Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms

Table 2

Diebold–Mariano test statistic for each pair of models for predicting volume.

		0.5 min			5 min			15 min
		ANN	RF	SVR	ANN	RF	SVR	ANN	RF	SVR
0.5 min	ANN	—	36.88	12.69	−54.59	23.10	−19.35	−71.81	−17.20	−38.30
	RF	−36.88	—	−27.87	−69.47	−11.95	−50.30	−94.20	−44.32	−63.09
	SVR	−12.69	27.87	—	−65.29	14.55	−52.00	−92.40	−31.63	−62.36
5 min	ANN	54.59	69.47	65.29	—	71.18	48.64	−11.23	43.96	25.76
	RF	−23.10	11.95	−14.55	−71.18	—	−50.28	−100.1	−45.49	−66.02
	SVR	19.35	50.30	52.00	−48.64	50.28	—	−79.88	0.49	−57.11
15 min	ANN	71.81	94.20	92.40	11.23	100.1	79.88	—	68.62	51.22
	RF	17.20	44.32	31.63	−43.96	45.49	−0.49	−68.62	—	−29.18
	SVR	38.30	63.09	62.36	−25.76	66.02	57.11	−51.22	29.18	—

Critical value: ; numbers in boldface indicate pairs of models that are not significantly different.