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Detection and Localization of Traffic Signals with GPS Floating Car Data and Random Forest

Authors Yann Méneroux, Hiroshi Kanasugi, Guillaume Saint Pierre, Arnaud Le Guilcher, Sébastien Mustière, Ryosuke Shibasaki, Yugo Kato



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Author Details

Yann Méneroux
  • Univ. Paris-Est, LASTIG COGIT, IGN, ENSG, Saint-Mandé, France
Hiroshi Kanasugi
  • CSIS, Institute of Industrial Sciences, The University of Tokyo, Japan
Guillaume Saint Pierre
  • Centre for Studies and Expertise on Risks, Mobility, Land Planning and the Environment (Cerema), Toulouse, France
Arnaud Le Guilcher
  • Univ. Paris-Est, LASTIG COGIT, IGN, ENSG, Saint-Mandé, France
Sébastien Mustière
  • Univ. Paris-Est, LASTIG COGIT, IGN, ENSG, Saint-Mandé, France
Ryosuke Shibasaki
  • CSIS, Institute of Industrial Sciences, The University of Tokyo, Japan
Yugo Kato
  • Transport Consulting Division, NAVITIME JAPAN Co., Ltd

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Yann Méneroux, Hiroshi Kanasugi, Guillaume Saint Pierre, Arnaud Le Guilcher, Sébastien Mustière, Ryosuke Shibasaki, and Yugo Kato. Detection and Localization of Traffic Signals with GPS Floating Car Data and Random Forest. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 11:1-11:15, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.11

Abstract

As Floating Car Data are becoming increasingly available, in recent years many research works focused on leveraging them to infer road map geometry, topology and attributes. In this paper, we present an algorithm, relying on supervised learning to detect and localize traffic signals based on the spatial distribution of vehicle stop points. Our main contribution is to provide a single framework to address both problems. The proposed method has been experimented with a one-month dataset of real-world GPS traces, collected on the road network of Mitaka (Japan). The results show that this method provides accurate results in terms of localization and performs advantageously compared to the OpenStreetMap database in exhaustivity. Among many potential applications, the output predictions may be used as a prior map and/or combined with other sources of data to guide autonomous vehicles.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Machine learning
  • Information systems → Global positioning systems
  • Information systems → Data mining
Keywords
  • Map Inference
  • Machine Learning
  • GPS Traces
  • Traffic Signal

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References

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