Clustering-based methodology for estimating bicycle accumulation levels on signalized links: A case study from the Netherlands

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
77 Downloads (Pure)

Abstract

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
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages1788-1793
ISBN (Electronic)9781538670248
DOIs
Publication statusPublished - 2019
Event22nd IEEE International Conference on Intelligent Transportation Systems, ITSC 2019 - Auckland, New Zealand
Duration: 27 Oct 201930 Oct 2019
https://www.itsc2019.org/

Conference

Conference22nd IEEE International Conference on Intelligent Transportation Systems, ITSC 2019
CountryNew Zealand
CityAuckland
Period27/10/1930/10/19
Internet address

Bibliographical note

Accepted Author Manuscript

Keywords

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

Fingerprint

Dive into the research topics of 'Clustering-based methodology for estimating bicycle accumulation levels on signalized links: A case study from the Netherlands'. Together they form a unique fingerprint.

Cite this