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原文传递 Linear Regression Crash Prediction Models: Issues and Proposed Solutions.

题名：	Linear Regression Crash Prediction Models: Issues and Proposed Solutions.
作者：	H. Rakha; M. Arafeh; A. G. Abdel-Salam; F. Guo; A. M. Flintsch;
关键词：	motor vehicle accidents,linear regression;predictions, software engineering, traffic control, roads, virginia, data processing, statistical analysis, calibration, tables(data);
摘要：	The 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 satisify 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. / Title Note: Final rept. / Supplementary Notes: Sponsored by Virginia Dept. of Transportation, Richmond., Department of Transportation, Washington, DC. Research & Innovative Technology Administration (RITA)., Federal Highway Administration, Washington, DC. and Virginia Transportation Research Council, Charlottesville. / Availability Note: Product reproduced from digital image. Order this product from NTIS by: phone at 1-800-553-NTIS (U.S. customers); (703)605-6000 (other countries); fax at (703)605-6900; and email at orders@ntis.gov. NTIS is located at 5301 Shawnee Road, Alexandria, VA, 22312, USA. / NTIS Prices: PC A03/MF A03 / NTIS In-house Control Codes: dotfha;12111,1101
报告类型：	科技报告