 A spatio-temporal mapping to assess bicycle collision risks on high-risk areas (Bridges) - A case study from Taipei (Taiwan)Hwachyi Wang, Hans De Backer, Dirk Lauwers and S.K.Jason Chang Journal of Transport Geography, 2019, vol. 75, issue C, 94-109 Abstract: Most bicycle collision studies aim to identify contributing factors and calculate risks based on statistical data (Loidl et al., 2016). The aim of this paper is to follow this approach, focusing on bicycle-motorized vehicle (BMV) collisions through a spatio-temporal workflow. For the spatial dimension (Kernel Density Estimation (KDE) method), a general estimation of the collision risks was obtained and the labour-intensive work of collecting counting data was avoided on the macro-scale level. The temporal dimension (negative binomial modeling method) focused on data from collisions occurring on bridges, enabling the inclusion of traffic exposure (counting data on the micro-scale level). Bridge collision risks and contributing factors related to road environment and cycling facilities were estimated using databases from eight government authorities and field investigation. Keywords: Urban bridge; Spatio-temporal; Kernel Density Estimation (KDE); Negative binomial modeling; Bicycle-motorized vehicle collisions (BMV); Geographic information system (GIS) (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:75:y:2019:i:c:p:94-109 DOI: 10.1016/j.jtrangeo.2019.01.014 Access Statistics for this article Journal of Transport Geography is currently edited by Frank Witlox More articles in Journal of Transport Geography from Elsevier |
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