Clustering-based methodology for estimating bicycle accumulation levels on signalized links
A case study from the Netherlands
Giulia Reggiani (Transport and Planning)
Azita Dabiri (Transport and Planning, TU Delft - Mechanical Engineering)
Winnie Daamen (Transport and Planning)
Serge Hoogendoorn (TU Delft - Civil Engineering & Geosciences)
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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.