Centroid of a type-2 fuzzy set
Information Sciences: an International Journal
Traffic signal control on similarity logic reasoning
Fuzzy Sets and Systems - Theme: Fuzzy control
Agent-Based Control for Networked Traffic Management Systems
IEEE Intelligent Systems
New geometric inference techniques for type-2 fuzzy sets
International Journal of Approximate Reasoning
Toward a Revolution in Transportation Operations: AI for Complex Systems
IEEE Intelligent Systems
A multiagent approach to autonomous intersection management
Journal of Artificial Intelligence Research
Neural Networks for Real-Time Traffic Signal Control
IEEE Transactions on Intelligent Transportation Systems
Queuing Models for Analysis of Traffic Adaptive Signal Control
IEEE Transactions on Intelligent Transportation Systems
Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Fuzzy Systems
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems
IEEE Transactions on Fuzzy Systems
Geometric Type-1 and Type-2 Fuzzy Logic Systems
IEEE Transactions on Fuzzy Systems
Markovian real-time adaptive control of signal systems
Mathematical and Computer Modelling: An International Journal
Neural Networks for Continuous Online Learning and Control
IEEE Transactions on Neural Networks
Multi-agent system in urban traffic signal control
IEEE Computational Intelligence Magazine
Study of traffic flow controlled with independent agent-based traffic signals
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part II
Effects of type reduction algorithms on forecasting accuracy of IT2FLS models
Applied Soft Computing
Engineering Applications of Artificial Intelligence
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Rapid urbanization and the growing demand for faster transportation has led to heavy congestion in road traffic networks, necessitating the need for traffic-responsive intelligent signal control systems. The developed signal control system must be capable of determining the green time that minimizes the network-wide travel time delay based on limited information of the environment. This paper adopts a distributed multiagent-based approach to develop a traffic-responsive signal control system, i.e., the geometric fuzzy multiagent system (GFMAS), which is based on a geometric type-2 fuzzy inference system. GFMAS is capable of handling the various levels of uncertainty found in the inputs and rule base of the traffic signal controller. Simulation models of the agents designed in PARAMICS were tested on virtual road network replicating a section of the central business district in Singapore. A comprehensive analysis and comparison was performed against the existing traffic-control algorithms green link determining (GLIDE) and hierarchical multiagent system (HMS). The proposed GFMAS signal control outperformed both the benchmarks when tested for typical traffic-flow scenarios. Further tests show the superior performance of the proposed GFMAS in handling unplanned and planned incidents and obstructions. The promising results demonstrate the efficiency of the proposed multiagent architecture and scope for future development.