Network-constrained spatial identification of high-risk roads for hit-parked-vehicle collisions in Brisbane, Australia
Liu, Yan
Network-constrained spatial identification of high-risk roads for hit-parked-vehicle collisions in Brisbane, Australia - Sage 2019. - Vol 51, Issue 2, 2019,(279-282 p.)
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.
Hit-parked-vehicle collision,
network-constrained spatial statistics,
local indicator of network-constrained clusters,
Brisbane
Network-constrained spatial identification of high-risk roads for hit-parked-vehicle collisions in Brisbane, Australia - Sage 2019. - Vol 51, Issue 2, 2019,(279-282 p.)
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.
Hit-parked-vehicle collision,
network-constrained spatial statistics,
local indicator of network-constrained clusters,
Brisbane