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Abstrakty
The computational capabilities of computers enable a human being to control the vehicles and their traffic. Automatic traffic control system not only reduces the effort of human but also provides secure and accurate results. Here, the architecture of Agent-Based Autonomous Controller (ABAC) that manages vehicles at traffic signals intelligently was proposed. The proposed solution is followed by a vehicle counting by infrared (IR) sensor; providing solution for independent and mutual dependent signals for smooth traffic flow; emergency vehicles alert and priority over public vehicles through Radio Frequency Identification (RFID). Finally, the proposed research was tested through simulation that reveals the performance over the previous traffic control and management architectures.
Wydawca
Rocznik
Tom
Strony
216--225
Opis fizyczny
Bibliogr. 30 poz., fig., tab.
Twórcy
autor
- Department of Computer Science, Federal Urdu University of Arts, Science & Technology, Islamabad, Pakistan
autor
autor
- College of Computer and Information Systems, Al Yamamah University, Riyadh,11512, Saudi Arabia
autor
- Department of Computer Science, Federal Urdu University of Arts, Science & Technology, Islamabad, Pakistan
autor
- Department of Computer Science, Federal Urdu University of Arts, Science & Technology, Islamabad, Pakistan
autor
- Department of Computer Science, Federal Urdu University of Arts, Science & Technology, Islamabad, Pakistan
Bibliografia
- 1. Mueller E.A., Aspects of the history of traffic signals. IEEE T. on Vehicular Technology, 19(1), 1970, 6–17.
- 2. Horn P., Autonomic Computing: IBM’s Perspective on the State of Information Technology, IBM Journal, 2001.
- 3. Kephart J.O. and Chess D.M., The Vision of Autonomic Computing. Computer, 36(1), 2003, 41-50.
- 4. An Architectural blueprint for autonomic computing, IBM White paper, 3rd Edition, 2005.
- 5. Huebscher M.C.J. and Mccann A., A survey of Autonomic Computing Degrees, Models, & Applications”, ACM Computing Surveys, 40(3), 2008.
- 6. Stuart R. and Peter N., Artificial Intelligence, A Modern Approach, 2nd Edition, 81-7758-367-0, 2008.
- 7. Alagar V.S. and Muthiayen D., A Rigorous Approach to Modeling Autonomous Traffic Control Systems. In: The 6th International Symposium on Autonomous Decentralized Sys., Italy, 2003, 193-200.
- 8. Shamshirband S.S., Shirgahi H., Gholami M. and Kia B., Coordination between Traffic Signals Based on Cooperative, World App. Sc. J. Vol. 5 (5), 2008, 525-530.
- 9. Liu X. and Fang Z., An Agent-Based Intelligent Transport System. In: 11th Int. Conference on Computer Supported Cooperative Work in Design, 2007, 304-315.
- 10. Casey M., MPEG-7 Sound Recognition Tools, Mitsubishi Electric Research Labs, Cambridge, MA, USA.
- 11. Albagul A., Hrairi M., Wahyudi, and Hidayathullah M.F., Design & Development of Sensor Based Traffic Light System, American J. of App. Sc. 3 (3), 2006, 1745-1749.
- 12. Huang Y., Design of Traffic Light Control Systems Using Statecharts, The Computer Journal, 49(6), 2006.
- 13. Rajeshwari S., Hebbar S. and Golla V., Implementing Intelligent Traffic Control System for Congestion Control, Ambulance Clearance and Stolen Vehicle Detection.
- 14. Bode M., Jha S.S. and Nair S.B. A mobile agent based autonomous partial green corridor discovery and maintenance mechanism for emergency services amidst urban traffic. In: Proceedings of 1st Int. Conference on IoT in Urban Space, 2014, pp. 13-18.
- 15. Bharadwaj R., Deepak J., Baranitharan M. and Vaidehi V.V. Efficient dynamic traffic control system using wireless sensor networks. In: Recent Trends in Information Technology (ICRTIT), International Conference on, IEEE, 2013, 668-673.
- 16. Nafi N.S., Khan R.H., Khan J.Y. and Gregory M. A predictive road traffic management system based on vehicular ad-hoc network. In: Telecommunication Networks and Applications Conference, Australasian, IEEE, 2014, 135-140.
- 17. Grover S., Saxena V.S. and Vatwani T. Design of intelligent traffic control system using image segmentation. Int. J. of Advances in Eng. & Tech., 7(5), 2014.
- 18. El-Tantawy S., Abdulhai B. and Abdelgawad H. Multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC): Methodology and large-scale application on downtown Toronto. Intelligent Transportation Systems. IEEE Transactions on, 14(3), 2013, 1140-1150.
- 19. llmudin A., Hashim M.H.A., Ja’afar N.M.Z., Salleh A.S., Jaafar A., and Sam M.F.M. Traffic Light Control System using 434 MHz Radio Frequency. Int. J. of Research in Advent Technology, 2(8), 2014, 26-31.
- 20. Latha J.R. and Suman U. Intelligent Traffic Light Controller. International Journal, 38, 2015.
- 21. Ramzanzad M. and Kanan H.R. A new method for design and implementation of intelligent traffic control system based on fuzzy logic using FPGA. In IEEE Fuzzy Systems, 13th Iranian Conference, 2013, 1-4.
- 22. Alam M., Chowdhury M., and Purohit P. Development of an Intelligent Traffic Management System Based on Modified Round-Robin Algorithm Emergency, 7(12), 2014.
- 23. Champion A., Mandiau R., Kolski C., Heidet A. and Kemeny A. Traffic Generation with the SCANeR II Simulator: Towards Mulit-Agent Architecture. In: Proceedings of the first Driving Simulation Conference, 1999, 311-324.
- 24. Elementz Engineers Private Limited, ,July 2018 http://www.elementzonline.com/ir-infrared-obstacle-avoidance-sensor-module
- 25. Mreeco Electronics, July 2018, http://www.mreeco.com/product/ir-infrared-obstacle-avoidance-sensor-module
- 26. Electronic Hub, August 2018, http://www.electronicshub.org/ir-sensor/
- 27. Traffic Simulation with Matlab, July 2018, https:// www.elprocus.com/rfid-basic-introduction-simple-application
- 28. Arduino, September 2018. https://create.arduino. cc/projecthub/gadget-programmers/online-attendance-system-without-ethernet-c07682
- 29. Elecrow Technology, September 2018, https:// www.elecrow.com/download/HC-12.pdf
- 30. Mateen A., Khalid A., Khan L., Majeed S., and Akhtar T. Vigorous algorithms to control urban vehicle traffic. ICIS 2016:1-5.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1da38498-8d3a-4eda-9b56-3f348c1c12c7