Urban residents’ transportation modes play a pivotal role in shaping transportation planning and policies for sustainable cities. Mining refined transportation modes from mobile phone location (MPL) data is a key spatiotemporal big data application for sustainable city planning and traffic management. However, key challenges persist: low recognition accuracy due to insufficient consideration of travel features of transportation modes, the positioning uncertainty of MPL data, and ineffective evaluation due to lacking validation datasets. To address these limitations, we propose an analytical framework for transportation mode recognition. First, precise moving segments are constructed through road network matching and linear interpolation, resolving the positioning uncertainty issues of MPL data. Then, we propose a comprehensive feature parameter system for transportation mode recognition and construct a transportation mode recognition model based on eXtreme Gradient Boosting (XGBoost). Finally, using synchronously collected GPS data and travel logs, we validated the framework’s recognition results, demonstrating its ability to improve the accuracy of transportation mode recognition.