Toward Fast and Accurate Map-to-Map Matching of City Street Maps

Abstract

Frequently, various sources of geographic street-related data are covering the same space. Many geospatial traffic services require interoperability of the different datasets, which can be achieved by road network matching. A prominent use case is map conflation. More recently, the authors have suggested approaches to dynamic location referencing between maps (GIMME) and to automatic relocation of link related data in updated street maps within a framework called Map2Map. In this paper, an update on the recent progress of GIMME and Map2Map is given. Methodologically, path contraction is used to obtain a simplified version of the digital road network. This pre-processed version then augments the original network, and serves as a guide for finding routes covering the entire network, and facilitating the inter-map matching process. Path contraction also helps to reduce the complexity of the core inter-map matching method GIMME without loss in matching quality. On the conceptual side, a general strategy called calibration-preserving pre- or post-processing (C-3PO) is introduced. The aforementioned path contraction and two more post-processing methods used in Map2Map give examples for an implementation of C-3PO. Experimental results demonstrate the effectiveness of the presented approach.