A Review of Traffic Congestion Prediction Using Artificial Intelligence

Table 5

The strength and weakness of the models of shallow machine Learning.

Methodology	Advantages	Disadvantages
Artificial neural network	(i) It is an adaptive system that can change structure based on inputs during the learning stage [96].	(i) BPNN requires vast data for training the model due to the parameter complexity resulting from its parameter nonsharing technique [97].
	(ii) It features defined early, FNN shows excellent efficiency in capturing the nonlinear relationship of data.	(ii) The training convergence rate of the model is slow.
Regression model	(i) Models are suitable for time series problems.	(i) Linear models cannot address nonlinearity, making it harder to solve complex prediction problems.
	(ii) Traffic congestion forecasting problems can be easily solved.	(ii) Linear models are sensitive to outliers.
	(iii) ARIMA can increase accuracy by maintaining minimum parameters.	(iii) Computationally expensive.
	(iv) Minimum complexity in the model.	(iv) ARIMA cannot deal multifeature dataset efficiently.
		(v) ARIMA cannot capture the rapidly changing traffic flow [8].
Support vector machine	(i) It is efficient in pattern recognition and classification.	(i) The improperly chosen kernel function may result in an inaccurate outcome.
	(ii) A universal learning algorithm that can diminish the classification error probability by reducing the structural risk [1].	(ii) Unstable traffic flow requires improved prediction accuracy of SVM.
	(iii) It does not need a vast sample size.	(iii) It takes high computational time and memory.