Route Based Map Matching via a Structured Codebook and Token Sequence Decoding
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.