Abstract: Aiming at the problems of bus travel time prediction,such as sparse data,lack of data and long update interval,this paper proposed a Kalman filter algorithm based on similar section segmentation and fusion of multi-line information.In this method,the attribute features and spatial structure features of each road segment are normalized,the similar road segments are dynamically divided by using the similarity between the attribute features and the spatial structure and the change of the traffic impact of the POI.Then,the data information of multiple bus lines on similar road segments and target road segments are integrated,and the experimental data are enriched by using the data from similar road segments.Finally,combining the dynamic and real-time characteristics of Kalman filtering algorithm,the model is established to realize short-term prediction and correct the information.In the experiment,162 lines and 299 lines in Shenyang City were selected as experimental lines,and a similar section was taken for basic data collection and experiments.The information on the similar road sections is used to infer sparse information or missing road sections,thereby shortening the data update interval and improving the real-time performance and accuracy of the algorithm prediction.Especially in the early peak period,the absolute average percentage error of the proposed model reaches 13.2%,which can effectively meet the performance requirements of real-time query.

Key words: Kalman filter, Multi-line information, Similar road, Travel time, Travel time prediction