TY - JOUR

T1 - Traffic Regulator Detection Using GPS Trajectories

AU - Golze, Jens

AU - Zourlidou, Stefania

AU - Sester, Monika

N1 - Funding information: Open Access funding provided by Projekt DEAL. This research was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft (DFG)) with grant number 227198829/GRK1931. The authors gratefully acknowledge this financial support.

PY - 2020/9

Y1 - 2020/9

N2 - 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.

AB - 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.

KW - GPS trajectories

KW - Intersection classification

KW - Random forest

KW - Traffic regulator detection

UR - http://www.scopus.com/inward/record.url?scp=85088643305&partnerID=8YFLogxK

U2 - 10.1007/s42489-020-00048-x

DO - 10.1007/s42489-020-00048-x

M3 - Article

AN - SCOPUS:85088643305

VL - 70

SP - 95

EP - 105

JO - KN - Journal of Cartography and Geographic Information

JF - KN - Journal of Cartography and Geographic Information

SN - 2524-4957

IS - 3

ER -