Generating urban road intersection models from low-frequency GPS trajectory data. Issue 12 (2nd December 2018)

• Record Type: Journal Article
• Authors: Deng, Min; Huang, Jincai; Zhang, Yunfei; Liu, Huimin; Tang, Luliang; Tang, Jianbo; Yang, Xuexi
• Is Part Of: International journal of geographical information science. Volume 32:Issue 12(2018)
• Page Start: 2337
• Page End: 2361
• Publication Date: 2018-12-02
• Subjects: Low-frequency trajectory data -- road intersections -- spatial clustering -- principle curve fitting -- traffic rules inference; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2018.1510124
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Detailed real-time road data are an important prerequisite for navigation and intelligent transportation systems. As accident-prone areas, road intersections play a critical role in route guidance and traffic management. Ubiquitous trajectory data have led to a recent surge in road map reconstruction. However, it is still challenging to automatically generate detailed structural models for road intersections, especially from low-frequency trajectory data. We propose a novel three-step approach to extract the structural and semantic information of road intersections from low-frequency trajectories. The spatial coverage of road intersections is first detected based on hotspot analysis and triangulation-based point clustering. Next, an improved hierarchical trajectory clustering algorithm is designed to adaptively extract the turning modes and traffic rules of road intersections. Finally, structural models are generated via K-segment fitting and common subsequence merging. Experimental results demonstrate that the proposed method can efficiently handle low-frequency, unstable trajectory data and accurately extract the structural and semantic features of road intersections. Therefore, the proposed method provides a promising solution for enriching and updating routable road data.