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Traffic Sensing and Assessing in Digital Transportation Systems

  • Hana Rabbouch
  • Foued Saâdaoui
  • Rafaa Mraihi
Chapter
Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)

Abstract

By integrating relevant vision technologies, based on multiview data and parsimonious models, into the transportation system’s infrastructure and in vehicles themselves, the main transportation problems can be alleviated and road safety improved along with an increase in economic productivity. This new cooperative environment integrates networking, electronic, and computing technologies, will enable safer roads, and achieve more efficient mobility and minimize the environmental impact. It is within this context of digital transportation systems that this chapter attempts to review the main concepts of intelligent road traffic management. We begin by summarizing the most best-known vehicle recording and counting devices, the major interrelated transportation problems, especially the congestion and pollution. The main physical variables governing the urban traffic and factors responsible for transportation problems as well as the common assessing methodologies are overviewed. Graphics and real-life shots are occasionally used to clearly depict the reported concepts. Then, in direct relation to the recent literature on surveillance based on computer vision and image processing, the most efficient counting techniques published over the few last years are reviewed and commented. Their few drawbacks are underlined and the prospects for improvement are briefly expressed. This chapter could be used not only as a pedagogical guide, but also as a practical reference which explains efficient implementing of traffic management systems into new smart cities.

Notes

Acknowledgements

We would like to thank the anonymous reviewers for their insightful and constructive comments that have greatly contributed to improving the chapter and to the editorial staff for their generous support and assistance during the review process.

References

  1. 1.
    Ambardekar, A., Nicolescu, M., Bebis, G., Nicolescu, M.: Vehicle classification framework: a comparative study. EURASIP J. Image Video Process. 2014(29), 1–13 (2014)Google Scholar
  2. 2.
    Anand, A., Ramadurai, G., Vanajakshi, L.: Data fusion-based traffic density estimation and prediction. J. Intell. Transp. Syst. 18(4), 367–378 (2014)CrossRefGoogle Scholar
  3. 3.
    Arnott, R.: On the optimal target curbside parking occupancy rate. Econ. Transp. 3(2), 133–144 (2014)CrossRefGoogle Scholar
  4. 4.
    Arnott, R., Kraus, M.: Congestion. In: Durlauf, S.N., Blume, L.E. (eds.) The New Palgrave Dictionary of Economics, 2nd edn. Palgrave Macmillan, London (2008)Google Scholar
  5. 5.
    Barcellos, P., Bouvié, C., Escouto, F.L., Scharcanski, J.: A novel video based system for detecting and counting vehicles at user-defined virtual loops. Expert Syst. Appl. 42(4), 1845–1856 (2014)CrossRefGoogle Scholar
  6. 6.
    Barria, J.A., Thajchayapong, S.: Detection and classification of traffic anomalies using microscopic traffic variables. IEEE Trans. Intell. Transp. Syst. 12(3), 695–704 (2011)CrossRefGoogle Scholar
  7. 7.
    Bas, E., Tekalp, M., Salman, F.S.: Automatic vehicle counting from video for traffic flow analysis. In: IEEE Symposium on Intelligent Vehicle - IV, pp. 392–397 (2007)Google Scholar
  8. 8.
    Bharadwaj, S., Ballare, S., Rohit, R., Chandel, M.K.: Impact of congestion on greenhouse gas emissions for road transport in Mumbai metropolitan region. In: Transportation Research Procedia, vol. 25, pp. 3538–3551 (2017)Google Scholar
  9. 9.
    Bouwmans, T., Porikli, F., Hörferlin, B., Vacavant, A.: Background Modeling and Foreground Detection for Video Surveillance: Traditional and Recent Approaches, Benchmarking and Evaluation. CRC Press, Taylor and Francis Group, Boca Raton (2014)CrossRefGoogle Scholar
  10. 10.
    Button, K.: Transport Economics, 3rd edn. Edward Elgar, Cheltenham (2010)Google Scholar
  11. 11.
    Cambridge Systematics, Inc., Dowling Associates, Inc., System Metrics Group, Inc., Texas Transportation Institute: NCHRP Report 618: Cost-effective performance measures for travel time delay, variation, and reliability. Transportation Research Board, Washington, DC (2008)Google Scholar
  12. 12.
    Chang, Y.S., Lee, Y.J., Choi, S.S.B.: Is there more traffic congestion in larger cities? Scaling analysis of the 101 largest U.S. urban centers. Transp. Policy 59, 54–63 (2017)Google Scholar
  13. 13.
    Chen, S., Shyu, M., Zhang, C., Strickrott, J.: A multimedia data mining framework: mining information from traffic video sequences. J. Intell. Inf. Syst. 19(1), 61–77 (2002)CrossRefGoogle Scholar
  14. 14.
    Chen, S., Zhang, J., Li, Y., Zhang, J.: A hierarchical model incorporating segmented regions and pixel descriptors for video background subtraction. IEEE Trans. Ind. Inf. 8(1), 118–127 (2012)CrossRefGoogle Scholar
  15. 15.
    Chilamkurti, N., Zeadally, S., Chaouchi, H.: Next-Generation Wireless Technologies: 4G and Beyond. Springer, London (2013)CrossRefGoogle Scholar
  16. 16.
    Cohen, S.: Ingénierie du Trafic Routier: Elément de Théorie du Trafic et Applications. Eyrolles, Paris (1993)Google Scholar
  17. 17.
    Corréïa, A.: Modélisation de conflits dans l’algèbre des dioïdes - application à la régulation de trafic dans les carrefours, Thèse de Doctorat en Automatique, Université de Technologie de Belfort-Montbéliard, Besançon, France (2007)Google Scholar
  18. 18.
    de la Rocha, E., Palacios, R.: Image-processing algorithms for detecting and counting vehicles waiting at a traffic light. J. Electron. Imaging 19(4), 043025-1–043025-8 (2010)CrossRefGoogle Scholar
  19. 19.
    Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Proceedings of the 6th European Conference on Computer Vision-Part II, pp. 751–767 (2000)Google Scholar
  20. 20.
    Faouzi, N.E., Leung, H., Kurian, A.: Data fusion in intelligent transportation systems: progress and challenges – a survey. Inf. Fusion 12(1), 4–10 (2011)CrossRefGoogle Scholar
  21. 21.
    Friedman, N., Russell, S.: Image segmentation in video sequences: a probabilistic approach. In: Proceeding UAI’97 Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, pp. 175–181 (1997)Google Scholar
  22. 22.
    Fu-min, Z., Lü-chao, L., Xin-hua, J., Hong-tu, L.: An automatic recognition approach for traffic congestion states based on traffic video. J. Highw. Transp. Res. Dev. 8(2), 72–80 (2014)Google Scholar
  23. 23.
    Hadi, R.A., Sulong, G., George, L.E.: Vehicle detection and tracking techniques: a concise review. Signal Image Process. Int. J. 5(1), 1–12 (2014)CrossRefGoogle Scholar
  24. 24.
    Hau, T.D.: Congestion pricing and road investment. In: Button, K.J., Verhoef, E.T. (eds.) Road Pricing, Traffic Congestion and the Environment, Chapter 3, pp. 39–78. Edward Elgar Publishing Limited, Cheltenham (1998)Google Scholar
  25. 25.
    He, S., Zhang, J., Cheng, Y., Wan, X., Ran, B.: Freeway multisensor data fusion approach integrating data from cell phone probes and fixed sensors. J. Sens. 2016, Article ID 7269382, 13 pp. (2016)Google Scholar
  26. 26.
    Hymel, K.: Does traffic congestion reduce employment growth? J. Urban Econ. 65(2), 127–135 (2009)CrossRefGoogle Scholar
  27. 27.
    Jang, H., Won, I.-S., Jeong, D.-S.: Automatic vehicle detection and counting algorithm. Int. J. Comput. Sci. Netw. Secur. 14(9), 99–102 (2014)Google Scholar
  28. 28.
    Javed, S., Oh, S.H., Heo, J., Jung, S.K.: Robust background subtraction via online robust PCA using image decomposition. In: International Conference on Research in Adaptive and Convergent System (ACM RACS 2014), pp. 105–110 (2014)Google Scholar
  29. 29.
    Jin, J., Rafferty, P.: Does congestion negatively affect income growth and employment growth? Empirical evidence from US metropolitan regions. Transp. Policy 55, 1–8 (2017)Google Scholar
  30. 30.
    Klein, L.A., Mills, M.K., Gibson, D.R.P.: Traffic Detector Handbook, vol. I, 3rd edn. FHWA-HRT-06-108, FHWA, Washington, DC (2006)Google Scholar
  31. 31.
    Klein, R.W., Koeser, A.K., Hauer, R.J., Hansen, G., Escobedo, F.J.: Relationship between perceived and actual occupancy rates in urban settings. Urban For. Urban Green. 19, 194–201 (2016)CrossRefGoogle Scholar
  32. 32.
    Kolm, S.-Ch.: La théorie générale de l’encombrement [The General Theory of Congestion]. SEDEIS, Paris (1968)Google Scholar
  33. 33.
    Lagorio, A., Grosso, E., Tistarelli, M.: Automatic detection of adverse weather conditions in traffic scenes. In: IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, pp. 273–279 (2008)Google Scholar
  34. 34.
    Laroche, F.: Économie politique des infrastructures ferroviaires. Thèse de Doctorat en Sciences économiques, Université Lumière Lyon 2, France (2014)Google Scholar
  35. 35.
    Levinson, H.S., Pratt, R.H., Bay, P.N., Douglas, G.B.: Quantifying Congestion, vol. 1, National Cooperative Highway Research Program (NCHRP): report 398. Transportation Research Board National Research Council, Washington (1997)Google Scholar
  36. 36.
    Lu, M., Sun, C., Zheng, S.: Congestion and pollution consequences of driving-to-school trips: a case study in Beijing. Transp. Res. Part D Transp. Environ. 50, 280–291 (2017)CrossRefGoogle Scholar
  37. 37.
    McCall, J.C., Trivedi, M.M.: Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE Trans. Intell. Transp. Syst. 7(1), 20–37 (2006)CrossRefGoogle Scholar
  38. 38.
    Mimbela, L.: A summary of vehicle detection and surveillance technologies used in intelligent transportation systems. Federal Highway Administration, Washington, DC (2007)Google Scholar
  39. 39.
    Minge, E.: Evaluation of non-intrusive technologies for traffic detection, Research Project, Minnesota Department of Transportation - Office of Traffic, Safety and Technology, Roseville, MN (2010)Google Scholar
  40. 40.
    Mohana, H.S., Ashwathakumar, M., Shivakumar, G.: Vehicle detection and counting by using real time traffic flux through differential technique and performance evaluation. In: International Conference on Advanced Computer Control, ICACC ’09, pp. 791–795 (2008)Google Scholar
  41. 41.
    Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 713–724 (2003)CrossRefGoogle Scholar
  42. 42.
    Narhe, M.C., Nagmode, M.S.: Vehicle counting using video image processing. Int. J. Comput. Technol. 1(7), 358–362 (2014)Google Scholar
  43. 43.
    Oliver, N.M., Rosario, B., Pentland, A.: A Bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831–843 (2000)CrossRefGoogle Scholar
  44. 44.
    Preethi, P., Ashalatha, R., Modelling saturation flow rate and right turn adjustment factor using area occupancy concept. Case Stud. Transp. Policy 6(1), 63–71 (2018)CrossRefGoogle Scholar
  45. 45.
    Rabbouch, H., Saâdaoui, F., Mraihi, R.: Unsupervised video summarization using cluster analysis for automatic vehicles counting and recognizing. Neurocomputing 260, 157–173 (2017)CrossRefGoogle Scholar
  46. 46.
    Raghtate, G., Tiwari, A.K.: Moving object counting in video signals. Int. J. Eng. Res. Gen. Sci. 2(3), 415–420 (2014)Google Scholar
  47. 47.
    Rahane, S.K., Saharkar, U.R.: Traffic congestion – causes and solutions: a study of Talegaon Dabhade city. J. Inf. Knowl. Res. Civ. Eng. 3(1), 160–163 (2014)Google Scholar
  48. 48.
    Réveillac, J.-M.: Modeling and Simulation of Logistics Flows 1: Theory and Fundamentals. Wiley, Hoboken (2017)zbMATHGoogle Scholar
  49. 49.
    Robitaille, M., Nguyen, T.: Évaluation de la congestion “De la théorie à la pratique” Réseau routier de l’agglomération de Montréal, congrès annuel de 2003 de l’Association des transports du Canada à St. John’s (2003)Google Scholar
  50. 50.
    Roess, R.P., Prassas, E.S., McShane, W.R.: Traffic Engineering, 3rd edn. Prentice Hall, Upper Saddle River (2004)Google Scholar
  51. 51.
    Shan, Z., Xia, Y., Hou, P., He, J.: Fusing incomplete multisensor heterogeneous data to estimate urban traffic. IEEE MultiMedia 23(3), 56–63 (2016)CrossRefGoogle Scholar
  52. 52.
    Shi, K., Di, B., Zhang, K., Feng, C., Svirchev, L.: Detrended cross-correlation analysis of urban traffic congestion and NO2 concentrations in Chengdu. Transp. Res. Part D Transp. Environ. 61, 165–173 (2018)CrossRefGoogle Scholar
  53. 53.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real time tracking. In: Computer Society Conference on Computer Vision and Pattern Recognition, Ft. Collins, vol. 2, pp. 246–252 (1999)Google Scholar
  54. 54.
    Texas A&M Transportation Institute (TTI): https://tti.tamu.edu/
  55. 55.
    Treiber, M., Kesting, A., Wilson, R.E.: Reconstructing the traffic state by fusion of heterogeneous data. Comput. Aided Civ. Infrastruct. Eng. 26(6), 408–419 (2011)CrossRefGoogle Scholar
  56. 56.
    Vickrey, W.: Congestion theory and transport investment. Am. Econ. Rev. 59(2), 251–262 (1969)Google Scholar
  57. 57.
    Walton, C.M., Persad, K., Wang, Z., Svicarovich, K., Conway, A., Zhang, G.: Arterial Intelligent Transportation Systems – Infrastructure Elements and Traveler Information Requirements, Center for Transportation Research The University of Texas at Austin 3208 Red River Austin, TX 78705 (2009)Google Scholar
  58. 58.
    Wang, K., Yao, Y.: Video-based vehicle detection approach with data-driven adaptive neuro-fuzzy networks. Int. J. Pattern Recognit. Artif. Intell. 29(7), 1–32 (2015)MathSciNetGoogle Scholar
  59. 59.
    Wardrop, J.G.: Some theoretical aspects of road traffic research. Proc. Inst. Civ. Eng. Part II 1(36), 352–362 (1952)Google Scholar
  60. 60.
    Weibin, Z., Yong, Q., Zhuping, Z., Antonio, B.S., Minglei, S., Yinhai, W.: A method of speed data fusion based on Bayesian combination algorithm and Markov model. Transportation Research Board 97th Annual Meeting, Washington, DC (2018)Google Scholar
  61. 61.
    Wu, K., Chen, Y., Ma, J., Bai, S., Tang, X.: Traffic and emissions impact of congestion charging in the central Beijing urban area: a simulation analysis. Transp. Res. Part D Transp. Environ. 51, 203–215 (2017)CrossRefGoogle Scholar
  62. 62.
    Xia, Y., Shi, X., Song, G., Geng, Q., Liu, Y.: Towards improving quality of video-based vehicle counting method for traffic flow estimation. Signal Process. 120, 672–681 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hana Rabbouch
    • 1
  • Foued Saâdaoui
    • 2
  • Rafaa Mraihi
    • 3
  1. 1.Université de Tunis, Institut Supérieur de Gestion de Tunis, Cité BouchouchaTunisTunisia
  2. 2.University of Monastir, Laboratoire d’Algèbre, Théorie de Nombres et Analyse Non-linéaire, Faculté des SciencesMonastirTunisia
  3. 3.Université de Manouba, Ecole Supérieure de Commerce de Tunis, Campus Universitaire de La ManoubaTunisTunisia

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