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Injury severity analysis： comparison of multilevel logistic regression models and effects of collision data aggregation

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Journal of Modern Transportation 2016年第1期24卷 73-87页

作者： taimur usman Liping Fu Luis F.Miranda-Moreno Department of Civil & Environmental Engineering University of Waterloo Waterloo ON N2L 3GI Canada School of Transportation and Logistics Southwest Jiaotong University Chengdu 610031 China Department of Civil Engineering & Applied Mechanics McGill University Montreal QC H3A 2K6 Canada

This paper describes an empirical study aiming at identifying the main differences between different logistic regression models and collision data aggregation methods that are commonly applied in road safety literature for modeling collision severity. In particular, the research compares three popular multilevel logistic models （i.e., sequential binary logit models, ordered logit models, and multinomial logit models） as well as three data aggregation methods （i.e., occupant based, vehicle based, and collision based）. Six years of collision data （2001-2006） from 31 highway routes from across the province of Ontario, Canada were used for this analysis. It was found that a multilevel multinomial logit model has the best fit to the data than the other two models while the results obtained from occupant-based data are more reliable than those from vehicle- and collision-based data. More importantly, while generally consistent in terms of factors that were found to be significant between different models and data aggregation methods, the effect size of each factor differ sub- stantially, which could have significant implications forevaluating the effects of different safety-related policies and countermeasures.

关键词： Injury severity - Multilevel logistic regressionmodels Collision data aggregation

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