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

Table 3

Traffic occupancy prediction under all conditions.

Model	Input resolution (minutes)
	0.5			5			15
	Accuracy	RMSE	MAE	Accuracy	RMSE	MAE	Accuracy	RMSE	MAE
ANN	0.859 (0.02)	1.98 (0.64)	1.00 (0.37)	0.838 (0.01)	2.59 (0.74)	1.27 (0.44)	0.780 (0.03)	3.51 (0.89)	1.70 (0.57)
RF	0.872 (0.01)	1.83 (0.48)	0.90 (0.30)	0.850 (0.01)	2.17 (0.55)	1.07 (0.35)	0.80 (0.03)	2.80 (0.70)	1.43 (0.47)
SVR	0.858 (0.01)	1.88 (0.46)	0.95 (0.30)	0.829 (0.01)	2.13 (0.52)	1.12 (0.33)	0.732 (0.04)	2.54 (0.59)	1.45 (0.34)
Historical avg.	0.433 (0.02)	7.49 (4.50)	3.56 (1.02)	0.433 (0.02)	7.49 (4.50)	3.56 (1.02)	0.433 (0.02)	7.49 (4.50)	3.56 (1.02)
ARIMA	0.689 (0.04)	20.5 (4.71)	10.1 (2.65)	0.833 (0.02)	2.37 (0.70)	1.17 (0.41)	0.834 (0.02)	2.59 (0.80)	1.22 (0.43)

Standard deviations across segments are reported in parentheses and numbers in boldface show the best results.