Short-term traffic flow forecasting: parametric and nonparametric approaches via emotional temporal difference learning

Information signal from real case and natural complex dynamical systems such as traffic flow are usually specified by irregular motions. Chaotic nonlinear dynamics approach is now the most powerful tool for scientists to deal with complexities in real cases, and neural networks and neuro-fuzzy models are widely used for their capabilities in nonlinear modeling of chaotic systems more than the traditional methods. As mentioned, the traffic flow conditions caused the forecasting values of traffic flow to lack robustness and accuracy. In this paper, the traffic flow forecasting is analyzed with emotional concepts and multi-agent systems (MASs) points of view as a new method in this field. The findings enabled the researchers to develop a newly object-oriented method of forecasting traffic flow. Its architecture is based on a temporal difference (TD) Q-learning with a neuro-fuzzy structure, which is the nonparametric approach. The performance of TD Q-learning is improved by emotional learning. The proposed method on the present conditions and the action of the system according to the criteria could forecast traffic signals so that the objectives are reached in minimum time. The ability of presented learning algorithm to prospect gains from future actions and obtain rewards from its past experiences allows emotional TD Q-learning algorithm to improve its decisions for the best possible actions. In addition, to study in a more practical situation, the neuro-fuzzy behaviors could be modeled by MAS. The proposed method (intelligent/nonparametric approach) is compared by parametric approach, autoregressive integrated moving average (ARIMA) method, which is implemented by multi-layer perceptron neural networks and called ARIMANN. Here, the ARIMANN is updated by backpropagation and temporal difference backpropagation for the first time. The simulation results revealed that the studied forecaster could discover the optimal forecasting by means of the Q-learning algorithm. Difficult to handle through parametric and classic methods, the real traffic flow signals used for fitting the algorithms is obtained from a two-lane street I-494 in Minnesota City.