Traffic Regulator Detection Using GPS Trajectories

Autor: Stefania Zourlidou, Monika Sester, Jens Golze
Rok vydání: 2020
Předmět:
Dewey Decimal Classification::500 | Naturwissenschaften::550 | Geowissenschaften
010504 meteorology & atmospheric sciences
Dewey Decimal Classification::900 | Geschichte und Geografie::910 | Geografie
Reisen
Computer science
Feature vector
Regulator
02 engineering and technology
GPS trajectories
computer.software_genre
01 natural sciences
Traffic signal
ddc:550
0202 electrical engineering
electronic engineering
information engineering
Earth and Planetary Sciences (miscellaneous)
Oversampling
Intersection classification
Computers in Earth Sciences
ddc:910
0105 earth and related environmental sciences
Earth-Surface Processes
business.industry
020206 networking & telecommunications
Random forest
Visualization
Traffic regulator detection
Global Positioning System
Data mining
business
Classifier (UML)
computer
Zdroj: KN-Journal of Cartography and Geographic Information 70 (2020)
ISSN: 2524-4965
2524-4957
Popis: This paper explores the idea of enriching maps with features predicted from GPS trajectories. More specifically, it proposes a method of classifying street intersections according to traffic regulators (traffic light, yield/priority-sign and right-of-way rule). Intersections are regulated locations and the observable movement of vehicles is affected by the underlying traffic rules. Movement patterns such as stop events or start-and-stop sequences are commonly observed at those locations due to traffic regulations. In this work, we test the idea of detecting traffic regulators by learning them in a supervised way from features derived from GPS trajectories. We explore and assess different settings of the feature vector being used to train a classifier that categorizes the intersections based on traffic regulators; also, we test several experimental setups. The results show that a Random Forest classifier with oversampling and Bagging booster enabled can predict the intersection regulators with 90.4% accuracy. We discuss future research directions and recommend next steps for improving the results of this research.
Databáze: OpenAIRE
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