Road construction events are a necessary part of keeping road infrastructure in good condition but can pose significant safety problems when implemented. Transportation authorities in NYC seek a better understanding of the type, severity, and extent of mobility impacts associated with work zones. This project proposes using a k-means clustering approach to predict the probability of a vehicle collision occurring in the proximity of a road construction event (i.e work zone). The proposed clustering method is applied to over 20,000 construction and emergency construction events of relatively short duration in New York City to identify types of work zones that may present greater safety risks. This methodology builds upon the existing body of research by utilizing only publicly available datasets and by applying the methodology to roads and highways in the five boroughs of New York City. The results of this project are in service of enabling practitioners to employ appropriate mitigation strategies during project programming, design, and in the development of effective transportation management plans. This project is part of the Center for Urban Science and Progress capstone process with capstone sponsor HDR and a consortium of transportation authorities in the New York City Metro Area.