Autoregressive nonlinear time-series modeling of traffic fatalities in Europe

European Transport Research Review

Original Paper

Eleonora Papadimitriou¹  George Yannis¹  Constantinos Antoniou² 

[1] Department of Transportation Planning and Engineering, National Technical University of Athens, 5 Iroon Polytechniou Street, 157 73, Zografou, Athens, Greece;Laboratory of Transportation Engineering, National Technical University of Athens, 9 Iroon Polytechniou Street, 157 80, Zografou, Athens, Greece;

关键词: Traffic safety;    Non-linear regression;    Time series analysis;    Autoregressive models;   

DOI  :  10.1007/s12544-011-0055-4

 received in 2011-02-05, accepted in 2011-08-30,  发布年份 2011

来源: Springer

【 摘 要 】

PurposeThe objective of this paper is to provide a parsimonious model for linking motorization level with the decreasing fatality rates observed across EU countries during the last three decades.MethodsA macroscopic analysis of road-safety in Europe at the country level is proposed through the application of non-linear models correlating fatalities and vehicles for the period between 1970 and 2002. Given the time series nature of road safety data, these models result in auto-correlated residuals, thus violating at least one of the assumptions of non-linear regression. Autoregressive forms of the considered models that overcome these limitations and provide superior predictive capabilities are also considered.ResultsAn autoregressive log-transformed model seems to outperform the base autoregressive non-linear model in this respect. The use of these models allowed for the identification of the best and worst performing countries.ConclusionsThe proposed models can prove useful for assessing the road safety performance of the examined countries, as well as for obtaining some insight on the current and future trends of less developed countries.

【 授权许可】

CC BY   
© The Author(s) 2011. This article is published under license to BioMed Central Ltd. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.