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Trajectory analysis at intersections for traffic rule identification

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Original languageEnglish
Pages (from-to)75-84
Number of pages10
JournalGeo-Spatial Information Science
Volume24
Issue number1
Publication statusPublished - 1 Dec 2020

Abstract

In this paper, we focus on trajectories at intersections regulated by various regulation types such as traffic lights, priority/yield signs, and right-of-way rules. We test some methods to detect and recognize movement patterns from GPS trajectories, in terms of their geometrical and spatio-temporal components. In particular, we first find out the main paths that vehicles follow at such locations. We then investigate the way that vehicles follow these geometric paths (how do they move along them). For these scopes, machine learning methods are used and the performance of some known methods for trajectory similarity measurement (DTW, Hausdorff, and Fréchet distance) and clustering (Affinity propagation and Agglomerative clustering) are compared based on clustering accuracy. Afterward, the movement behavior observed at six different intersections is analyzed by identifying certain movement patterns in the speed- and time-profiles of trajectories. We show that depending on the regulation type, different movement patterns are observed at intersections. This finding can be useful for intersection categorization according to traffic regulations. The practicality of automatically identifying traffic rules from GPS tracks is the enrichment of modern maps with additional navigation-related information (traffic signs, traffic lights, etc.).

Keywords

    clustering, GPS trajectories, intersection classification, similarity measures, speed-profiles, traffic regulators, Traffic rules

ASJC Scopus subject areas

Cite this

Trajectory analysis at intersections for traffic rule identification. / Wang, Chenxi; Zourlidou, Stefania; Golze, Jens et al.
In: Geo-Spatial Information Science, Vol. 24, No. 1, 01.12.2020, p. 75-84.

Research output: Contribution to journalArticleResearchpeer review

Wang, C, Zourlidou, S, Golze, J & Sester, M 2020, 'Trajectory analysis at intersections for traffic rule identification', Geo-Spatial Information Science, vol. 24, no. 1, pp. 75-84. https://doi.org/10.1080/10095020.2020.1843374
Wang C, Zourlidou S, Golze J, Sester M. Trajectory analysis at intersections for traffic rule identification. Geo-Spatial Information Science. 2020 Dec 1;24(1):75-84. doi: 10.1080/10095020.2020.1843374
Wang, Chenxi ; Zourlidou, Stefania ; Golze, Jens et al. / Trajectory analysis at intersections for traffic rule identification. In: Geo-Spatial Information Science. 2020 ; Vol. 24, No. 1. pp. 75-84.
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AU - Zourlidou, Stefania

AU - Golze, Jens

AU - Sester, Monika

N1 - Funding Information: This work is supported by the German Research Foundation (Deutsche Forschungsgemeinschaft (DFG)) with grant number 227198829/GRK1931. The authors gratefully acknowledge the financial support from DFG. The authors gratefully acknowledge the financial support from DFG.

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N2 - In this paper, we focus on trajectories at intersections regulated by various regulation types such as traffic lights, priority/yield signs, and right-of-way rules. We test some methods to detect and recognize movement patterns from GPS trajectories, in terms of their geometrical and spatio-temporal components. In particular, we first find out the main paths that vehicles follow at such locations. We then investigate the way that vehicles follow these geometric paths (how do they move along them). For these scopes, machine learning methods are used and the performance of some known methods for trajectory similarity measurement (DTW, Hausdorff, and Fréchet distance) and clustering (Affinity propagation and Agglomerative clustering) are compared based on clustering accuracy. Afterward, the movement behavior observed at six different intersections is analyzed by identifying certain movement patterns in the speed- and time-profiles of trajectories. We show that depending on the regulation type, different movement patterns are observed at intersections. This finding can be useful for intersection categorization according to traffic regulations. The practicality of automatically identifying traffic rules from GPS tracks is the enrichment of modern maps with additional navigation-related information (traffic signs, traffic lights, etc.).

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