Анализ алгоритмов обнаружения дорожно-транспортных инцидентов на скоростных автомагистралях, использующих стационарные детекторы транспорта

Алгоритмы обнаружения инцидентов с точки зрения автоматизации можно разделить на две категории: автоматического и неавтоматического обнаружения инцидентов. Автоматические алгоритмы относятся к тем алгоритмам, которые автоматически определяют инцидент на основании данных о состоянии транспортного потока, полученных от детекторов транспорта. Неавтоматические алгоритмы или процедуры основаны на сообщениях свидетелей-людей. По функциональным признакам алгоритмы обнаружения инцидентов на алгоритмы для автомагистралей и алгоритмы для уличной сети. По методам получения данных алгоритмы обнаружения инцидентов делятся на три группы: алгоритмы, использующие данные от стационарных детекторов транспорта (индуктивные петли, радары, видеокамеры и т.д.); алгоритмы, использующие мобильные датчики (Bluetooth, wi-fi, RFID, GPS, Глонасс-датчики, транспондеры системы оплаты проезда и т.д.). алгоритмы, использующие информацию от водителей (GSM-связь, навигационные сервисы, интернет-приложения и др.). В настоящей статье рассмотрены алгоритмы, использующие данные от стационарных детекторов транспорта. К недостаткам алгоритмов обнаружения инцидентов, использующих стационарные детекторы транспорта, следует в отнести: необходимость установки и эксплуатации детекторов транспорта (индуктивных, видео и т.д.) приводит к помехам для транспортного потока и иногда к временному закрытию движения; место установки детекторов транспорта, частота их установки и количество являются критически важными с точки зрения обнаружения инцидента на том или ином участке магистрали. Однако крайне трудоемко и капиталоемко установить стационарные детекторы по всей длине магистрали. Также индуктивные детекторы транспорта, которые в основном используются для определения параметров транспортных потоков на автомагистралях, являются ненадежными и часто выходят из строя, что делает не эффективным обнаружение инцидентов на том или ином участке дороги. К достоинствам рассматриваемых алгоритмов следует отнести подтвержденная на протяжении десятилетий надежность и точность в определении инцидентов, что является их несомненным преимуществом по сравнению с алгоритмами, использующими мобильные датчики или информацию от водителей.

дорожно-транспортный инцидент, алгоритмы обнаружения, детектирование параметров транспортного потока, управление движением, скоростные автомагистрали

1. Abdulhai, B. and Ritchie, S.G. (1999). “ Enhancing the universality and transferability of freeway incident detection using a Bayesian-based neural network.” Transportation Research Part C. Vol. 7, No. 5, pp. 261-280

2. Adeli, H. and Samant, A. (2000). “ An adaptive conjugate gradient neural network-wavelet model for traffic incident detection.” Computer-Aided Civil and Infrastructure Engineering. Vol. 15, No.4, pp. 251.260

3. Ahmed, M.S. and Cook, A.R. (1977). “Analysis of freeway traffic time-series data using Box Jenkins techniques.” Transportation Research Record, No. 722, TRB, National Research Council, pp. 1-9.

4. Ahmed, M.S. and Cook, A.R. (1980). “Time series models for freeway incident detection.” Journal of Transportation Engineering, Vol. 106, No. 6, ASCE, pp. 731-745.

5. Ahmed, M.S. and Cook, A.R. (1982). “Application of time-series analysis techniques to freeway incident detection.” Transportation Research Board, No. 841, TRB, National Research Council, pp. 19-21.

6. Al-Deek, H.M., Ishak, S.S. and Khan, A.A. (1996). “Impact of freeway geometric and incident characteristics on incident detection.” Journal of Transportation Engineering. Vol. 122, No. 6, ASCE, pp. 440-446.

7. Balke, K.N. (1993). “An evaluation of existing incident detection algorithms.” Research Report, FHWA/TX-93/123220, Texas Transportation Institute, the Texas A&M University System, College Station, TX, November 1993.

8. Black, J. and Sreedevi, I. (2001). “ Automatic incident detection algorithms.” ITS DecisionDatabase in PATH, http://www.path.berkeley.edu/~leap/TTM/Incident_Manage/Detection/aida.html, February 2001.

9. Chang, E.C.-P. and Wang, S.-H. (1994). “Improved freeway incident detection using fuzzy set theory.” Transportation Research Record, No. 1453, TRB, National Research Council, pp. 75-82

10. Chen, S., Wang, W., van Zuylen, H., 2009. Construct support vector machine ensemble to detect traffic incident. Expert Systems with Applications 36, рр. 10976-10986.

11. Cheu, R.L. and Ritchie, S.G. (1995). “Automated detection of lane-blocking freeway incidents using artificial neural networks.” Transportation Research Part C. Vol. 3, No. 6, pp. 371-388

12. CLIPS Reference Manual, Version 5.1 of CLIPS, Vol. I. Software Technology Branch, Lyndon B. Johnson Space Center, Sept. 1991.

13. CLIPS User's Guide, Version 5.1 of CLIPS, Vol. II. Software Technology Branch, Lyndon B. Johnson Space Center, Sept. 1991.

14. Collins, J.F., Hopkins, C.M. and Martin, J.A. (1979). “ Automatic incident detection ‒ TRRL algorithms HIOCC and PATREG.” TRRL Supplementary Report, No. 526, Crowthorne, Berkshire, U.K.

15. Dia, H. and Rose, G. (1997). “Development and evaluation of neural network freeway incident detection models using field data.” Transportation Research Part C. Vol. 5, No. 5, pp. 313-331

16. Dudek, C.L., Messer, C.J. and Nuckles, N.B. (1974). “Incident detection on urban freeway.” Transportation Research Record, No. 495, TRB, National Research Council, pp. 12-24

17. Fide Tutorial. Aptronix, 1992.

18. Fide Reference Manual. Aptronix, 1992.

19. Jian Lu, Shuyan Chen, Wei Wang, Henk van Zuylen (2011). A hybrid model of partial least squares and neural network for traffic incident detection, School of Transportation, Southeast University, Nanjing 210096, China Civil Engineering and Geosciences, Delft University of Technology, 2600 GA Delft, The Netherlands.

20. Hsiao, C.-H., Lin, C.-T. and Cassidy, M. (1994). “Application of fuzzy logic and neural networks to automatically detect freeway traffic incidents.” Journal of Transportation Engineering. Vol. 120, No. 5, ASCE, pp. 753-772

21. Ishak, S.S. and Al-Deek, H.M. (1998). “Fuzzy ART neural network model for automated detection of freeway incidents.” Transportation Research Record, No. 1634, TRB, National ResearchCouncil, pp. 56-63.

22. Levin, M. and Krause, G.M. (1978). “Incident detection: a Bayesian approach.” Transportation Research Record, No. 682, TRB, National Research Council, pp. 52-58.

23. Lin, C.K. and Chang, G.L. (1998). “Development of a fuzzy-expert system for incident detection and classification.” Mathematical and Computer Modeling. Vol. 27, No. 9-11, pp. 9-25.

24. Masters, P.H., Lam, J.K. and Wong, K. (1991). “Incident detection algorithms of COMPASS ‒ an advanced traffic management system.” Proceedings of Vehicle Navigation and InformationSystems Conference, Part 1, SAE, Warrendale, PA, October 1991, pp. 295-310.

25. Marijke, F. A., & Thomas, P. H. (2004). Evolving transfer functions for artificial neural networks. Neural Computation and Application, 13, 38-46.

26. Michalopoulos, P.G. (1991). “Vehicle detection video through image processing: the Autoscope system.” IEEE Transactions on Vehicular Technology. Vol. 40, No. 1, IEEE, pp. 21-29.

27. Michalopoulos, P.G., Jacobson, R.D., Anderson, C.A. and DeBruycker, T.B. (1993). “Automatic incident detection through video image processing.” Traffic Engineering and Control. Vol. 34, No. 2, pp. 66-75.

28. Payne, H.J. and Tignor, S.C. (1978). “Freeway incident-detection algorithms based on decision trees with states.” Transportation Research Record. No. 682, TRB, National Research Council, pp. 30-37.

29. Ritchie, S.G. and Cheu, R.L. (1993). “Simulation of freeway incident detection using artificial neural networks.” Transportation Research Part C. Vol. 1, No. 3, pp. 203-217.

30. Stephanedes, Y.J., Chassiakos, A.P. and Michalopoulos, P.G. (1992). “Comparative performance evaluation of incident detection algorithms.” Transportation Research Record, No. 1360, TRB, National Research Council, pp. 50-57.

31. Stephanedes, Y.J. and Liu, X. (1995). “Artificial neural networks for freeway incident detection.” Transportation Research Record, No. 1494, TRB, National Research Council, pp. 91-97.

32. Subramaniam, S. (1991). “Literature review of incident detection algorithms to initiative diversion strategies.” Working Paper, University Center of Transportation Research, Virginia Polytechnic Institute and State University, Blacksburg, VA.

33. Tsai, J. and Case, E.R. (1979). “Development of freeway incident detection algorithms by using patternrecognition techniques.” Transportation Research Record, No. 722, TRB, National Research Council, pp. 113-116.

34. Weil, R., Wootton, J. and Garcia-Ortiz, A. (1998). “Traffic incident detection: sensors and algorithms.” Mathematical and Computer Modeling. Vol. 27, No. 9-11, pp. 257-291.

35. Николаев А.Б., Ягудаев Г.Г., Сапего Ю.С., Еремин С.В., Кулаков А.В. Анализ алгоритмов управления инцидентами в интеллектуальных транспортных системах // Интернет-журнал «НАУКОВЕДЕНИЕ» Том 9, №4 (2017) http://naukovedenie.ru/PDF/16TVN417.pdf (доступ свободный). Загл. с экрана. Яз. рус., англ.

36. Bottino, A., Garbo, A., Loiacono, C. and Quer, S. (2016). Street viewer: An autonomous vision based traffic tracking system. Sensors, 16(6). doi: 10.3390/s16060813

37. Chassiakos, A. and Stephanedes, Y. (1993a). Smoothing algorithms for incident detection. Transportation Research Record, no. 1394, pp. 8-16.

38. Chung, E. and Rosalion, N. (1999). [Electronic resource] Effective incident detection and management on freeways. Technical Report ARRB Transport Research Ltd. Access mode: https://trid.trb.org/view.aspx?id=1164576.

39. CTC & Associates LLC (2012). Automated video incident detection systems: Preliminary investigation. Technical Report Caltrans Division of Research and Innovation. [Electronic resource] Access mode: http://www.dot.ca.gov/newtech/researchreports/preliminary_investigations/docs/automated_incident_pi.pdf.

40. Dia, H. and Rose, G. (1997). Development and evaluation of neural network freeway incident detection models using field data. Transportation Research, 5C(5), 313-331.

41. Chintalacheruvu, N. and Muthukumar, V. (2012). Video based vehicle detection and its application in intelligent transportation systems. Journal of Transportation Technologies, 2. doi: 10.4236/jtts.2012.24033.

42. Fishbain, B., Ideses, I., Mahalel, D. and Yaroslavsky, L. (2009). Real-time vision-based traffic flow measurements and incident detection. In Real-Time Image and Video Processing 2009, no. 7244 in Proc. SPIE, pages 72440I. doi: 10.1117/12.812976

43. Kastrinaki, V., Zervakis, M. and Kalaitzakis, K. (2003a). A survey of video processing techniques for traffic applications. Image and Vision Computing, 21(4), 359-381. doi: 10.1016/S0262-8856(03)00004-0.

44. Luk, J., Han, C. and Chin, D. (2010). [Electronic resource] Automatic freeway incident detection: Review of practices and guidance. In 24th ARRB conference : building on 50 years of road and transport research : proceedings. Access mode: http://railknowledgebank.com/Presto/content/GetDoc.axd?ctID=MjE1ZTI4YzctZjc1YS00MzQ4LTkyY2UtMDJmNTgxYjg2ZDA5& rID=NTY=&pID=MTQ3Ng==&attchmnt=VHJ1ZQ==&uSesDM=False&rIdx=NzgyNA==&rCFU=.

45. Martin, P. T., Perrin, J. and Hansen, B. (2001). [Electronic resource] Incident detection algorithm evaluation. Technical Report Utah Deportment of Transportation. Access mode: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.569.5951&rep=rep1&type=pdf.

46. Loureiro, P.F.Q., Rossetti, R.J.F. and Braga, R.A.M. (2009). Video processing techniques for traffic information acquisition using uncontrolled video streams. In ITSC ’09. 12th International IEEE Conference on Intelligent Transportation Systems . doi: 10.1109/ITSC.2009.5309595

47. Netten, B., Weekley, J., Miles, A., Nitsche, P. and Baan, J. et al. (2013b). [Electronic resource] RAIDER – realising advanced incident detection on european roads: Generic specifications for incident detection systems. Technical Report ERA-NET ROAD. Access mode: http://www.cedr.eu/download/other_public_files/research_programme/eranet_road/call_2011/mobility/raider/02_raider-d4_1_genericspecificationsforincidentdetectionsystems_v5.pdf

48. Li, Q., fu Shao, C. and Zhao, Y. (2014). A robust system for real-time pedestrian detection and tracking. J. Cent. South Univ., 21, 1643-1653. doi: 10.1007/s11771-014-2106-1

49. Mehboob, F., Abbas, M. and Jiang, R. (2016). Traffic event detection from road surveillance vide os based on fuzzy logic. In 2016 SAI Computing Conference (SAI), pp. 188-194. doi: 10.1109/SAI.2016.7555981

50. Nathanail, E., Kouros, P. and Kopelias, P. (2017). Traffic volume responsive incident detection. Transportation Research Procedia, 25(Supplement C), 1755-1768. World Conference on Transport Research -WCTR 2016 Shanghai. 10-15 July 2016. doi: 10.1016/j.trpro.2017.05.136

51. Porikli, F. and A. Yilmaz. Object detection and tracking, in Video Analytics for Business Intelligence. 2012, Springer.

52. Ren, J. (2016). Detecting and positioning of traffic incidents via video-based analysis of traffic states in a road segment. IET Intelligent Transport Systems, 10, 428-437(9).

53. Ritchie, S. and Cheu, R. (1993). Simulation of freeway incident detection using artificial neural networks. Transportation Research, 1C(3), 203-217.

54. Shukla, A. and M. Saini. “Moving Object Tracking of Vehicle Detection”: A Concise Review. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2015.

55. Shahade, A.K. and G.Y. Patil. Efficient Background Subtraction and Shadow Removal Technique for Multiple Human object Tracking. International Journal, 2013.

56. Vasu, L. Effective Step to Real-time Implementation of Accident Detection System Using Image Processing. 2010.

57. Vinay, D. and N.L. Kumar. Object Tracking Using Background Subtraction Algorithm. International Journal of Engineering Research and General Science, 2015.

58. Wan, Y., Huang, Y. and Buckles, B. (2014). Camera calibration and vehicle tracking: Highway traffic video analytics. Transportation Research Part C: Emerging Technologies, 44, 202-213. doi: 10.1016/j.trc.2014.02.018

59. Zhang, W., Q.J. Wu, and H. Bing Yin. Moving vehicles detection based on adaptive motion histogram. Digital Signal Processing, 2010.

60. Капский, Д.В. Основы автоматизации интеллектуальных транспортных систем : Учебник / Д.В. Капский, Е.Н. Кот, С.В. Богданович [и др.]. – Вологда : Общество с ограниченной ответственностью "Издательство "Инфра-Инженерия", 2022. – 412 с.

61. Скирковский, С.В. Теоретические и практические подходы к созданию и развитию интеллектуальной транспортной системы города / С.В. Скирковский, Д.В. Капский, Д.В. Навой ; МТиК Респ. Бел.; УО «БелГУТ». – Гомель : УО «БелГУТ», 2022. – 171 с.