Heterogeneity in crash data: A cross comparison between ordered probability model and its variant using crashes at suburban type arterial

•Evaluation of the injury severity of police-reported crashes on suburban type roadways (STRs) in Ohio.•Ordered heterogeneity models are estimated to account for unobserved heterogeneity in the crash data.•Heterogeneity models show a superior fit than a fixed parameters model in crash data.•Rear-end crashes were the most frequent, and head-on crashes were the least frequent crash type. Transportation researchers have long been using the statistical analysis of traffic crash data to create a proactive awareness of traffic safety, make important decisions about the design of vehicles and highways, and develop and implement safe preventive strategies to improve safety. Despite significant progress toward maintaining and analyzing traffic crash data, researchers still encounter several challenges and methodological barriers when conducting statistical analysis. One of these challenges is dealing with the issue of unobserved heterogeneity in crash data. This study uses state-of-the-art methodologies to model the injury severity of traffic crashes that occurred on a specific road segment, namely, a suburban-type road (STR), simultaneously addressing issues related to unobserved heterogeneity in data. Multiple heterogeneity ordered probit models are evaluated against Ohio crash data from the Highway Safety Information System (HSIS). The findings reveal the heterogeneous nature of some variables, such as the nighttime indicator, and demonstrate the distinctive feature of each model to capture the effect of unobserved heterogeneity in analyzing data with such variables. Furthermore, the result helps comprehend the contextual scenarios of crashes at STRs and formulate practical plans to lower the severity of such crashes.