A novel method for the detection of road intersections and traffic rules using big floating car data. Issue 8 (17th September 2021)

• Record Type: Journal Article
• Authors: Hu, Rong; Xu, Yong; Chen, Hanlin; Zou, Fumin
• Is Part Of: IET intelligent transport systems. Volume 16:Issue 8(2022)
• Page Start: 983
• Page End: 997
• Publication Date: 2021-09-17
• Subjects: floating car data -- intelligent traffic system -- road intersection -- road network -- traffic rules; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/itr2.12116
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract: The information of road intersection and traffic rules plays an essential role in transportation system especially for people travelling to large, unfamiliar cites. Moreover, cities are growing fast and it is difficult to keep up with the changing frequency of road network information although road map is periodically updated to provide reliable information. In order to tackle this problem, an automatic method to detect road intersection and traffic rules using a floating car trajectory dataset is proposed. The turning trajectories using a newly proposed algorithm employing flexible spatial, temporal, and logical constraint rules were first extracted. Then a Two‐trajectory Intersect Angle‐based (TIA) method was proposed to locate the candidate points at road intersection. Finally the intersections (junctions) were detected using a cluster‐based technique. Furthermore, turning rules at road intersections were extracted automatically by clustering the angles both before and after making a turn. The effectiveness of our method was demonstrated using big floating car trajectory dataset collected in Fuzhou, China. Additional comparisons and analysis were also conducted to confirm identification results. Our findings showed that a total 333 at‐grade intersections were detected automatically with more than 94% in accuracy, and turning rules identification reached 85% in accuracy by manually comparing the results.