دانلود مقاله ISI انگلیسی شماره 25906
عنوان فارسی مقاله

# تجزیه و تحلیل حساسیت مدل پیش بینی تصادف به روش فاکتوریل جزئی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
25906 2007 6 صفحه PDF سفارش دهید 4394 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Sensitivity analysis of an accident prediction model by the fractional factorial method
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Accident Analysis & Prevention, Volume 39, Issue 1, January 2007, Pages 63–68

کلمات کلیدی
کلمات کلیدی انگلیسی
Accident prediction model, Number of accidents, Road safety, Sensitivity analysis, Factorial design method,
پیش نمایش مقاله

#### چکیده انگلیسی

Sensitivity analysis of a model can help us determine relative effects of model parameters on model results. In this study, the sensitivity of the accident prediction model proposed by Zegeer et al. [Zegeer, C.V., Reinfurt, D., Hummer, J., Herf, L., Hunter, W., 1987. Safety Effect of Cross-section Design for Two-lane Roads, vols. 1–2. Report FHWA-RD-87/008 and 009 Federal Highway Administration, Department of Transportation, USA] to its parameters was investigated by the fractional factorial analysis method. The reason for selecting this particular model is that it incorporates both traffic and road geometry parameters besides terrain characteristics. The evaluation of sensitivity analysis indicated that average daily traffic (ADT), lane width (W), width of paved shoulder (PA), median (H) and their interactions (i.e., ADT–W, ADT–PA and ADT–H) have significant effects on number of accidents. Based on the absolute value of parameter effects at the three- and two-standard deviation thresholds ADT was found to be of primary importance, while the remaining identified parameters seemed to be of secondary importance. This agrees with the fact that ADT is among the most effective parameters to determine road geometry and therefore, it is directly related to number of accidents. Overall, the fractional factorial method was found to be an efficient tool to examine the relative importance of the selected accident prediction model parameters.