Abstract
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 substantially, which could have significant implications for evaluating the effects of different safety-related policies and countermeasures.
Highlights
The outcome of a collision is polytomous in nature such as no injury (NI), minimal injury, minor injury, major injury, and fatal injury
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
For minor injuries the difference is from -49 % to 139 % between occupantbased and vehicle-based data and from -29 % to 134 % between occupant-based and collisionbased data, whereas for vehicle-based data and collision-based data this difference is from -3 % to 186 %. This shows that aggregating the data results in underestimation of the parameters estimates. This could be of grave consequences if the purpose of the analysis is to evaluate the effects of some policies through some variables, in which case precise estimation of the magnitude of the parameter for the variable is of great importance
Summary
The outcome of a collision is polytomous in nature such as no injury (NI), minimal injury, minor injury, major (incapacitating) injury, and fatal injury. Jones and Jørgensen [17] and Lenguerrand et al [20] were among the first, as identified in Usman et al [23], to recognize the need to consider the hierarchical crash-car-occupant structure of collision data for crash severity modeling. They discussed the potential issues of ignoring the clustering nature of data and the correlation within the clusters, such as erroneous estimates of model coefficients and understated standard errors and confidence intervals for the effects. The main findings are summarized, focussing on the differences from different approaches
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