Multilevel Discrete Outcome Modeling for Crash Severity: A Novel Approach for Crash Severity Models

Abstract

Combining a total crash count model and a crash severity probability model simultaneously to predict crashes by severity is challenging because crash count models are implemented at an aggregate scale, that is, by sites (segments or intersections), while crash severity probability models are at a disaggregate scale, that is, by crashes or persons (drivers or occupants). This study develops a multilevel discrete outcome modeling framework to estimate crash counts by severity for California, U.S., rural four-lane divided highways by estimating and combining three models: a univariate count model for total crashes at the site level predicts total crash counts; an ordered logit discrete outcome crash severity prediction model at the crash-level predicts proportion of crashes by severity; and a proportional regression connection model that predicts proportion of groups of drivers and vehicles to connect the other two models. The combination of all three models provides crash counts by severity at the site level considering both aggregate and disaggregate factors. The prediction from the multilevel discrete outcome model is compared with predictions made by traditional univariate count models by severity for accuracy and transferability using estimation and validation data. Based on the estimation data, the multilevel discrete outcome model predicts injury and fatal crashes more accurately than the univariate count model. For validation data, the multilevel model has slightly better prediction accuracy for minor and possible injuries. Finally, the calibration factor indicates the multilevel discrete outcome model is more transferable than the univariate count model.