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This paper presents the results of a research effort in recognizing fatality patterns in large truck-involved pedestrian/bike crashes in the state of Florida. Using 2007–2016 crash data, machine learning models, including random forests and decision tree were applied. Model results showed that fatality patterns and contributing factors tended to be different by traffic volume and road type classification. For local roads with an AADT higher than 38,000, proximity to intersection and vehicle speed were found as decisive parameters in increasing crash injury severity. On high volume roads not classified as local, crashes that involved young/middle-aged truck drivers happened on divided roadways with a speed limit of 50–75 mph, and during the midday period had a high probability of a fatal outcome. For low volume roads, under clear weather conditions, a combination of median barrier and curb shoulder close to a …