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

Giulia Reggiani; Azita Dabiri; Winnie Daamen; Serge Hoogendoorn

doi:10.1109/ITSC.2019.8917138

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

Giulia Reggiani, Azita Dabiri, Winnie Daamen, Serge Hoogendoorn

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

3 Citations (Scopus)

135 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 language	English
Title of host publication	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE
Pages	1788-1793
ISBN (Electronic)	9781538670248
DOIs	https://doi.org/10.1109/ITSC.2019.8917138
Publication status	Published - 2019
Event	22nd IEEE International Conference on Intelligent Transportation Systems, ITSC 2019 - Auckland, New Zealand Duration: 27 Oct 2019 → 30 Oct 2019 https://www.itsc2019.org/

Conference

Conference	22nd IEEE International Conference on Intelligent Transportation Systems, ITSC 2019
Country/Territory	New Zealand
City	Auckland
Period	27/10/19 → 30/10/19
Internet address	https://www.itsc2019.org/

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

Access to Document

10.1109/ITSC.2019.8917138

rootAccepted author manuscript, 1.42 MB

Cite this

@inproceedings{3f8f4ddbc201448d8fe870e03ab451cd,

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

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.",

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

author = "Giulia Reggiani and Azita Dabiri and Winnie Daamen and Serge Hoogendoorn",

note = "Accepted Author Manuscript; 22nd IEEE International Conference on Intelligent Transportation Systems, ITSC 2019 ; Conference date: 27-10-2019 Through 30-10-2019",

year = "2019",

doi = "10.1109/ITSC.2019.8917138",

language = "English",

pages = "1788--1793",

booktitle = "2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019",

publisher = "IEEE",

address = "United States",

url = "https://www.itsc2019.org/",

}

Reggiani, G, Dabiri, A , Daamen, W & Hoogendoorn, S 2019, Clustering-based methodology for estimating bicycle accumulation levels on signalized links: A case study from the Netherlands. in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019., 8917138, IEEE, Piscataway, NJ, USA, pp. 1788-1793, 22nd IEEE International Conference on Intelligent Transportation Systems, ITSC 2019, Auckland, New Zealand, 27/10/19. https://doi.org/10.1109/ITSC.2019.8917138

Clustering-based methodology for estimating bicycle accumulation levels on signalized links: A case study from the Netherlands. / Reggiani, Giulia; Dabiri, Azita ; Daamen, Winnie et al.
2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Piscataway, NJ, USA: IEEE, 2019. p. 1788-1793 8917138.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Clustering-based methodology for estimating bicycle accumulation levels on signalized links

T2 - 22nd IEEE International Conference on Intelligent Transportation Systems, ITSC 2019

AU - Reggiani, Giulia

AU - Dabiri, Azita

AU - Daamen, Winnie

AU - Hoogendoorn, Serge

N1 - Accepted Author Manuscript

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Modeling, Simulation, and Control of Pedestrians and Cyclists

KW - Data Mining and Data Analysis

KW - Off-line and Online Data Processing Techniques

UR - http://www.scopus.com/inward/record.url?scp=85076819315&partnerID=8YFLogxK

U2 - 10.1109/ITSC.2019.8917138

DO - 10.1109/ITSC.2019.8917138

M3 - Conference contribution

SP - 1788

EP - 1793

BT - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

PB - IEEE

CY - Piscataway, NJ, USA

Y2 - 27 October 2019 through 30 October 2019

ER -

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

Abstract

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this