عرض بسيط للتسجيلة

المؤلفAbdella, Galal M.
المؤلفAl-Khalifa, Khalifa N.
المؤلفTayseer, Maha A.
المؤلفHamouda, Abdel Magid S.
تاريخ الإتاحة2020-07-09T21:13:34Z
تاريخ النشر2019
اسم المنشورInternational Journal of Operational Research
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1504/IJOR.2019.099106
معرّف المصادر الموحدhttp://hdl.handle.net/10576/15208
الملخصThe 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.
اللغةen
الناشرInderscience Enterprises Ltd.
الموضوعARMA
Multicollinearity
Road crash modelling
العنوانModelling trends in road crash frequency in Qatar State
النوعArticle
الصفحات507-523
رقم العدد4
رقم المجلد34
dc.accessType Abstract Only


الملفات في هذه التسجيلة

الملفاتالحجمالصيغةالعرض

لا توجد ملفات لها صلة بهذه التسجيلة.

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة