License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GISCIENCE.2018.11
URN: urn:nbn:de:0030-drops-93397
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9339/
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Méneroux, Yann ; Kanasugi, Hiroshi ; Saint Pierre, Guillaume ; Le Guilcher, Arnaud ; Mustière, Sébastien ; Shibasaki, Ryosuke ; Kato, Yugo

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

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LIPIcs-GISCIENCE-2018-11.pdf (0.7 MB)

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.

BibTeX - Entry

@InProceedings{mneroux_et_al:LIPIcs:2018:9339,
  author =	{Yann M{\'e}neroux and Hiroshi Kanasugi and Guillaume Saint Pierre and Arnaud Le Guilcher and S{\'e}bastien Musti{\`e}re and Ryosuke Shibasaki and Yugo Kato},
  title =	{{Detection and Localization of Traffic Signals with GPS Floating Car Data and Random Forest}},
  booktitle =	{10th International Conference on Geographic Information  Science (GIScience 2018)},
  pages =	{11:1--11:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Stephan Winter and Amy Griffin and Monika Sester},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9339},
  URN =		{urn:nbn:de:0030-drops-93397},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.11},
  annote =	{Keywords: Map Inference, Machine Learning, GPS Traces, Traffic Signal}
}

Keywords: Map Inference, Machine Learning, GPS Traces, Traffic Signal
Collection: 10th International Conference on Geographic Information Science (GIScience 2018)
Issue Date: 2018
Date of publication: 02.08.2018

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