Short-Term Traffic Flow Forecasting Using Fuzzy Logic System Methods

Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

“…The maximum likelihood (ML) model is robust for sensor failures and rapid change in conditions [7]. Despite the benefits of the exponential smoothing method for predicting traffic flow, it is very difficult to determine constant convergence for the model during major changes in traffic flow [8]. The simplicity and strong potential of time series models for online operation makes these models popular for most traffic predictions.…”

“…The second disadvantage is their high dependence on input data. Incomplete or inaccurate input data produce an inaccurate time series model, resulting in an incorrect prediction [8]. Time series models used to predict traffic flow include the auto-regressive integrated moving average (ARIMA) [9], seasonal ARIMA [10], vector ARIMA [11], and ARIMA with EXtra (ARIMAX) [12].…”

“…The biggest flaw in neural networks is the type of training required and their need to handle large volumes of data to train the network weights. Fuzzy [14], k-nearest neighbour (KNN) [15], support vector machine [16][17], Bayesian networks [18] and wavelet [19] are other parametric methods used to predict traffic flow.…”

“…In recent years, researchers have used linear and non-linear hybrids of parametric and non-parametric methods to increase traffic flow prediction accuracy. The accuracy of hybrid methods depends on the type of parametric and non-parametric methods used; however, the computational complexity and developmental costs of hybrid methods exceed the individual parametric and non-parametric methods [8]. Some of these methods are combinations of a neural network model and models such as the genetic algorithm (GA) [20], fuzzy [3], wavelet [4] and ARIMA [21] models.…”

“…A general well-defined, robust, highly efficient and comprehensive method has yet to be developed to forecast traffic flow and deliver an accurate response for all aspects of traffic [8]. The high volume of data combined with inaccurate and noisy data is a major disadvantage of these three prediction methods.…”