A novel self-organizing fuzzy rule-based system for modelling traffic flow behaviour

Design and implementation of a neuro-fuzzy system for longitudinal control of autonomous vehicles

“…Moreover, fuzzy inference systems provide an efficient environment or computing framework based on the concepts of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantages of combining neural networks and fuzzy inference systems are clearly presented in [8][9][10][11][12][13].…”

“…In ITS, neuro-fuzzy systems had been used for modelling traffic flow behaviour, using a specific class of neuro-fuzzy system known as the Pseudo Outer-Product Fuzzy-Neural Network, with Truth-Value-Restriction method (POPFNN-TVR) [11].…”

How to Provide Accurate and Robust Traffic Forecasts Practically?

“…Besides the above neural networks models, computational intelligence (CI) techniques that encompass fuzzy systems, machine learning and evolutionary computation have been successfully developed in the field of traffic forecasting. For instance, some literature applies Bayesian networks (Zhang et al , 2004;Castillo et al, 2008) and Bayesian inference based regression techniques (Khan, 2011;Tebaldi et al, 2002;Sun et al, 2005Sun et al, , 2006Zheng et al, 2006;Ghosh et al, 2007), some literature uses fuzzy systems or fuzzy NNs to predict the traffic states (Dimitriou et al, 2008;Quek et al, 2009). While others start to explore support vector regression (SVR) to model traffic characteristics and produce prediction of traffic states (Castro-Neto, 2009;Ding et al, 2002;Hong, 2011;Hong et al, 2011;Wu et al, 2004;Vanajakshi & Rilett, 2004).…”

An Optimal Investment Scheduling Framework for Intelligent Transportation Systems Architecture

“…Quek, Pasquier, and Lim (2009) studied ITS and developed a self-organizing fuzzy rule-based system for modeling traffic flow. Wang, Lin, and Chen (2010) proposed a new lane-detection and lane-departure warning system on the basis of fuzzy rules.…”