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A Framework for Modelling Crash Likelihood Information Under Rainy Weather Conditions

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Advances in Human Aspects of Transportation (AHFE 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 786))

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Abstract

This paper suggests a framework that collects real-time public big data and reproduces them useful information such as crash likelihood to provide to drivers for their awareness of hazardous road under rainy weather conditions. A binary logistic regression model is applied to estimate driving environmental impact on freeway crashes. The driving environments are categorized as weather condition, traffic condition and road geometry. Eleven factors from the environment information are chosen for independent variables of the logistic regression analysis. The model proves that speed, cumulative precipitation and road geometry characteristics are correlated to the crash likelihood. Drivers would use the crash likelihood information for avoiding hazardous road, and vehicle navigation systems would add the information in their routing problem.

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Acknowledgements

This work was supported by the National Research Foundation (NRF) grant funded by the Korea Government (MSIP) (NRF-2017R1A2B4008984).

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Authors and Affiliations

  1. Yeungnam University, Gyeungsan, 38541, Korea

    Younshik Chung & Seonjung Kim

  2. Korea Transport Institute, Sejong, 30147, Korea

    Seunghoon Cheon

Authors
  1. Younshik Chung
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  2. Seonjung Kim
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  3. Seunghoon Cheon
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Corresponding author

Correspondence to Younshik Chung .

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Editors and Affiliations

  1. Faculty of Engineering and the Environment, Transportation Research Group, University of Southampton, Southampton, UK

    Neville Stanton

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Chung, Y., Kim, S., Cheon, S. (2019). A Framework for Modelling Crash Likelihood Information Under Rainy Weather Conditions. In: Stanton, N. (eds) Advances in Human Aspects of Transportation. AHFE 2018. Advances in Intelligent Systems and Computing, vol 786. Springer, Cham. https://doi.org/10.1007/978-3-319-93885-1_76

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  • DOI: https://doi.org/10.1007/978-3-319-93885-1_76

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93884-4

  • Online ISBN: 978-3-319-93885-1

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