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Paper published in KN - Kartographische Nachrichten: Traffic Regulator Detection Using GPS Trajectories

Paper published in KN - Kartographische Nachrichten: Traffic Regulator Detection Using GPS Trajectories

Jens Golze and Stefania Zourlidou developed a method to derive traffic regulators from GPS trajectories

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.

 

link.springer.com/article/10.1007/s42489-020-00048-x

Published by m sester