A Review of Traffic Congestion Prediction Using Artificial Intelligence

Table 6

The strength and weakness of the models of deep machine learning.

Methodology	Advantages	Disadvantages
Convolutional neural networks	(i) Capable of learning features from local connections and composing them into high-level representation.	(i) Computationally expensive as a huge kernel is needed for feature extraction.
	(ii) Classification is less time-consuming.	(ii) A vast dataset is required.
	(iii) Can automatically extract features.	(iii) Traffic data needs to be converted to an image.
		(iv) No available strategies are available on CNN model depth and parameter selection.
Recurrent neural network	(i) Shows excellent performance in processing sequential data flow.	(i) Long-term dependency results in bad performance.
	(ii) Efficient in sequence classification.	(ii) No available firm guideline in dependency elimination.
	(iii) Efficient in processing time-series with long intervals and postponements.
Extreme learning machine	(i) Fast learning speed	(i) Training time increases with the hidden node rise.
	(ii) Can avoid local minima.	(ii) Unlabelled data problem.
	(iii) Modified models are available to deal with an unlabelled data problem.	(iii) May produce less accurate results.