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Understanding Driving Behavior: Measurement, Modeling and Analysis

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Advanced Intelligent Systems for Sustainable Development (AI2SD’2018) (AI2SD 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 915))

Abstract

Human factors contribute in the manifestation of 95% of all accidents; recently there has been a research emphasis on driving behavior established as an outcome of individual actions as well as psychophysical values. This paper pursues the guidelines of systematic literature reviews to present an unbiased survey of the existing research on driving behavior in line with the psychophysical state as well as the behavioral operations of the driver and to develop unconventional taxonomies based upon the nature of the conducted study, measurement patterns and supervision motives underlying the detection and prediction models of driving behavior. A discussion on each classification is provided with a focus on the dominant mechanisms thought to be involved. The proposed overview gives insights into the scope of the problem and paves the way for grasping the major contributions and shortcomings in the state-of-the-art research.

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Acknowledgements

This research received funding from the Moroccan Ministry of Equipment, Transport and Logistics and was supported by the Moroccan National Center for Scientific and Technical Research (CNRST).

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Authors and Affiliations

  1. LISI Laboratory, Cadi Ayyad University, Marrakesh, Morocco

    Zouhair Elamrani Abou Elassad & Hajar Mousannif

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  1. Zouhair Elamrani Abou Elassad
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  2. Hajar Mousannif
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Correspondence to Zouhair Elamrani Abou Elassad .

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Editors and Affiliations

  1. Faculty of Sciences and Techniques, Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

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Elamrani Abou Elassad, Z., Mousannif, H. (2019). Understanding Driving Behavior: Measurement, Modeling and Analysis. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-11928-7_41

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