Отправить эту статью по почте DEVELOPING INCIDENT DETECTION ALGORITHM BASED ON THE MAMDANI FUZZY INFERENCE ALGORITHM
Аннотация
Application of fuzzy logic in the incident detection system allows making a decision under uncertainty. The phase of incident detection is a process of finding difficulties in traffic. The difficulty in traffic is the main sign that there was a road accident and requires a reaction for its elimination. This leads to the use of input data that must be relevant to the vehicles and the road. These data must be considered together, and should be compared with the corresponding values for further analysis. The main parameters of the traffic flow, which can characterize its current state, are a flow rate, a volume flow.
Necessary to analyze the input data received from the sensors. After processing the input data, using the previously entered fuzzy rules, will be taken action that will improve the situation in traffic or at least not allow it worse.
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Литература
Abdulrahman Alkandari. Accident Detection and Action System Using Fuzzy Logic Theory // Proceedings of 2013 International Conference on Fuzzy Theory and Its Application. Dec. 6–8, 2013. Taipei, Taiwan, pp. 385–390.
Deniz O., Celikoglu H.B. Overview to some existing incident detection algorithms: a comparative evaluation // Procedia – Social and Behavioral Sciences. 2011, pp. 1–13.
Iancu i. A Mamdani Type Fuzzy Logic Controller // Fuzzy Logic – Controls, Concepts, Theories and Applications. University of Craiova Romania, pp. 325–350.
Manstetten D., Maichle J. Determination of traffic characteristics using fuzzy logic. 11 pp.
Hi-ri-o-tappa K., Likitkhajorn C., Poolsawat A., Thajchayapong S. Traffic incident detection system using series of point detectors // Intelligent Transportation Systems (ITSC), 15th International IEEE Conference on. 2012, pp. 182–187.
Hourdos J., Garg V., Michalopoulos P. Accident Prevention Based on Automatic Detection of Accident Prone Traffic Conditions: Phase I. Final Report. CTS 08-12. 2008. 1–152 p.
Hyung Jin Kim, Ph.D., Hoi-Kyun Choi, Ph.D. A comparative analysis of incident service time on urban freeways // IATSS Research. Vol.25 No.1, 2001, pp. 62 – 72.
Mingwei Hu, Hao Tang. Development of the Real-time Evaluation and Decision Support System for Incident Management. IEEE. 2003, pp. 426–431.
Parkany E. A Complete Review of Incident Detection Algorithms & Their Deployment: What Works and What Doesn’t. Feb. 7, 2005. 112 p.
Rossi R., Gastaldi M. Fuzzy logic-based incident detection system using loop detectors data // Transportation Research Procedia 10 ( 2015), pр. 266–275.
Rubanov V.G., Filatov A.G., Rybin I.A. Intelligent automatic control system. Fuzzy control in technical systems. Electronic manual. URL: http://nrsu.bstu.ru/chap27.html
Sergio Mitrovich, Gaetano Valenti, Massimo Mancini. A decision support system (DSS) for traffic incident management in roadway tunnel infrasrtucture // Association for European Transport and contributors. 2006.
Viswanathan M., Lee S.H., Yang Y.K. Neuro-fuzzy Learning for Automated Incident Detection / Advances in Applied Artificial Intelligence. Volume 4031 of the series Lecture Notes in Computer Science, pp. 889–897.
Xie Binglei, Hu Zheng, Ma Hongwei. Fuzzy-logic-Bbased traffic incident detection algorithm for freeways // Proceedings of the Seventh International Conference on Machine Learning and Cybernetics. July 12–15, 2008, pp. 1254–1259.
Zadeh L.A. Fuzzy algorithms//Information and Control 12 (2). 1968, pp. 94–102.
Zadeh L.A. Fuzzy Sets // Information and control (8). 1965, pp. 338–353.
DOI: https://doi.org/10.12731/2227-930X-2017-1-18-27
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