基于海量公交轨迹数据挖掘的地图匹配算法  

Map matching algorithm based on massive bus trajectory data mining

作　　者：陈辉 蒋圭峰 姜桂圆 武继刚[1] CHEN Hui;JIANG Guifeng;JIANG Guiyuan;WU Jigang(School of Computers,Guangdong University of Technology,Guangzhou Guangdong 510006,China;School of Computer Science and Engineering,Nanyang Technological University,Singapore 639798,Singapore)

机构地区：[1]广东工业大学计算机学院,广州510006 [2]南洋理工大学计算机科学与工程学院,新加坡新加坡639798

出　　处：《计算机应用》2018年第7期1923-1928,共6页journal of Computer Applications

基　　金：广东省省级科技计划资助项目(2017A040402009)~~

摘　　要：针对现有地图匹配算法(如基于经典隐马尔可夫及其变体、先进算法等)对于低频轨迹数据匹配效果不甚理想的问题,提出一种基于海量公交历史轨迹数据的轨迹数据挖掘方法。首先,以公交站点为序列骨架,从大量低频轨迹中挖掘、提取轨迹点数据,进行重组、排序形成高质量高频轨迹数据序列;然后,将高质量高频轨迹数据序列应用基于经典隐马尔可夫模型地图匹配算法,得到公交路线地图匹配结果。与未经过挖掘算法处理的低频轨迹数据的匹配方法相比,所提方法在匹配误差上平均下降6.3%,匹配所需的数据规模、时间大幅缩减;且该方法对于低频、不稳定的噪声数据具有鲁棒性,适用于所有公交路线的地图匹配问题。Concerning poor matching effect of existing map matching algorithms （such as classical Hidden Markov and its variants, advanced algorithms） for low-frequency trajectory data, a trajectory data mining method based on massive bus historical trajectory data was proposed. Taking bus stations as the sequence skeleton firstly, by mining, extracting, regrouping and sorting trajectory data from a large number of low frequency trajectory points to form high frequency trajectory data, then the high-frequency trajectory data sequence was processed by the map matching algorithm based on classical hidden Markov model to get the bus route map matching results. Compared with the matching method on the low-frequency data not processed by the mining algorithm, the proposed method reduces the matching error by an average of 6.3%, requires smaller data size and costs less time. In addition, this method is robust to low-frequency, unstable noise trajectory data, and it is suitable for map matching of all bus routes.

关 键 词：公交轨迹数据 地图匹配 数据驱动 高频轨迹数据挖掘 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]