基于结构化码本和令牌序列解码的基于路线的地图匹配

中文摘要 AI 辅助

本研究针对城市高速公路网络上的GPS轨迹数据提出了一种高效且计算量小的基于路线的地图匹配方法。关键在于利用命名线路和命名路口的符号结构，而这在层级地图匹配中未被利用。我们将每个候选路线表示为线路和路口名称的序列，将此类序列集作为路线码本，并将地图匹配表述为探测轨迹与码本成员的计分对齐。探测通过网格量化器成为令牌序列，预计算网格将每个坐标映射到线路或路口令牌，解码器通过构建返回码本成员。码本由DAFSA×Levenshtein自动机索引，这是一种来自近似字符串匹配和语音识别的模糊查找技术，每次查询的解码成本比暴力扫描低几个数量级。我们在东京都市高速公路拓扑的变形副本上评估该方法。该方法在适度GPS噪声下能恢复准确路线，在强噪声下仍能识别线路和路口序列；灵敏度分析确定了网格分辨率的工作范围。实际探测评估、信道模型校准以及与隐马尔可夫模型的直接比较留待后续版本。

英文摘要

This study proposes an efficient and computationally light route based map matching method for GPS track data on urban expressway networks. The key idea is to exploit a symbolic structure of named lines and named junctions that link level map matching leaves unused. We represent each candidate route as a sequence of line and junction names, take the set of such sequences as a route codebook, and formulate map matching as scored alignment of a probe trajectory against members of the codebook. Probes become token sequences via a mesh quantizer, a precomputed grid mapping each coordinate to a line or junction token, and the decoder returns a member of the codebook by construction. The codebook is indexed by a DAFSA $\times$ Levenshtein automaton, a fuzzy lookup technique from approximate string matching and speech recognition; the per query decoding cost is orders of magnitude lower than a brute force scan. We evaluate the method on a deformed replica of the Tokyo Metropolitan Expressway topology. The method recovers the exact route at moderate GPS noise and continues to identify the line and junction sequence under heavy noise; a sensitivity analysis maps the mesh resolution operating range. Real probe evaluation, channel model calibration, and a head to head HMM comparison are left to a forthcoming version.

发表机构

• Institute of Science Tokyo（东京科学大学）
• The University of Tokyo（东京大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

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18 pages, 9 figures