在线地图匹配通过索引近似路段

Jingyu Han, Xiong Fu, Linfeng Liu, Dawei Jiang
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引用次数: 4

摘要

在一个典型的现实世界应用中,大量的跟踪点以流的方式以高速率出现,有效的在线地图匹配是一个迫切需要关注的问题。本文提出了两种映射匹配方案,积极利用可用主存实现高效匹配。首先,根据最小描述长度(MDL)原则,将路网划分为近似段,即直线段或圆弧段;其次,将近似段索引到优化的打包R树中,确保实现最小的覆盖和重叠。在此基础上,我们提出了两种在线地图匹配方案,即Top matching (TM)和kNN Refinement matching (KRM),并结合相应的缓冲策略。理论和实验表明,该算法的道路访问次数大大减少,约为3 ~ 6次的一小部分,在运行时间和匹配质量方面表现出优越的性能。
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Online map matching by indexing approximate road segments
Given a typical real-world application, a large number of tracking points come at a high rate in a stream fashion and efficient online map matching is a pressing concern. In this paper, two map matching schemes are proposed, which make aggressive use of available main memory to achieve efficient matching. First, road network is partitioned into approximate segments, namely line or arc segments, by minimum description length (MDL) principle. Second, approximate segments are indexed into an optimized packed R tree, insuring that the minimal coverage and overlap are achieved. Based on this, we propose two online map matching schemes, namely Top Matching (TM) and kNN Refinement Matching (KRM), coupled with corresponding buffering strategies. Theory and experiment show that the times of accessing roads is significantly reduced to approximately a fraction of 3 to 6, demonstrating superior performance in terms of running time and matching quality.
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