01702416

Component

https://doi.org/10.1177/0361198119842828

This study investigates factors that significantly contribute to the severity of pedestrian injuries resulting from pedestrian-vehicle crashes. Multinomial logit (MNL) models, mixed logit (ML) models, and ordered logit/probit models have been widely used in modeling crash injury severity, including pedestrian injury severity in pedestrian-vehicle crashes. However, both MNL and ML models treat injury severity levels as non-ordered, ignoring the inherent hierarchical nature of crash injury severities, and the data used in ordered logit models need to be strictly subjected to the proportional odds (PO) assumption. In this study, a partial proportional odds (PPO) logit model approach is employed to explore the issues of pedestrian safety associated with each age group: young (aged under 24), middle-aged (aged 25–55), and older pedestrians (aged over 55). Data used in this study are police-reported pedestrian crash data collected from 2007 to 2014 in North Carolina. A variety of motorist, pedestrian, environmental, and roadway characteristics are inspected. Results from likelihood ratio tests statistically show the better performance of developing separate injury severity models for each age group compared with estimating a single model utilizing all data. Relevant parameter estimates and associated marginal effects are used to interpret the results, followed by recommendations made in the concluding section.

19-02385

English

pp 731-746

2019-5

Transportation Research Record: Journal of the Transportation Research Board

Digital/other

References (46) ; Tables (5)

Age groups; Injury severity; Logits; Pedestrian safety; Pedestrian vehicle crashes; Traffic safety

North Carolina

Highways; Pedestrians and Bicyclists; Safety and Human Factors

TRIS, TRB, ATRI

Mar 20 2019 2:52PM