Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/31041
Title: On the use of clustering analysis for identification of unsafe places in an urban traffic network
Authors: Holmgren, Johan
KNAPEN, Luk 
Olsson, Viktor
Masud, Alexander
Issue Date: 2020
Publisher: Elsevier
Source: Elsevier, p. 187 -194
Abstract: As an alternative to the car, the bicycle is considered important for obtaining more sustainable urban transport. The bicycle has many positive effects; however, bicyclists are more vulnerable than users of other transport modes, and the number of bicycle related injuries and fatalities are too high. We present a clustering analysis aiming to support the identification of the locations of bicyclists' perceived unsafety in an urban traffic network, so-called bicycle impediments. In particular, we used an iterative k-means clustering approach, which is a contribution of the current paper, and DBSCAN. In contrast to standard k-means clustering, our iterative k-means clustering approach enables to remove outliers from the data set. In our study, we used data collected by bicyclists travelling in the city of Lund, Sweden, where each data point defines a location and time of a bicyclist's perceived unsafety. The results of our study show that 1) clustering is a useful approach in order to support the identification of perceived unsafe locations for bicyclists in an urban traffic network and 2) it might be beneficial to combine different types of clustering to support the identification process.
Other: ant2020_paper36_review.pdf contains the peer review
Keywords: Cluster analysis;k-means;iterative k-means;DBSCAN;Click-point data;bicycle impediment
Document URI: http://hdl.handle.net/1942/31041
ISSN: 1877-0509
DOI: 10.1016/j.procs.2020.03.024
ISI #: WOS:000582714500023
Rights: 2020The Authors. Published by Elsevier B.V.This is an open access article under the CCBY-NC-ND license
Category: C1
Type: Proceedings Paper
Validations: ecoom 2021
Appears in Collections:Research publications

Files in This Item:
File Description SizeFormat 
1-s2.0-S1877050920304531-main.pdfPublished version1.73 MBAdobe PDFView/Open
Show full item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.