Abstract: With the technological development of intelligent transportation system and the surging spatio-temporal data in urban,the demand for public services is increasingly emphasized.As a vital part of urban transportation,public transportation also faces enormous challenges,and the spatio-temporal prediction task in transportation network is the core of the solutions for various traffic problems.Mobility pattern in traffic can reflect the travel behaviors of people and their rules.In most studies on traffic prediction task,the importance of mobility pattern is neglected.In view of the problem of existing work,a multi-pattern traffic prediction framework,MPGNNFormer,is proposed,in which based-graph neural network deep clustering method is used to extract mobility patterns of stations,and a Transformer-based spatio-temporal prediction model is designed to learn temporal dependence and spatial dependence of stations and to improve the computational efficiency.Then,a series of experiments are conducted on real bus dataset for evaluation and testing,including analysis of mobility patterns and comparison of prediction results.Finally,experimental results prove the efficacy of proposed method in the short and long-term traffic prediction of traffic networks,and its sca-lability is discussed.

Key words: Spatio-Temporal data mining, Short and long-term traffic prediction, Mobility pattern, Deep learning