Traffic Sensing and Assessing in Digital Transportation Systems

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


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.



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.


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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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