Review of Research on Bus Travel Trajectory Prediction Based on Deep Learning

Bus travel trajectory prediction predicts when the bus arrives at important track points on its route, such as stops and road intersections. Accurate bus arrival time prediction at road intersections and stops can improve the efficiency and service quality of urban public transport system, which is crucial for urban public transport planning and bus dispatch. From the perspective of the development of bus travel trajectory prediction methods, this paper analyzes the factors that affect bus operation, explores the types of datasets, and summarizes the data preprocessing methods. According to their development venation, bus travel trajectory prediction methods are divided into three categories：historical methods, parametric models represented by time series models, and non-parametric models including machine learning and deep learning methods. The advantages and limitations of different methods are summarized. Due to the superior performance of deep learning models in time series prediction tasks, more and more scholars begin to adopt deep learning based models to solve the problem of bus travel trajectory prediction, and consider combining the spatial and temporal features exhibited by urban roads to improve prediction accuracy further. Finally, the challenges faced in bus travel trajectory prediction field are analyzed, and future development and research directions in this field are prospected. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.