The goal of the Infrastructure Safety Assessment in a Connected Vehicle (CV) Environment project was to develop a method to identify infrastructure safety “hot spots” using CV data. Using these basic safety messages to detect hot spots may allow for quicker discovery than traditional methods, such as police-reported crashes. The basic safety message may be able to detect events that police normally cannot obtain, including unreported crashes and near-crashes. The project successfully explored some models and algorithms to detect crashes and near-crashes and also designed a methodology to apply to hot spot identification. With the data available, conclusive results were not achieved; however, the models showed some potential. Three techniques were tested to predict crashes using vehicles’ kinematic data. To predict where a crash was occurring, multivariate adaptive regression splines, classification and regression trees, and a novel pattern matching approach were all tested. The models were able to identify the majority of 13 known crashes with different amounts of false positives. The pattern matching approach outperformed a simple acceleration threshold by identifying nearly 70% of crashes in a crash- only test set and 74% of near-crashes in a near-crash only test set. On the training set, it was able to identify more crashes than the thresholds without increasing the number of false positives observed. Based on the work described in this report, the CVI-UTC is fully prepared to apply the methodology to data collected on the field test bed.