Expert Systems as Knowledge Servers
IEEE Expert: Intelligent Systems and Their Applications
Modelling of traffic flow and air pollution emission with application to Hong Kong Island
Environmental Modelling & Software
An evaluation of freeway lane control signing using computer simulation
Mathematical and Computer Modelling: An International Journal
A novel self-organizing fuzzy rule-based system for modelling traffic flow behaviour
Expert Systems with Applications: An International Journal
The first search right algorithm for redundant reader elimination in RFID network
SEPADS'10 Proceedings of the 9th WSEAS international conference on Software engineering, parallel and distributed systems
A simulation-based fuzzy model for traffic signal control
CI'10 Proceedings of the 4th WSEAS international conference on Computational intelligence
A simulation-based fuzzy model for traffic signal control
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
A knowledge-based problem solving method in GIS application
Knowledge-Based Systems
ACM SIGAPP Applied Computing Review
Automatic linguistic report of traffic evolution in roads
Expert Systems with Applications: An International Journal
Presenting a fuzzy model to control and schedule traffic lights
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 12.05 |
Traffic congestion is a severe problem in many modern cities around the world. To solve the problem, we have proposed a framework for a dynamic and automatic traffic light control expert system combined with a simulation model, which is composed of six submodels coded in Arena to help analyze the traffic problem. The model adopts interarrival time and interdeparture time to simulate the arrival and leaving number of cars on roads. In the experiment, each submodel represents a road that has three intersections. The simulation results physically prove the efficiency of the traffic system in an urban area, because the average waiting time of cars at every intersection is sharply dropped when the red light duration is 65s and the green light time duration is 125s. Meanwhile, further analysis also shows if we keep the interarrival time of roads A, B, and C, and change that of roads D, E, and F from 1.7 to 3.4s and the interdeparture times at the three intersections on roads A, B, and C are equal to 0.6s, the total performance of the simulation model is the best. Finally, according to the data collected from RFID readers and the best, second and third best traffic light durations generated from the simulation model, the automatic and dynamic traffic light control expert system can control how long traffic signals should be for traffic improvement.