Signal control using fuzzy logic
Fuzzy Sets and Systems - special issue on fuzzy sets in traffic and transport systems
Automated Phase Design and Timing Adjustment for Signal Phase Design
Applied Intelligence
Vision Based Adaptive Traffic Signal Control System Development
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
Towards a Logic of Perishable Propositions
Applied Intelligence
The Research on Optimal Green Time For Intersection Groups
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 03
Adaptive Traffic Signals Control by Using Fuzzy Logic
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
An iterative approach to enhanced traffic signal optimization
Expert Systems with Applications: An International Journal
Real-Time Detection of Vehicles for Advanced Traffic Signal Control
ICCEE '08 Proceedings of the 2008 International Conference on Computer and Electrical Engineering
Information Sciences: an International Journal
Cooperative particle swarm optimization for multiobjective transportation planning
Applied Intelligence
A real-time transportation prediction system
Applied Intelligence
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Traffic lights are installed at intersections mostly for traffic management. Traffic signals turn on during the amount of time determined. Intelligent traffic management systems emerge as a need to handle the dynamicity of traffic. These systems are first implemented on simulators in order to mimic the real life situations before realization.Yet, we have implemented a real time traffic simulator with an adaptive fuzzy inference algorithm that arranges the foreseen light signal duration. It changes the time duration of lights depending on waiting vehicles behind green and red lights at crossroad. The simulation has also been supported with real time graphical visualization. Given a scenario, it creates random traffic flows according to specified parameters. Next, obtained results have been interpreted in the simulation environment.According to inferences from adaptive environment, TSK (Takagi-Sugeno-Kang) and Mamdani models have also been implemented to give baselines for verification. Several experiments have been conducted and compared against classical techniques such as Webster (1958) Road research technical paper No 39 and HCM (2000) TRB, special report 209, statistically to demonstrate the effectiveness of the proposed method.