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On the use of clustering analysis for identification of unsafe places in an urban traffic network
Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmo Univ, Internet Things & People Res Ctr, S-20506 Malmo, Sweden.;Malmo Univ, Dept Comp Sci & Media Technol, S-20506 Malmo, Sweden..
Hasselt University, Martelarenlaan 42, Hasselt, 3500, Belgium; VU Amsterdam, De Boelelaan 1105, Amsterdam, 1081, Netherlands.
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
2020 (English)In: Procedia Computer Science, E-ISSN 1877-0509, Vol. 170, p. 187-194Article in journal (Refereed) Published
Abstract [en]

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. (C) 2020 The Authors. Published by Elsevier B.V.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 170, p. 187-194
Keywords [en]
Cluster analysis, k-means, iterative k-means, DBSCAN, Click-point data, bicycle impediment
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:mau:diva-37093DOI: 10.1016/j.procs.2020.03.024ISI: 000582714500023Scopus ID: 2-s2.0-85085578574OAI: oai:DiVA.org:mau-37093DiVA, id: diva2:1506456
Conference
11th International Conference on Ambient Systems, Networks and Technologies (ANT) / 3rd International Conference on Emerging Data and Industry 4.0 (EDI), APR 06-09, 2020, Warsaw, POLAND
Available from: 2020-12-03 Created: 2020-12-03 Last updated: 2024-06-17Bibliographically approved

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Holmgren, Johan

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