Clustering-based methodology for estimating bicycle accumulation levels on signalized links

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

Reggiani, G. (TU Delft Transport and Planning)
Dabiri, A. (TU Delft Team Bart De Schutter; TU Delft Transport and Planning)
Daamen, W. (TU Delft Transport and Planning)
Hoogendoorn, S.P. (TU Delft Transport and Planning)

Transport and Planning

2019

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.

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

http://resolver.tudelft.nl/uuid:3f8f4ddb-c201-448d-8fe8-70e03ab451cd

https://doi.org/10.1109/ITSC.2019.8917138

IEEE, Piscataway, NJ, USA

9781538670248

2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

22nd IEEE International Conference on Intelligent Transportation Systems, ITSC 2019, 2019-10-27 → 2019-10-30, Auckland, New Zealand

Accepted Author Manuscript

Institutional Repository

conference paper

© 2019 G. Reggiani, A. Dabiri, W. Daamen, S.P. Hoogendoorn