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    Accepted author manuscript, 1 MB, PDF-document


The number of queued bicycles on a signalised link is crucial information for the adoption of intelligent transport systems, aiming at a better management of cyclists in cities. An unsupervised machine learning methodology is deployed to produce estimations of accumulation levels based on data retrieved from a bicycle street of the Netherlands. The use of a clustering-based approach, combined with a conceptual insight into the bicycle accumulation process and various data sources, makes the applied methodology less dependent on sensor errors. This clustering-based methodology is a first step in bicycle accumulation estimation and clearly identifies levels of cyclists accumulated in front of a traffic light.
Original languageEnglish
Title of host publication2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Number of pages6
ISBN (Electronic)9781538670248
Publication statusPublished - 2019
Event22nd IEEE International Conference on Intelligent Transportation Systems, ITSC 2019 - Auckland, New Zealand
Duration: 27 Oct 201930 Oct 2019


Conference22nd IEEE International Conference on Intelligent Transportation Systems, ITSC 2019
CountryNew Zealand
Internet address

    Research areas

  • Modeling, Simulation, and Control of Pedestrians and Cyclists, Data Mining and Data Analysis, Off-line and Online Data Processing Techniques

ID: 55353957