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

A case study from the Netherlands

Conference paper (2019)

Authors

G. Reggiani

Azita Dabiri ,

A. Dabiri ,

W. Daamen

S.P. Hoogendoorn Transport and Planning

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

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

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

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Published Date

2019

Language

English

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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.

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