Virginia Tech Transportation InstituteSmadi, OmarRakha, Hesham A.Arafeh, MohamadrezaAbdel-Salam, Abdel-Salam GomaaGuo, FengFlintsch, Alejandra Medina2015-07-312015-07-312010-05Rakha, H. A., Arafeh, M., Abdel-Salam, A.-S. G., Guo, F., & Flintsch, A. M. (2010). Linear regression crash prediction models : Issues and proposed solutions. (MAUTC-2009-01 VT). Blacksburg, VA: Virginia Tech Transportation Institute. Retrieved from http://ntl.bts.gov/lib/47000/47000/47020/VT-2008-02.pdf.VT-2008-02http://hdl.handle.net/10919/55103The paper develops a linear regression model approach that can be applied to crash data to predict vehicle crashes. The proposed approach involves novice data aggregation to satisfy linear regression assumptions namely error structure normality and homoscedasticity. The proposed approach is tested and validated using data from 186 access road sections in the state of Virginia. The approach is demonstrated to produce crash predictions consistent with traditional negative binomial and zero inflated negative binomial general linear models. It should be noted however that further testing of the approach on other crash datasets is required to further validate the approach.21 pagesapplication/pdfenIn CopyrightLinear regression analysisAccident dataTraffic accidentsMathematical predictionAccident risk forecastingTraffic accidentsTraffic safetyRegression analysis--mathematical modelsAccidentsHighway safetyGovernment documenthttp://ntl.bts.gov/lib/47000/47000/47020/VT-2008-02.pdf