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Understanding the underlying relationship between pedestrian injury severity outcomes and factors leading to more severe injuries is very important in addressing the problem of pedestrian safety. This research combines data mining and statistical regression methods to identify the main factors associated with the levels of pedestrian injury severity outcomes. This work relies on the analysis of two unique pedestrian injury severity datasets from New York City, US (2002–2006) and the City of Montreal, Canada (2003–2006). General injury severity models were estimated for each dataset and for sub-populations obtained through clustering analysis. This paper shows how the segmentation of the accident datasets helps to better understand the complex relationship between the injury severity outcomes and the contribution of geometric, built environment and socio-demographic factors. While using the same …