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This paper presents a study in investigating the impacts of contributing factors to large track crash severity outcomes, with particular interest in examining the role of heterogeneity and potential sources of heterogeneity. The study focused on large truck vehicle-in-motion crashes on state highways occurred between 2007 and 2016 throughout the state of Florida. Random-parameter ordered logit (RPOL) models were developed to identify random parameters and explore interaction effects. Results showed that physical roadway characteristics such as roadway alignment and shoulder type showed significant random impacts on crash severity. In order to detect potential heterogeneity sources, various driver attributes were incorporated into the model structure as interaction variables, including demographic characteristics, drive condition, driver action, distraction, and vision obstruction, etc. Only few of them showed statistical significance. Accordingly, vision obstruction due to fixed objects or fog were significant environmental conditions on straight roadway alignments. In view of driver actions, running red light and following too closely were among the most hazardous driver actions on straight alignment and unpaved shoulders. Interestingly, the presence of parked-stopped vehicle that obstructed driver’s vision tended to decrease crash severity in presence of unpaved shoulders. Model results show that incorporating heterogeneity and interaction effects significantly improved the goodness-of-fit of the model. results of this study provide better understanding of the contributing factors to large truck crash severity, and lead to the development of more …