	0
V_{85 (n-2)}	Corr	.443^b	0.057	-0.048	-0.001	.271^b	.423^b	.286^b	0.151	1	0.081	.325^b	.433^b	.321^b	0.022	.453^b	0.015
V_{85 (n-2)}	Sig	0	0.28	0.313	0.496	0.002	0	0.001	0.062	1	0.204	0	0	0	0.413	0	0.438
C _(n-2)	Corr	-0.003	.477^b	.791^b	.420^b	-.264^b	.275^b	-.672^b	.428^b	0.081	1	-.294^b	.228^b	-.683^b	.425^b	-0.078	.791^b
C _(n-2)	Sig	0.486	0	0	0	0.003	0.002	0	0	0.204	1	0.001	0.009	0	0	0.214	0
L _(n-3)	Corr	0.12	-0.086	-.374^b	-0.061	.236^b	0.043	.439^b	-0.024	.325^b	-.294^b	1	0.123	.433^b	-0.034	.250^b	-.223^a
L _(n-3)	Sig	0.111	0.191	0	0.266	0.007	0.332	0	0.404	0	0.001	1	0.104	0	0.364	0.005	0.011
V_{85 (n-3)}	Corr	.280^b	0.057	.198^a	.171^a	0.083	.451^b	0	.290^b	.433^b	.228^b	0.123	1	0.152	.331^b	.418^b	.284^b
V_{85 (n-3)}	Sig	0.002	0.282	0.021	0.04	0.197	0	0.499	0.001	0	0.009	0.104	1	0.059	0	0	0.002
C _(n-3)	Corr	0.126	-.425^b	-.672^b	-.258^b	.443^b	0.052	.766^b	-.205^a	.321^b	-.683^b	.433^b	0.152	1	-.252^b	.260^b	-.672^b
C _(n-3)	Sig	0.1	0	0	0.004	0	0.297	0	0.017	0	0	0	0.059	1	0.005	0.004	0
L _(n-4)	Corr	0.119	.232^b	.369^b	.183^a	-0.061	.167^a	-.352^b	.242^b	0.022	.425^b	-0.034	.331^b	-.252^b	1	0.114	.434^b
L _(n-4)	Sig	0.113	0.008	0	0.03	0.268	0.044	0	0.006	0.413	0	0.364	0	0.005	1	0.123	0
V_{85 (n-4)}	Corr	.316^b	0.001	-0.111	-0.05	0.049	.305^b	.250^b	0.067	.453^b	-0.078	.250^b	.418^b	.260^b	0.114	1	0.109
V_{85 (n-4)}	Sig	0	0.495	0.128	0.304	0.309	0.001	0.005	0.246	0	0.214	0.005	0	0.004	0.123	1	0.133
C _(n-4)	Corr	-0.038	.444^b	.640^b	.214^a	-.373^b	.188^a	-.661^b	.431^b	0.015	.791^b	-.223^a	.284^b	-.672^b	.434^b	0.109	1
C _(n-4)	Sig	0.349	0	0	0.014	0	0.027	0	0	0.438	0	0.011	0.002	0	0	0.133	1

^aCorrelation is significant at the 0.05 level
^bCorrelation is significant at the 0.01 level

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