Title: Modelling trends in road crash frequency in Qatar State

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

Addresses: Qatar Transportation and Traffic Safety Center, Qatar University, Doha, Qatar ' Qatar Transportation and Traffic Safety Center, Qatar University, Doha, Qatar; Department of Mechanical and Industrial Engineering, Qatar University, P.O. Box 2713, Doha, Qatar ' Qatar Transportation and Traffic Safety Center, Qatar University, Doha, Qatar ' Qatar Transportation and Traffic Safety Center, Qatar University, Doha, Qatar; Department of Mechanical and Industrial Engineering, Qatar University, P.O. Box 2713, Doha, Qatar

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.

Keywords: ARMA; multicollinearity; road crash modelling.

DOI: 10.1504/IJOR.2019.099106

International Journal of Operational Research, 2019 Vol.34 No.4, pp.507 - 523

Received: 02 Jun 2015
Accepted: 30 Apr 2016

Published online: 10 Apr 2019 *

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