Castells-Graells, D, Salahub, C and Pournaras, E (2020) On cycling risk and discomfort: urban safety mapping and bike route recommendations. Computing, 102 (5). pp. 1259-1274. ISSN 0010-485X
Abstract
Bike usage in Smart Cities is paramount for sustainable urban development: cycling promotes healthier lifestyles, lowers energy consumption, lowers carbon emissions, and reduces urban traffic. However, the expansion and increased use of bike infrastructure has been accompanied by a glut of bike accidents, a trend jeopardizing the urban bike movement. This paper leverages data from a diverse spectrum of sources to characterise geolocated bike accident severity and, ultimately, study cycling risk and discomfort. Kernel density estimation generates a continuous, empirical, spatial risk estimate which is mapped in a case study of Zürich city. The roles of weather, time, accident type, and severity are illustrated. A predominance of self-caused accidents motivates an open-source software artifact for personalized route recommendations. This software is used to collect open baseline route data that are compared with alternative routes minimizing risk and discomfort. These contributions have the potential to provide invaluable infrastructure improvement insights to urban planners, and may also improve the awareness of risk in the urban environment among experienced and novice cyclists alike.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | Cycling; Bike; Accident; Severity; Weather; Zurich; Risk; Safety; Route; Recommendation; Smart City; Kernel density |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Feb 2020 15:17 |
Last Modified: | 02 Oct 2020 13:39 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/s00607-019-00771-y |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156764 |
Download
Filename: Castells-Graells2020_Article_OnCyclingRiskAndDiscomfortUrba.pdf
Licence: CC-BY 4.0