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pp. 9039-9059 | Article Number: ijese.2016.663
Published Online: October 23, 2016
Abstract
In the paper it’s proposed an algorithm for the management of traffic incidents, aimed at minimizing the impact of incidents on the road traffic in general. The proposed algorithm is based on the theory of fuzzy sets and provides identification of accidents, as well as the adoption of appropriate measures to address them as soon as possible. A criterion of algorithm’s effectiveness is the time interval from beginning of accident until its complete elimination. In this paper the main stage of development of fuzzy algorithm are considered, linguistic variables and fuzzy rules are introduced, as well as it’s reviewed an example of the work of the proposed algorithm.
Keywords: Transport system, accident, fuzzy algorithm, monitoring, situational management, fuzzy situational network, knowledge base, fuzzy inference, fuzzy linguistic variables, fuzzy rules
References
Akhmadieva, R.Sh. & Minnikhanov, R.N. (2015) Regional practice of developing road safety behavior competency in future specialists. Journal of Sustainable Development, 8(3), 242-249.
Akhmadieva, R.Sh. (2015) Competency development for safety measures on roads as a strategy for prevention of traffic accidents. Mediterranean Journal of Social Sciences, 6(2S3), 176-181.
Alkandari, A. (2013) Accident Detection and Action System Using Fuzzy Logic Theory. Proceedings of 2013 International Conference on Fuzzy Theory and Its Application. National Taiwan University of Science and Technology. Taipei, Taiwan, Dec. 6-8, 385-390.
Bartolini, C., Salle, M. (2004). Business driven prioritization of service incident. DSOM.
Binglei, X., Zheng, H. & Hongwei, M. (2008). Fuzzy-logic-based traffic incident detection algorithm for freeway. Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, 1254-1259
Bulat, P.V. & Volkov, K.N. (2016). Shock Waves Oscillations in the Interaction of Supersonic Flows with the Head of the Aircraft. International Journal of Environmental and Science Education, 11(12), 4976-4984.
Cordon, O. & Herrera, F. (1995). A General study on genetic fuzzy systems. Genetic Algorithms in Engineering and Computer Science, 2, 33-57.
Hourdos, J., Garg, V. & Michalopoulos, P. (2008). Accident Prevention Based on Automatic Detection of Accident Prone Traffic Conditions. Direct access: http://www.sciencedirect.com/science/art-icle/pii/S1877042811014078
Hu, M. & Tang, H. (2003). Development of the Real-time Evaluation and Decision Support System for Incident Management. IEEE, 5, 426-431.
ITS Decision. (2016) Direct access: http://fresno.ts.odu.edu/newitsd/ITS_Serv_Tech/incident_mana-g/detection_algorithms_report.html
Kong, Y. & Xue, A. (2006). Urban Traffic Incident Detection Based On Fuzzy Logic. IECON 2006 – 32nd Annual Conference, 772-775.
Krieger, L.S. (2012). Intelligent decision support system in the management of public transport. Management, Computer Science and Informatics, Vestnik AGTU, 2, 150-155.
Mamdani algorithm in fuzzy inference systems. (2011). Direct access: https://habrahabr.ru/post/1-13020/
Manstetten, D. & Maichle, J. (1996). Determination of traffic characteristics using fuzzy logic. Direct access: http://vnoojournal.ru/wp-content/uploads/2016/03/vns_2_p4_99-101.pdf
Mitrovich, S., Valenti, G. & Mancini, M. (2006). A decision support system (DSS) for traffic incident management in roadway tunnel infrastructure. RAIN Consortium – ENEA. Association for European Transport and contributors, 7, 165-178.
Nikolaev, A.B. & Sapego, Y.S. (2013). Methods of automation of the incident management process. Devices and systems. Management, monitoring, diagnostics, 11, 38-41.
Nikolaev, A.B. & Sapego, Y.S. (2015). Development of Traffic Accidents Control System. Automation and Control in Technical Systems, 1, 45-50. DOI: 10.12731/2306-1561-2015-1-6.
Ozbay, M. & Xiao, A. (2009). Evaluation of incident management strategies and technologies using an integrated traffic/incident management simulation. World Review of Intermodal Transportation Research, 2(3), 155-186. DOI: 10.1504/WRITR.2009.023305.
Parkany, E. (2005). A Complete Review of Incident Detection Algorithms & Their Deployment: What Works and What Doesn’t. The New England Transportation Consortium, 1, 102-112.
Shtovba, S.D. (2007). Design of fuzzy systems using MATLAB S. Мoscow: Hotline Telekom, 363 p.
Simankov, V.S. & Shopin, А.B. (2004). Situational management complex object in conditions the fuzzy initial information. Proceedings of the FOR A, 9, 116-120
Škorput, P., Mandžuka, S. & Jelušić, N. (2010) Real-time Detection of Road Traffic Incidents. Promet – Traffic&Transportation, 22(4), 273-283.
Sultangazinov, S.K., Yessengarayev, B.S., Kainarbekov, A., Nauryzova, K.S. & Shagiachmetow, a.D.R. (2016). Working Capacity of Track Structure and Failure Simulation of its Components. IEJME-Mathematics Education, 11(8), 2995-3008.
Toymentseva, I.A., Karpova, N.P., Toymentseva, A.A., Chichkina, V.D. & Efanov, A.V. (2016). Methods of the Development Strategy of Service Companies: Logistical Approach . International Journal of Environmental and Science Education, 11(14), 6820-6836.
Zadeh, L. (1976). The concept of linguistic variable and its application to decision the approximate solutions, Moscow: Mir, 362 p.