引用本文: 朱递, 刘瑜. 一种路网拓扑约束下的增量型地图匹配算法[J]. 武汉大学学报 ( 信息科学版), 2017, 42(1): 77-83.
ZHU Di, LIU Yu. An Incremental Map-Matching Method Based on Road Network Topology[J]. Geomatics and Information Science of Wuhan University, 2017, 42(1): 77-83.
 Citation: ZHU Di, LIU Yu. An Incremental Map-Matching Method Based on Road Network Topology[J]. Geomatics and Information Science of Wuhan University, 2017, 42(1): 77-83.

## An Incremental Map-Matching Method Based on Road Network Topology

• 摘要: 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。

Abstract: 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, this paper proposes a 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.

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