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Modelling trends in road crash frequency in Qatar State

Author

Listed:
  • Galal M. Abdella
  • Khalifa N. Al-Khalifa
  • Maha A. Tayseer
  • Abdel Magid S. Hamouda

Abstract

The data-based regression models are widely popular in modelling the relationship between the crash frequencies and contributing factors. However, one common problem usually associated with the classical regression models is the multicollinearity, which leads to biased estimation of the model coefficients. This paper mainly focuses on the consequences of multicollinearity and introduces a multiple objective-based best-subset approach for promoting the accuracy of the road crash model in Qatar State. The prediction performance of the methodology is verified through a comparative study with two of well-known time series models, namely autoregressive moving average (ARMA) and double exponential smoothing (DES). The mean absolute percentage error (MAPE) is used to assess the ability of each model in maintaining minimum prediction errors. The methodology is illustrated by using a data set of road crashes in Qatar State, 2007-2013.

Suggested Citation

  • Galal M. Abdella & Khalifa N. Al-Khalifa & Maha A. Tayseer & Abdel Magid S. Hamouda, 2019. "Modelling trends in road crash frequency in Qatar State," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 34(4), pages 507-523.
    • Handle: RePEc:ids:ijores:v:34:y:2019:i:4:p:507-523
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    Cited by:

    1. Murat Kucukvar & Khalel Ahmed Alawi & Galal M. Abdella & Muhammet Enis Bulak & Nuri C. Onat & Melih Bulu & Murat Yalçıntaş, 2021. "A frontier‐based managerial approach for relative sustainability performance assessment of the world's airports," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(1), pages 89-107, January.
    2. Muhammet Enis Bulak, 2024. "A Frontier Approach to Eco-Efficiency Assessment in the World’s Busiest Sea Ports," Sustainability, MDPI, vol. 16(3), pages 1-22, January.

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