A polygon-based approach for matching OpenStreetMap road networks with regional transit authority data. Issue 4 (2nd April 2016)

• Record Type: Journal Article
• Authors: Fan, Hongchao; Yang, Bisheng; Zipf, Alexander; Rousell, Adam
• Is Part Of: International journal of geographical information science. Volume 30:Issue 4(2016)
• Page Start: 748
• Page End: 764
• Publication Date: 2016-04-02
• Subjects: Map matching -- OpenStreetMap -- road network -- polygon similarity; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2015.1100732
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Matching road networks is an essential step for data enrichment and data quality assessment, among other processes. Conventionally, road networks from two datasets are matched using a line-based approach that checks for the similarity of properties of line segments. In this article, a polygon-based approach is proposed to match the OpenStreetMap road network with authority data. The algorithm first extracts urban blocks that are central elements of urban planning and are represented by polygons surrounded by their surrounding streets, and it then assigns road lines to edges of urban blocks by checking their topologies. In the matching process, polygons of urban blocks are matched in the first step by checking for overlapping areas. In the second step, edges of a matched urban block pair are further matched with each other. Road lines that are assigned to the same matched pair of urban block edges are then matched with each other. The computational cost is substantially reduced because the proposed approach matches polygons instead of road lines, and thus, the process of matching is accelerated. Experiments on Heidelberg and Shanghai datasets show that the proposed approach achieves good and robust matching results, with a precision higher than 96% and a F1-score better than 90%.