Adaptive Traffic Controller Based On Pre-Timed System

Freddy Kurniawan, Haruno Sajati, Okto Dinaryanto


Adaptive traffic controller systems based on image processing have been developed widely. Nevertheless, in a developing country, the systems often could not be easily applied because all types of vehicle use the same road. Therefore, to overcome the problem, the new concept of the systems is proposed. The systems were developed from a pre-timed traffic controller system that based on AVR microcontroller. By default, the systems use the signal-timing plans to control the vehicle flow. To accommodate the traffic variations, a new method of vehicle detection has been built. The method calculated an intensity histogram standard deviation of the image representing a detection area to determine traffic density of each intersection lane. The systems modified the green-time of each lane based on the traffic density. The method could detect all types of vehicles and work properly in a day and a night time.


determine traffic density; standard deviation; intensity histogram; signal-timing plan; AVR microcontroller

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Dobre C. Using Intelligent Traffic signals to Reduce Vehicle Emissions. International Journal of Innovative Computing, Information and Control. 2012; 8(9): 6283-6302.

Zhu H, Fan H, Guo S. Moving Vehicle Detection and Tracking in Traffic Images based on Horizontal Edges. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2013; 11(11): 6477-6483.

Fazli S, Mohammadia S, Rahmani M. Neural Network based Vehicle Classification for Adaptive Traffic Control. International Journal of Software Engineering & Applications. 2012; 3(3): 17-22.

Khan BA, Lai NS. An Advanced Fuzzy Logic based Traffic Controller. International Journal of Innovation in the Digital Economy. 2014; 5(1): 31-40.

Askerzade IN, Mahmood M. Control the Extension Time of Traffic Light in Single Junction by Using Fuzzy Logic. International Journal of Electrical & Computer Sciences IJECS – IJENS. 2010; 10(2): 48-55

Sutjiadi R, Setyadi E, Lim R. Adaptive Background Extraction for Video Based Traffic Counter Application Using Gaussian Mixture Models Algorithm. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2015; 13(3): 1006-1013

Chirag I.P, Ripal P. Counting Cars in Traffic Using Cascade Haar with KLP. International Journal of Computer and Electrical Engineering. 2013; 5(4): 435-437.

Abbas N, Tayyab M, Qadri MT. Real Time Traffic Density Count using Image Processing. International Journal of Computer Application. 2013; 83(9): 16-19.

Chaple M, Paygude SS. Vehicle Detection and Tracking from Video Frame Sequence. International Journal of Science & Engineering Research. 2013; 4(3): 1-7.

Jatmiko W, Azurat A, Wibowo AH, Marihot H, Wicaksana M, Takagawa I, Sekiyama K, Fukuda T. Self-Organizing Urban Traffic Control Architecture with Swarm-Self Organizing Map in Jakarta: Signal Control System and Simulator. International Journal on Smart Sensing and Adaptive Systems. 2010; 3(3): 443-465.

Kurniawan F, Dermawan D, Dinaryanto O, Irawati M. Pre-Timed and Coordinated Traffic Controller Systems Based on AVR Microcontroller. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2014; 12(4): 787-794.

Dunne P, Matuszewski BJ. Histogram-based Detection of Moving Objects for Tracker Initialization in Surveillance Video. International Journal of Grid and Distributed Computing. 2011; 4(3): 71-78.

Wang J, Sun X, Guo J. A Region Tracking-Based Vehicle Detection Algorithm in Night time Traffic Scenes. Sensors. 2013; 13: 16474-16493.


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