Document Open Access Logo

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

PDF
Thumbnail PDF

File

LIPIcs.GISCIENCE.2018.11.pdf
  • Filesize: 0.7 MB
  • 15 pages

Document Identifiers

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

Cite AsGet BibTex

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

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Cindie Andrieu, Guillaume Saint Pierre, and Xavier Bressaud. Estimation of space-speed profiles: A functional approach using smoothing splines. In Intelligent Vehicles Symposium (IV), 2013 IEEE, pages 982-987. IEEE, 2013. Google Scholar
  2. Gustavo E. A. P. A. Batista, Ronaldo C. Prati, and Maria Carolina Monard. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. Newsl., 6(1):20-29, 2004. Google Scholar
  3. James Biagioni and Jakob Eriksson. Inferring road maps from global positioning system traces: Survey and comparative evaluation. Transportation Research Record: Journal of the Transportation Research Board, 2291:61-71, 2012. URL: http://dx.doi.org/10.3141/2291-08.
  4. Olivier Bonin. Modèle d'erreurs dans une base de données géographiques et grandes déviations pour des sommes pondérées ; application à l'estimation d'erreurs sur un temps de parcours. Thèse de doctorat, spécialité mathématiques - statistique, Université Paris VI - Pierre et Marie Curie, mar 2002. Google Scholar
  5. Kevin W. Bowyer, Nitesh V. Chawla, Lawrence O. Hall, and W. Philip Kegelmeyer. SMOTE: synthetic minority over-sampling technique. CoRR, abs/1106.1813, 2011. Google Scholar
  6. Leo Breiman. Random forests. Machine learning, 45(1):5-32, 2001. Google Scholar
  7. Yihua Chen and John Krumm. Probabilistic modeling of traffic lanes from gps traces. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 81-88, November 2010. URL: http://dx.doi.org/10.1145/1869790.1869805.
  8. A Criminisi, J Shotton, and E Konukoglu. Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning. Microsoft Research Cambridge, Tech. Rep. MSRTR-2011-114, 5(6):12, 2011. Google Scholar
  9. Mohamed el Habib Boukhobza and Malika Mimi. Classification automatique de la densité des tissus mammaires. Traitement du Signal, 33:441-460, 2016. Google Scholar
  10. Alireza Fathi and John Krumm. Detecting road intersections from gps traces. In International Conference on Geographic Information Science, pages 56-69. Springer, 2010. Google Scholar
  11. Daphne Koller and Nir Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009. Google Scholar
  12. Jun Li, Qiming Qin, Jiawei Han, Lu-An Tang, and Kin Hou Lei. Mining trajectory data and geotagged data in social media for road map inference. Transactions in GIS, 19(1):1-18, 2015. Google Scholar
  13. Andy Liaw, Matthew Wiener, et al. Classification and regression by randomforest. R news, 2(3):18-22, 2002. Google Scholar
  14. Xuemei Liu, James Biagioni, Jakob Eriksson, Yin Wang, George Forman, and Yanmin Zhu. Mining large-scale, sparse gps traces for map inference: Comparison of approaches. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '12, pages 669-677, New York, NY, USA, 2012. ACM. Google Scholar
  15. Gilles Louppe. Understanding random forests: From theory to practice. arXic, 2014. URL: http://arxiv.org/abs/1407.7502.
  16. Y Méneroux, D Manandhar, S Ranjit, G Saint Pierre, and R Shibasaki. Positional accuracy control in dense urban environment with low-cost receiver and multi-constellation gnss. In Proc. 9th Multi-GNSS Asia – MGA Conference, 2017. Google Scholar
  17. Volodymyr Mnih and Geoffrey E. Hinton. Learning to detect roads in high-resolution aerial images. In Kostas Daniilidis, Petros Maragos, and Nikos Paragios, editors, Computer Vision - ECCV 2010, pages 210-223, Berlin, Heidelberg, 2010. Springer Berlin Heidelberg. Google Scholar
  18. Ana Tsui Moreno and Alfredo García. Use of speed profile as surrogate measure: Effect of traffic calming devices on crosstown road safety performance. Accident Analysis &Prevention, 61:23-32, 2013. Google Scholar
  19. Mario Munoz-Organero, Ramona Ruiz-Blaquez, and Luis Sánchez-Fernández. Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on gps traces while driving. Computers, Environment and Urban Systems, 68:1-8, 2018. Google Scholar
  20. Paul Newson and John Krumm. Hidden markov map matching through noise and sparseness. In Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, pages 336-343. ACM, 2009. Google Scholar
  21. Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. Collective classification in network data. AI magazine, 29(3):93, 2008. Google Scholar
  22. Bernard W Silverman. Density estimation for statistics and data analysis, volume 26. CRC press, 1986. Google Scholar
  23. Mohit Dev Srivastava, Shubhendu Sachin Prerna, Sumedha Sharma, and Utkarsh Tyagi. Smart traffic control system using plc and scada. International Journal of Innovative Research in Science, Engineering and Technology, 1(2):169-172, 2012. Google Scholar
  24. Leon Stenneth and Philip S. Yu. Monitoring and mining gps traces in transit space. In Proceedings of the 2013 SIAM International Conference on Data Mining, pages 359-368, 2013. URL: http://dx.doi.org/10.1137/1.9781611972832.40.
  25. Karl Van Winden, Filip Biljecki, and Stefan Van der Spek. Automatic update of road attributes by mining gps tracks. Transactions in GIS, 2016. Google Scholar
  26. Christopher K. H. Wilson, Seth Rogers, and Shawn Weisenburger. The potential of precision maps in intelligent vehicles. In IEEE International Conference on Intelligent Vehicles, pages 419-422. Citeseer, 1998. Google Scholar
  27. Lijuan Zhang and Monika Sester. Incremental data acquisition from gps-traces. In Geospatial Data and Geovisualization: Environment, Security, and Society; Special Joint Symposium of ISPRS Commission IV and AutoCarto, 2010. Google Scholar
  28. Qiaoping Zhang and Isabelle Couloigner. Automated road network extraction from high resolution multi-spectral imagery. In Proceedings of ASPRS 2006 Annual Conference, pages 01-05, 2006. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact