Identyfikatory
Warianty tytułu
Kamera cyfrowa jako źródło danych koncepcji ITS dla sterowania i zarządzania ruchem drogowym
Języki publikacji
Abstrakty
The traffic adaptive-control processes and Intelligent Transportation Systems (ITS) work on traffic characteristics provided by vehicles various detectors. In majority cases the algorithms work on vehicles number evidences only, recorded on traffic lanes. The expected data concerns the vehicles number and a time schedule observed at stop-lines on intersection inlets or another points of the traffic intensity checking. A satisfactory usage of the video technology needs various simplifications of the data source structure and the processing algorithms. For simplification of these all processes several solutions must be implemented. One can try reducing the data size and improve the processing algorithms. Better results can be expected after proper selection of the data sampling intervals, namely the data granularity finding. Several conclusions concerning the traffic recording and modeling are presented in this work. The discussed technology was implemented to produce.
Adaptacyjne sterowanie procesem transportowym oraz tzw. Inteligentne Systemy Transportowe (ITS) korzystają z charakterystycznych danych zarejestrowanych za pomocą różnych detektorów ruchu. W większości przypadków algorytmy sterowania wykorzystują zapis o liczbie pojazdów na pasach ruchu. Potrzebne dane dotyczą zarówno liczby pojazdów, jak i czasów ich dojazdu do linii zatrzymania na wlocie skrzyżowania lub innego charakterystycznego punktu pomiaru natężenia ruchu. Zadowalające zastosowanie technik pomiarowych wideo uwarunkowane jest wprowadzeniem wielu uproszczeń dla rejestrowanych danych i algorytmów przetwarzania. Modelowanie strumieni pojazdów wiąże się zwykle z pewnymi stratami rejestrowanych danych. Często te dane są nadmiarowe dla uzyskania zadowalającej jakości sterowania ruchem. Dla uproszczenia wszystkich faz sterowania przedstawiono kilka ważnych rozwiązań z teorii rejestracji i przetwarzania danych. Można dokonać znaczącej redukcji rozmiaru plików danych wejściowych oraz uprościć algorytmy ich przetwarzania, nie tracąc niczego z jakości sterowania. Dobrym sposobem dla tych ograniczeń jest określenie właściwego interwału próbkowania danych pomiarowych oraz zdefiniowanie rozmiarów ziarna pomiarowego systemu sterowania. Kilka istotnych rozwiązań, dotyczących rejestracji i modelowania procesów transportowych, zaprezentowano w niniejszym artykule. Opracowania te zostały wdrożone do produkcji.
Czasopismo
Rocznik
Tom
Strony
57--70
Opis fizyczny
Bibliogr. 39 poz.
Twórcy
autor
- Silesian University of Technology, Faculty of Transport Krasińskiego st. 8, 40-019 Katowice, Poland, jan.piecha@polsl.pl
Bibliografia
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- 6. Deng L., Tang N., Lee D., Wang Ch., Lu M.: Vision Based Adaptive Traffic Signal Control System Development, Proc. of Int. Conf. Parallel and Distributed Systems, Vol. 2, 2005, pp. 634-638.
- 7. Chitturi M.V., Medina J.C., Benekohal R. F.: Effect of shadows and time of day on performance of video detection systems at signalized intersections. Transportation Research Part C 18, 2010, pp. 176–186.
- 8. Degang C., Zhang L., Zhao S., Hu Q, Zhu P.: A Novel Algorithm of Finding Reducts with Fuzzy Rough Set. Transactions on Fuzzy Systems, IEEE, Issue 99, 2011, pp. 385-389.
- 9. Gnyla P., Piecha J.: The Transportation Network Rough Description for an Adaptive Traffic Control Process by Means of Video Detection Technology. Transport Problems, IV Int. Conf., Silesian University of Technology, Katowice 2012, pp. 155-163.
- 10. Haag M., Nagel H. H.: Combination of Edge Element and Optical Flow Estimates for 3D-Model-Based Vehicle Tracking in Traffic Image Sequences. International Journal of Computer Vision Kluwer Academic Publishers, Vol. 35, No. 3, 1999, pp. 295-319.
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- 14. Pamuła W.: Object Classification Methods for Application in FPGA Based Vehicle Video Detector. Transport Problems Scientific Journal, Silesian University of Technology, Vol.4, Iss. 2, 2009, pp. 5-14.
- 15. Pamuła W.: Vehicle Detection Algorithm for FPGA Based Implementation, in Computer Recognition Systems. M. Kurzyński, M. Woźniak (eds.): Springer Verlag, Berlin, Heidelberg 2009, pp. 586-592.
- 16. Pamuła W.: Feature Extraction Using Reconfigurable Hardware. [In:] Bolc L. et al., (eds.): Computer Vision and Graphics, Lecture Notes in Computer Science 6375, Springer-Verlag, Berlin, Heidelberg 2010, pp. 158-165.
- 17. Pamuła W.: Determining Feature Points for Classification of Vehicles. [In:] Burduk et al. (eds.): Computer Recognition Systems 4. AISC 95, pp. 677-684, Springer Verlag, Berlin, Heidelberg 2011.
- 18. Pamuła W.: Wavelet-based data reduction for detection of moving objects. Machine Graphics and Vision, Institute of Computer Science, Polish Academy of Science, Vol. 20, pp. 27-40, Warsaw 2011.
- 19. Pawlak Z.: Rough sets and some problems of artificial intelligence. Published by Polish Academy of Science, Warszawa 1985 (the rights of manuscript).
- 20. Piecha J., Gnyla P.: Main principles to the road traffic intensity prediction methods. Transactions on Transport Systems Telematics and Safety, Silesian University of Technology - Academic Press, Gliwice 2011, pp. 79-90.
- 21. Piecha J., Gnyla P., Baca M.: Some traffic control proposals by means of fuzzy sets theory. Proc. of Central European Conf. on Information and Intelligent Systems - CECIIS - 2011, Zagreb, Sept. 2011.
- 22. Piecha J., Staniek M.: Syntactic Method For Vehicles Movement Description And Analysis. Journal of Information and Organizational Sciences, Zagreb University of Technology 2009, Vol. 33., pp. 327-334.
- 23. Piecha J., Staniek M.: Vehicles Trajectories Movement Description by Means of Syntactic Method. Transport Problems Scientific Journal, Silesian University of Technology 2009, Vol. 4/4, pp. 53-60.
- 24. Piecha J., Staniek M.: Formal Language for Vehicle Trajectory Description. Transactions on Transport Systems Telematics and Safety, Silesian University of Technology - Academic Press, Gliwice 2009, pp. 117-123.
- 25. Piecha J., Staniek M.: The Context-Sensitive Grammar for Vehicle Movement Description. ICCVG 2010, Part II. Lecture Notes in Computer Science, LNCS 6375, Springer-Verlag, Berlin, Heidelberg 2010, pp. 193-202.
- 26. Płaczek B.: The method of data entering into cellular traffic model for on-line simulation. Transactions on Transport Systems Telematics. [In:] J. Piecha ed., Gliwice, 2006.
- 27. Płaczek B.: Selective data collection in vehicular networks for traffic control applications. Transportation Research, Part C vol. 23, 2012, pp. 14-28.
- 28. Płaczek B.: Fuzzy cellular model for traffic data fusion. Transactions on transport systems telematics and safety, 2009, pp. 25-35.
- 29. Płaczek B.: Vehicles Recognition Using Fuzzy Descriptors of Image Segments. Advances in Soft Computing. Computer Recognition Systems 3. Springer-Verlag, Berlin, Heidelberg 2009, pp. 79-86.
- 30. Płaczek B., Staniek M.: Klasyfikator i licznik pojazdów WD-K. Moduły wideo-detektorów pojazdów ZIR-WD do sterowania i nadzoru ruchu drogowego. Praca badawcza: 512/11/475/06/FS-11. Zad. 1.2 Sprawozdanie nr 3, Katowice 2007, Research reports in polish.
- 31. Płaczek B. Staniek M.: Miernik parametrów ruchu drogowego WD-M. Praca wdrożeniowa: Moduły wideo-detektorów pojazdów ZIR-WD do sterowania i nadzoru ruchu drogowego. Praca badawcza: 512/11/475/06/FS-11. Zad. 1.3 Sprawozdanie nr 8, Katowice 2007, Research reports in polish.
- 32. Płaczek B., Staniek M.: The vehicles tracking algorithm for hardware video-detection implementation, Transactions on Transport Systems Telematics, Modeling, Management and Image Processing, Monograph edited by J. Piecha, Silesian University of Technology, Gliwice 2007, pp. 120-127.
- 33. Shakeri M., Deldari H., Rezvanian A., Foroughi H.A Novel Fuzzy Method to Traffic Light Control Based on Unidirectional Selective Cellular Automata for Urban Traffic. Proceedings of 11th International Conference on Computer and Information Technology (ICCIT 2008), Khulna, Bangladesh, December 2008, pp. 25-27.
- 34. Staniek M., Piecha J.: A parser construction for identification of traffic events in a linguistic description of road traffic flow. Transactions on Transport Systems Telematics and Safety, Silesian University of Technology - Academic Press, Gliwice, 2009, pp. 61-66.
- 35. Starzyk J.A.,Nelson D.E, Sturtz K.A.: Mathematical Foundation for Improved Reduct Generation in Information Systems. Journal of Knowledge and Information Systems, vol. 2 No. 2, 2000, pp.131-146.
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- 37. Yao J. T., Yao Y.Y.: Induction of classification rules by granular computing. Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing (TSCTC'02): 331–338, London, Springer-Verlag, UK 2002.
- 38. Yong-Gang G., Lin L.: A Fuzzy Cellular Automaton Model Based On NaSchH Model. 2nd International Conference on Signal Processing Systems (ICSPS), 2010.
- 39. Zhao Y., Kockelman K.M.: The propagation of uncertainty through travel demand models: an exploratory analysis. Annals of Regional Science, 2001.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSL9-0069-0017