A network-constrained spatial identification of high-risk roads for hit-parked-vehicle collisions in Brisbane, Australia

Yan Liu, Siqin Wang, Xuanming Fu and Bin Xie

Environment and Planning A, 2019, vol. 51, issue 2, 279-282

Abstract: The severe loss of human life and material damage caused by traffic accidents is a growing concern faced by many countries across the world. In Australia, despite a decline in the total number of traffic collisions since 2001, the number of hit-parked-vehicle (HPV) collisions as a special type of road accident has increased over time. Utilizing the road collisions and roadway network data in Brisbane, Australia over a 10-year period from 2001 to 2010, we generated graphics illustrating the spatial patterning of high-risk road segments for HPV crashes identified using the local indicator of network-constrained clusters (LINCS) approach. These spatial patterns vary by days of the week and times of the day. Roads with high risk for HPV collision tend to occur in high-density road networks and cluster around road intersections. The methodology applied in this work is applicable to other network-constrained point-pattern analysis.

Keywords: Hit-parked-vehicle collision; network-constrained spatial statistics; local indicator of network-constrained clusters; Brisbane (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envira:v:51:y:2019:i:2:p:279-282

DOI: 10.1177/0308518X18810531

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