Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 75-84 |
| Seitenumfang | 10 |
| Fachzeitschrift | Geo-Spatial Information Science |
| Jahrgang | 24 |
| Ausgabenummer | 1 |
| Publikationsstatus | Veröffentlicht - 1 Dez. 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.).
ASJC Scopus Sachgebiete
- Sozialwissenschaften (insg.)
- Geografie, Planung und Entwicklung
- Erdkunde und Planetologie (insg.)
- Computer in den Geowissenschaften
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in: Geo-Spatial Information Science, Jahrgang 24, Nr. 1, 01.12.2020, S. 75-84.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Trajectory analysis at intersections for traffic rule identification
AU - Wang, Chenxi
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.
PY - 2020/12/1
Y1 - 2020/12/1
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.).
AB - 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.).
KW - clustering
KW - GPS trajectories
KW - intersection classification
KW - similarity measures
KW - speed-profiles
KW - traffic regulators
KW - Traffic rules
UR - http://www.scopus.com/inward/record.url?scp=85097014424&partnerID=8YFLogxK
U2 - 10.1080/10095020.2020.1843374
DO - 10.1080/10095020.2020.1843374
M3 - Article
AN - SCOPUS:85097014424
VL - 24
SP - 75
EP - 84
JO - Geo-Spatial Information Science
JF - Geo-Spatial Information Science
SN - 1009-5020
IS - 1
ER -