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Integrating Spatial and Temporal Approaches for Explaining Bicycle Crashes in High-Risk Areas in Antwerp (Belgium)

1
Department of Civil Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan
2
Center for Mobility and Spatial Planning, Ghent University, Sint-Pietersnieuwstraat 41 B2, B-9000 Gent, Belgium
3
Department of Civil Engineering, Technologiepark Zwijnaarde 904, Ghent University, B-9052 Zwijnaarde, Belgium
4
Department of Geography, Krijgslaan 281 S8, Ghent University, B-9000 Gent, Belgium
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(13), 3746; https://doi.org/10.3390/su11133746
Received: 11 June 2019 / Revised: 1 July 2019 / Accepted: 3 July 2019 / Published: 9 July 2019
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Abstract

The majority of bicycle crash studies aim at determining risk factors and estimating crash risks by employing statistics. Accordingly, the goal of this paper is to evaluate bicycle–motor vehicle crashes by using spatial and temporal approaches to statistical data. The spatial approach (a weighted kernel density estimation approach) preliminarily estimates crash risks at the macro level, thereby avoiding the expensive work of collecting traffic counts; meanwhile, the temporal approach (negative binomial regression approach) focuses on crash data that occurred on urban arterials and includes traffic exposure at the micro level. The crash risk and risk factors of arterial roads associated with bicycle facilities and road environments were assessed using a database built from field surveys and five government agencies. This study analysed 4120 geocoded bicycle crashes in the city of Antwerp (CA, Belgium). The data sets covered five years (2014 to 2018), including all bicycle–motorized vehicle (BMV) crashes from police reports. Urban arterials were highlighted as high-risk areas through the spatial approach. This was as expected given that, due to heavy traffic and limited road space, bicycle facilities on arterial roads face many design problems. Through spatial and temporal approaches, the environmental characteristics of bicycle crashes on arterial roads were analysed at the micro level. Finally, this paper provides an insight that can be used by both the geography and transport fields to improve cycling safety on urban arterial roads. View Full-Text
Keywords: urban arterial roads; geographic information system; bicycle–motorized vehicle (BMV) crashes; spatial; weighted kernel density estimation; temporal; negative binomial; crash severity index urban arterial roads; geographic information system; bicycle–motorized vehicle (BMV) crashes; spatial; weighted kernel density estimation; temporal; negative binomial; crash severity index
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, H.; Chang, S.K.J.; De Backer, H.; Lauwers, D.; De Maeyer, P. Integrating Spatial and Temporal Approaches for Explaining Bicycle Crashes in High-Risk Areas in Antwerp (Belgium). Sustainability 2019, 11, 3746.

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