|
| Methodology | Advantages | Disadvantages |
|
| Fuzzy logic | (i) It converts the binary value into the linguistic description hence portraying the traffic congestion state. | (i) No appropriate membership function shape selection method exists. |
| (ii) iIt can portray more than two states. | (ii) Traffic pattern recognition capability is not as durable as ML algorithms. |
| (iii) As it does not need an exact crisp input, it can deal with uncertainty. | (iii) Traffic state may not match the actual traffic state as the outcome is not exact. |
| Hidden Markov model | (i) The model can overcome noisy measurements. | (i) Accuracy decreases with scarce temporal probe trajectory data |
| (ii) Can efficiently learn from non-preprocessed data. | (ii) Not suitable in case of missing dataset. |
| (iii) Can evaluate multiple hypotheses of the actual mapping simultaneously. | |
| Gaussian mixture model | (i) Can do traffic parameter distribution over a period as a mixture regardless of the traffic state. | (i) Optimization algorithm used with GMM must be chosen cautiously. |
| (ii) Can overcome the limitation of not being able to account for multimodal output by a single Gaussian process. | (ii) Results may show wrong traffic patterns due to local optima limitation and lack of traffic congestion threshold knowledge of the optimisation algorithm. |
| Bayesian network | (i) It can understand the underlying relationship between random variables. | (i) Computationally expensive. |
| (ii) It can model and analyse traffic parameters between adjacent road links. | (ii) The model performs poorly with the increment in data. |
| (iii) The model can work with incomplete data. | (iii) The model represents one-directional relation between variables only. |
|
