A mandatory lane change occurs when buses are ready to enter the station, which will easily cause a reduction of urban road capacity and induce traffic congestion. Using deep learning methods to make lane-changing decisions has become one of the research hotspots in the field of public transportation, especially with the development of the Cooperative Vehicle-Infrastructure System. Aiming at the exploration of the bus lane-changing rules and decisions during entering, we built a GRU neural network model considering bus priority by using the first real-world V2X (vehicle to everything) dataset. Firstly, we illustrated the image and point cloud data processing by coordinate transformation. Secondly, the Kalman filtering algorithm was used to evaluate the vehicle state. Combined with the bus priority rules, we propose a flexible right-of-way lane in front of the bus stop. And then, we obtain the feature variables as inputs to the model. The XGBoost algorithm was chosen to train the GRU model. Results show that the model has higher identification accuracy for lane-changing maneuvers by comparison with other models. It plays a very important role in providing a decision basis for more refined bus operation management.
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