高级地图匹配算法:研究现状和趋势

Advanced Map Matching Algorithms:A Survey and Trends

地图匹配是许多位置服务与轨迹挖掘应用的基础.随着定位技术和位置服务应用的发展,地图匹配研究不断演进,从早期基于高采样率GPS(Global Position System)的实时匹配,到近期基于低采样率GPS轨迹的离线匹配、再到当前非GPS定位数据或高精度地图匹配。迄今已有许多地图匹配算法相继提出,但鲜有研究对这些算法进行全面总结.为此,对近十年提出的地图匹配算法进行调研,归纳出地图匹配算法的统一框架及常用时空特征.从模型或实现技术角度分类发现:现有算法大都采用HMM(Hidden Markov Model)模型,其次是最大权重模型;深度学习技术近期开始用于地图匹配,将是未来高精度地图匹配研究的趋势.

Map matching is a necessary procedure for many trajectory data mining and various location-based applications.Map matching algorithms are continuously evolving with the development of positioning techniques and application requirements.Research on map matching has undergone several stages,from real-time GPS data map matching,to low-sampling rate GPS trajectories offline map matching,to recently non-GPS positioning data or high resolution map matching.Various advanced map matching algorithms have been proposed.However,there is a short of a complete review of recent map matching algorithms.To bridge this gap,this paper conducts a comprehensive survey on map matching algorithms proposed in the last decade.A general framework of map matching algorithms is extracted,and spatial or spatial-temporal features commonly used in these algorithms are summarized.From the technical perspective,the HMM is the most commonlyused model in existing algorithms,before the maximum weights model.The deep learning technique has been recently applied into map matching,and is becoming a future trend for high resolution map matching.