Conflicts between taxiing aircraft are resolved by making the aircraft with lower priority wait, slow down, or change their path. Prevalent priority assignment is based on rules such as First Come First Serve. However, this is not viable as a priority assignment done by an air-traffic controller (ATC) based on multiple factors. Thus, a machine learning approach is proposed to mimic an ATC’s priority assignment. Firstly, the potential conflict scenarios between two aircraft from historical data, which are resolved, are detected and extracted. Then a Random Forest model is developed to learn ATC’s behaviors. The model mimics ATC’s behavior with an accuracy of 89% and can thus be an effective approach for priority assignment in path-planning and conflict resolution. Further analysis indicates that features such as unimpeded time difference, distance to destination and start, and speed are major considerations that affect the ATC’s decisions. Civil Aviation Authority of Singapore (CAAS) National Research Foundation (NRF) Submitted/Accepted version This research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme.