Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Implementing fuzzy logic controllers using a neural network
Fuzzy Sets and Systems
Neurocontrol and fuzzy logic: connections and designs
International Journal of Approximate Reasoning - Special issue on fuzzy logic and neural networks for pattern recognition and control
A neuro-fuzzy adaptive control strategy for refuse incineration plants
Fuzzy Sets and Systems - Special issue on industrial applications
A structure of problem-solving methods for real-time decision support in traffic control
International Journal of Human-Computer Studies
Neural Fuzzy Control Systems with Structure and Parameter Learning
Neural Fuzzy Control Systems with Structure and Parameter Learning
An Evolutionary Fuzzy System for Coordinated and Traffic Responsive Ramp Metering
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 3 - Volume 3
POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Distributed and adaptive traffic signal control within a realistic traffic simulation
Engineering Applications of Artificial Intelligence
Decision support for coordinated road traffic control actions
Decision Support Systems
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When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control.