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A spatio-temporal mapping to assess bicycle collision risks on high-risk areas (Bridges) - A case study from Taipei (Taiwan)

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  • Wang, Hwachyi
  • De Backer, Hans
  • Lauwers, Dirk
  • Chang, S.K.Jason

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.

Suggested Citation

  • Wang, Hwachyi & De Backer, Hans & Lauwers, Dirk & Chang, S.K.Jason, 2019. "A spatio-temporal mapping to assess bicycle collision risks on high-risk areas (Bridges) - A case study from Taipei (Taiwan)," Journal of Transport Geography, Elsevier, vol. 75(C), pages 94-109.
    • Handle: RePEc:eee:jotrge:v:75:y:2019:i:c:p:94-109
    DOI: 10.1016/j.jtrangeo.2019.01.014
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    1. Hwachyi Wang & S. K. Jason Chang & Hans De Backer & Dirk Lauwers & Philippe De Maeyer, 2019. "Integrating Spatial and Temporal Approaches for Explaining Bicycle Crashes in High-Risk Areas in Antwerp (Belgium)," Sustainability, MDPI, vol. 11(13), pages 1-28, July.

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