This article focuses on identifying the appropriate ordered response structure for modeling pedestrian injury severity. The alternative ordered response approaches considered for the empirical analysis include ordered logit model (OL), generalized ordered logit model (GOL), and latent segmentation based ordered logit model (LSOL). The GOL and LSOL models enhance the traditional OL model in different ways. The GOL model relaxes the restrictive thresholds in the OL model by allowing for individual-level exogenous variable impacts on the threshold parameters. On the other hand, the LSOL model allows for differential impact on the alternatives by segmenting the pedestrian crash population into various segments with segment-specific OL parameters. In this study, the authors focus on examining the performance of these two model structures relative to the traditional OL model in the context of pedestrian injury severity. The performance of the formulated injury severity models are tested based on the New York City (NYC) Pedestrian Research Data Base for the years 2002 through 2006. To the authors’ knowledge, the study provides a first of its kind comparison exercise among OL, GOL, and LSOL models for examining pedestrian injury severity. The model estimation results clearly highlight the presence of segmentation based on the crash location attributes of pedestrian accidents. The crash location attributes that affect the allocation of pedestrians into these segments include regional county, functional classification of roadway, pedestrian location on roadway, number of travel lanes, and number of parking lanes in the roadway system. The key factors influencing pedestrian injury severity are weather condition, lighting condition, vehicle types, pedestrian age, and season. Overall, the results of the empirical analysis provide credence to the hypothesis that LSOL model is a promising ordered framework to accommodate population heterogeneity in the context of pedestrian injury severity.