Trajectory analysis at intersections for traffic rule identification

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:

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

Acknowledgments

The authors gratefully acknowledge the financial support from DFG.

Data availability statement

The data that support the findings of this study are openly available in Research Data Repository of the Leibniz University Hannover at https://data.uni-hannover.de, reference number https://doi.org/10.25835/0043786.

Notes

1. physical features: final stop duration, minimum crossing speed, number of deceleration events, number of stops, distance of last stop from the intersection.

2. statistical features: minimum, maximum, mean, and variance of physical features.

Additional information

Funding

This work is supported by the German Research Foundation (Deutsche Forschungsgemeinschaft (DFG)) with grant number 227198829/GRK1931.

Notes on contributors

Chenxi Wang

Chenxi Wang has received her M.Sc. degree in Computer Engineering from Leibniz University Hannover (Germany). Her research interests are trajectory analysis and pattern recognition.

Stefania Zourlidou

Stefania Zourlidou is a doctoral candidate at the Institute of Cartography and Geoinformatics, Leibniz University Hannover (Germany). She has received her M.Sc. degree in Intelligent Systems from UCL (London, UK). Her research interests include spatiotemporal analysis of crowdsourced data and machine learning.

Jens Golze

Jens Golze is a doctoral candidate at the Institute of Cartography and Geoinformatics, Leibniz University Hannover. He has received his M.Sc. in Geodesy and Geoinformatics from Leibniz University Hannover (Germany). His research interests include trajectory and laser scanning imaging analysis.

Monika Sester

Monika Sester is a Professor and Head of the Institute of Cartography and Geoinformatics at Leibniz University Hannover. She has received her PhD from the University of Stuttgart (Institute of Photogrammetry). Her research interests include geoinformatics, map generalization, spatial data integration and interpretation, spatial analysis and modeling, and machine learning.