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Risky lane-changing (LC) behavior of vehicles on the road has negative effects on traffic safety. This study presents a research framework for key feature selection and risk prediction of car’s LC behavior on the highway based on vehicles’ trajectory dataset. To the best of our knowledge, this is the first study that focuses on key feature selection and risk prediction for LC behavior on the highway. From the vehicles’ trajectory dataset, we extract car’s candidate features and apply fault tree analysis and k-Means clustering algorithm to determine the LC risk level based on the performance indicator of Crash Potential Index (CPI). Random Forest (RF) classifier is applied to select key features from car’s candidate features and predict LC risk level. This study also proposes a method to evaluate the resampling methods to resample the LC risk dataset in terms of fitness performance and prediction performance. The cars …