Drivers route choice model is essential in transportation software such as navigation,fleet management and simulation,where the random utility models(RUM)have dominated for years.We investigate in this paper whether other statistics methods,such as the statistical learning(also known as the machine learning,ML)models could be applied into this field,and whether these approaches outperform the traditional models in model fit and prediction.The application framework and data structure are proposed where the challenging problems are:(1)to pool data from multiple origin-destination pairs; and(2)to interpret results for behaviour analysis.All models are applied in a real network.Results suggest that the random forest has satisfying performances with acceptable computation time,making it suitable for large network and real-time analysis.This paper shows that the ML methods can be adopted for behaviour analysis,such as to prioritise the importance of variables,compute the elasticity,and forecast for scenarios.Future directions on combining the RUM and ML methods are discussed.