An Artificial Intelligence Techniques and Simulation Model to Control a Traffic Jam System in Malaysia

Authors

  • Khaled Abdul Rahman Jomaa Faculty of Industrial Management, University Malaysia Pahang, 26300, Pahang

Keywords:

Transportation system, Traffic Congestion system, Simulation, Artificial Intelligence, Kuala Lumpur, Kuantan

Abstract

Traffic jam in Malaysia is a huge and complicated problem nowadays, due to the rapid increase in the demand for transportation. This causes a longer vehicle travel times, increased energy consumption, growing environmental pollution, reduced traffic safety, and a decrease in the efficiency of transportation infrastructure. Hence, controlling the flow of traffic has become a very important issue under a growing pressure to relieve traffic jam. In this study, a new Artificial Intelligence Techniques (AIT) and Simulation Model (SM) are applied in order to elicit a general diagnosis for the traffic congestion problem in Kuala Lumpur and Kuantan. An integrated model involves a Neural Network (NN), Fuzzy Logic (FL), Genetic Algorithm (GA), and Simulation Model (SM) is used. The current traffic demand data will be captured by strategically placed cameras. By receiving and processing data, we plan to use our integrated model to adjust traffic lights timing to optimize traffic flow in coordinated traffic lights systems, in order to minimize the traffic congestion through controlling traffic lights. The results of this study will be reported and used to suggest and apply more efficient transportation policies with the aim of providing useful insights on traffic congestion problem, and to assist the Malaysian decision makers to elaborate the best transportation policies.

References

Department of Statistics, Malaysia Official Website, 2010-2013.

Aldukali Salem, et. al, An Overview of Urban Transport in Malaysia, The Social Sciences, 2011 | Volume: 6 | Issue: 1 | Page No.: 24-33.

Frost and Sullivan . Survey finds KL road users’ frustration with traffic jams highest in the, the Malaysian Insider, 8 January 2014.

Osigwe U. Chinyere, Et. Al. Design and Simulation of an Intelligent Traffic Control System, International Journal of Advances in Engineering & Technology, Nov 2011.

Kavya P Walad, Jyothi Shetty,Traffic Light Control System Using Image Processing , International Journal of Innovative Research in Computer and Communication Engineering Vol.2, Special Issue 5, October 2014.

Mohit D. Srivastava et. al. Smart Traffic Control System Using PLC And SCADA, International Journal of Innovative Research in Science, Engineering and Technology Vol. 1, Issue 2, December 2012.

Yue-Jiao Gong and Jun Zhang, Real-Time Traffic Signal Control for Modern Roundabouts by Using Particle Swarm Optimization-Based Fuzzy Controller, Sun Yat-sen University Technical Report – SYSU – 2011

Y.P.Singh, Pradeep K. Mittal, Analysis and Designing of Proposed Intelligent Road Traffic Congestion Control System with Image Mosaicking Technique, International Journal of IT, Engineering and Applied Sciences Research (IJIEASR) ISSN: 2319-4413, Volume 2, No. 4, April 2013.

Muzhir Shaban Al-Ani And Khattab Alheeti, Intelligent Traffic Light Control System Based Image Intensity Measurement, Al-Anbar University- College Of Computer Science - Iraq

Madhavi Arora, V. K. Banga, Real Time Traffic Light Control System Using Morphological Edge Detection and Fuzzy Logic, 2nd International Conference on Electrical, Electronics and Civil Engineering (ICEECE'2012) Singapore April 28-29, 2012

Siuli Roy et. al. Real time traffic congestion detection and management using Active RFID and GSM technology, research supported by “Smart-Road: An Intelligent Traffic Congestion Management System using RFID and wireless Networking Technology project of IIM Calcutta, India.

Erwin Normanyo, et. al. Telemetric Control of Traffic Lights Intersections in Ghana, Proceedings of the World Congress on Engineering and Computer Science 2009 Vol I.

Ahmed A. Ezzat, et. al. Development of a Stochastic Genetic Algorithm for Traffic Signal Timings Optimization, Proceedings of the Industrial and Systems Engineering Research Conference 2014.

Sanchez, J., M. Galan and E. Rubio. "Applying a Traffic Lights Evolutionary Optimization Technique to a Real Case: “Las Ramblas†Area in Santa Cruz de Tenerife." Evolutionary Computation, IEEE Transactions 2008 on 12(1):25-40.

Javier J. et al. Traffic Signals in Traffic Circles: Simulation and Optimization Based Efficiency Study. R. Moreno-D´ıaz et al. (Eds.): EUROCAST 2009, LNCS 5717, pp. 453–460.

Kaur, D. and E. Konga. "Fuzzy traffic light controller." In Circuits and Systems, Proceedings of the 37th Midwest Symposium 1994.

Hong, Wei, Yong Wang, Xuanqin Mu and Yan Wu. "A cooperative fuzzy control method for traffic lights." In Intelligent Transportation Systems,Proceedings. 2001 IEEE.

Hong, You-Sik, Geuk Lee, Cheonshik Kim and Jong Kim. "Traffic Signal Planning Using a Smart Agent System." In Agent and Multi-Agent Systems: Technologies and Applications 2007.

de la Escalera, A., J. M. Armingol and M. Mata. "Traffic sign recognition and analysis for intelligent vehicles." Image and Vision Computing 2003, 21(3):247-258.

Kuei-Hsiang, Chao, Lee Ren-Hao and Yen Kun-Lung. "An intelligent traffic light control method based on extension theory for crossroads." In Machine Learning and Cybernetics, International Conference2008.

Chao, Kuei-Hsiang, Ren-Hao Lee and Meng-Hui Wang. "An Intelligent Traffic Light Control Based on Extension Neural Network." In Knowledge-Based Intelligent Information and Engineering Systems 2009.

http://en.wikipedia.org/wiki/Traffic_simulation.

Downloads

Published

2016-02-15

How to Cite

Jomaa, K. A. R. (2016). An Artificial Intelligence Techniques and Simulation Model to Control a Traffic Jam System in Malaysia. Asian Journal of Business and Management, 4(1). Retrieved from https://www.ajouronline.com/index.php/AJBM/article/view/3488

Issue

Vol. 4 No. 1 (2016): February 2016

Section

Articles

License

  • Papers must be submitted on the understanding that they have not been published elsewhere (except in the form of an abstract or as part of a published lecture, review, or thesis) and are not currently under consideration by another journal published by any other publisher.
  • It is also the authors responsibility to ensure that the articles emanating from a particular source are submitted with the necessary approval.
  • The authors warrant that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required.
  • The authors ensure that all the references carefully and they are accurate in the text as well as in the list of references (and vice versa).
  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
  • The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.