Neuro-Fuzzy Prediction of Cooperation Interaction Profile of Flexible Road Train Based on Hybrid Automaton Modeling

Abstract

Abstract. Accurate prediction of traffic information is important in many applications in relation to Intelligent Transport systems (ITS), since it reduces the uncertainty of future traffic states and improves traffic mobility. There is a lot of research done in the field of traffic information predictions such as speed, flow and travel time. The most important research was done in the domain of cooperative intelligent transport system (C-ITS). The goal of this paper is to introduce the novel cooperation behaviour profile prediction through the example of flexible Road Trains useful road cooperation parameter, which contributes to the improvement of traffic mobility in Intelligent Transportation Systems. This paper presents an approach towards the control and cooperation behaviour modelling of vehicles in the flexible Road Train based on hybrid automaton and neuro-fuzzy (ANFIS) prediction of cooperation profile of the flexible Road Train. Hybrid automaton takes into account complex dynamics of each vehicle as well as discrete cooperation approach. The ANFIS is a particular class of the ANN family with attractive estimation and learning potentials. In order to provide statistical analysis, RMSE (root mean square error), coefficient of determination (R2) and Pearson coefficient (r), were utilized. The study results suggest that ANFIS would be an efficient soft computing methodology, which could offer precise predictions of cooperative interactions between vehicles in Road Train, which is useful for prediction mobility in Intelligent Transport systems.

Hybrid Automaton Based Vehicle Platoon Modelling and Cooperation Behaviour Profile Prediction

“…The predictive techniques are being accomodated to allow advanced nonlinear models using historical baseline traffic data, such as speed, flow and travel time. For traffic information prediction, between numerous nonparametric prediction approaches most often are used artificial neural networks (ANN), support vector regression (SVR), and the adaptive neurofuzzy system (ANFIS)) [15][16][17][18]. The main advantage of the artificial neural network (ANN) is its ability to model very complex multivariable systems, and the quality of prediction is tuned and improved by parameters of the network such as the number of hidden neurons and learning factor [19].…”

“…Similar approach in modelling is with SVR techniques where the predictive model relies heavily on proper determination of model parameters [17]. ANFIS predictive model has proven to have the quality for prediction of cooperative behaviour profile of the flexible Road Train [18].…”