多线路信息融合的公交车行程时间预测算法

doi:10.11896/jsjkx.180901764

摘要/Abstract

摘要： 针对公交车行程时间预测存在数据稀疏、数据缺失及更新间隔长等问题,提出了一种基于相似路段划分并融合多线路信息的卡尔曼滤波算法。该算法对每条路段的属性特征和空间结构特征进行归一化处理,利用属性特征和空间结构的相似性及POI(Point of Interest)对交通影响的变化动态地划分相似路段;然后融合相似路段与目标路段上的多条公交线路的数据信息,用相似路段的数据丰富实验数据;最后结合卡尔曼滤波算法动态性高、实时性强等特点建立模型,从而实现短时预测,并对信息进行修正。选取沈阳市162线路和299线路作为实验线路,各划取一段相似路段进行基础数据采集并进行实验。通过相似路段上的信息来推断数据稀疏或缺失路段的信息,能够缩短数据更新间隔并提高算法预测的实时性及精准性,尤其在早高峰时段,提出的算法模型的绝对平均百分误差达到13.2%,能达到实时查询的性能需求。

关键词: 多线路信息, 卡尔曼滤波, 相似路段, 行程时间, 行程时间预测

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

中图分类号:

TP39

马林宏, 陈廷伟, 郝明, 张雷. 多线路信息融合的公交车行程时间预测算法[J]. 计算机科学, 2019, 46(11): 222-227. https://doi.org/10.11896/jsjkx.180901764

MA Lin-hong, CHEN Ting-wei, HAO Ming, ZHANG Lei. Bus Travel Time Prediction Algorithm Based on Multi-line Information Fusion[J]. Computer Science, 2019, 46(11): 222-227. https://doi.org/10.11896/jsjkx.180901764

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