一种路网拓扑约束下的增量型地图匹配算法

An Incremental Map-Matching Method Based on Road Network Topology

着眼于低频浮动车轨迹数据,对地图匹配问题进行了抽象,并分析了影响匹配结果的几何约束与拓扑约束。针对GPS采样的低频性和城市路网的复杂性,提出了一种路网拓扑约束下的增量型地图匹配算法(topology-constrained incremental matching algorithm,TIM)。选取北京市浮动车的GPS样例轨迹数据进行匹配,结果表明,该匹配算法在不同复杂程度的城市路网下均表现较好。

The emergence of big spatio-temporal data brings brand new perspectives as well as challenges for us to investigate and understand urban space.Due to existence of GPS position error,it is inevitable to adopt the map-matching methods to map the spatio-temporal trajectories onto geographic space.This research focuses on the low-sampling trajectories of floating cars in urban road networks by formalizing the map-matching process and exploring the influence of both the geometric and topology constraints on matching results.To solve the problem of matching low-sampling GPS data in the context of complex urban road networks,we proposea topology-constrained incremental matching algorithm（TIM）.Utilizing a sample GPS trajectory of Beijing float car as an example,the TIM algorithm is verified to be efficient and accurate give various road network complexity.Our study is valuable for the pre-processing of massive spatio-temporal data,and has the potential to benefit trajectory data mining and related urban informatics research in the future.