A New, Score-Based Multi-Stage Matching Approach for Road Network Conflation in Different Road Patterns

Abstract

Road-matching processes establish links between multi-sourced road lines representing the same entities in the real world. Several road-matching methods have been developed in the last three decades. The main issue related to this process is selecting the most appropriate method. This selection depends on the data and requires a pre-process (i.e., accuracy assessment). This paper presents a new matching method for roads composed of different patterns. The proposed method matches road lines incrementally (i.e., from the most similar matching to the least similar). In the experimental testing, three road networks in Istanbul, Turkey, which are composed of tree, cellular, and hybrid patterns, provided by the municipality (authority), OpenStreetMap (volunteered), TomTom (private), and Basarsoft (private) were used. The similarity scores were determined using Hausdorff distance, orientation, sinuosity, mean perpendicular distance, mean length of triangle edges, and modified degree of connectivity. While the first four stages determined certain matches with regards to the scores, the last stage determined them with a criterion for overlapping areas among the buffers of the candidates. The results were evaluated with manual matching. According to the precision, recall, and F-value, the proposed method gives satisfactory results on different types of road patterns.

A rule-based approach for generating urban footprint maps: from road network to urban footprint

“…( 7) might be used. F-measure can be used to quantify the balance between accuracy and completeness (Samal et al 2004;Song et al 2011;Akbulut et al 2018;Hacar and Gökgöz, 2019).…”

A new approach for matching road lines using efficiency rates of similarity measures

“…In other words, the geometrical properties of corresponding line objects such as orientation, length, shape, location have not equal values. According to Hacar and Gökgöz (2019b), there are three important reasons that researchers prefer to work with line matching rather than point and polygon matchings: (1) difficulties in establishing relationships between complex representations such as patterns, intersections, roundabouts, dead ends, (2) the need to keep navigation datasets up-to-date and (3) the rise of Volunteered Geographical Information (VGI) datasets.…”

“…Pourabdollah et al (2013) also conducted a conflation study with attribute-rich OS data to improve the quality of OSM road data. Besides, Hacar and Gökgöz (2019b) conducted a matching study with OSM and TomTom navigation data. In some cases, line-based (linear) approaches to matching road objects may be insufficient.…”

“…The conflation process is based on the principle of matching geometries (point, line, and polygon) that represent the same real entities (Yuan and Tao, 1999). Determining the correspondences between the objects according to their relations and similarities is called matching process (Hacar and Gökgöz, 2019b).…”

Multi-Source Geo-Information Fusion in Transition: A Summer 2019 Snapshot

“…Similarity scores for Road-matching [32]: Hausdorff distance (between vertices), orientation (octant), sinuosity, mean perpendicular distance, mean length of the edges of a triangulated irregular network (TIN), and degree of connectivity (valence of intersections), are six indices used to compute a similarity score. The sources are the Istanbul Metropolitan authority, OSM, TomTom and Basarsoft (both of the latter are private navigation data).…”