Despite efforts to reduce them, traffic accidents continue to increase and bypass reduction targets. The costs of traffic accidents are enormous, killing 1.35 million people every year and costing 3% of most countries' GDP. Recent research aims to target interventions at high-accident-density locations, called accident hotspots. New methods and technologies can systematically identify hotspots, but it remains unclear whether hotspots contribute to accident costs as well as volume. This paper investigates the monetary and human costs of accident hotspots. We analyze a dataset of all accidents from 2011 - 2017 in Switzerland. We identify hotspots, then analyze their contributions to traffic accident costs. We find that hotspot accidents are not different in monetary costliness or injury rates from non-hotspot accidents, so hotspots drive costs along with accident volume. However, hotspot accidents are less fatal, so hotspot targeting might not be best for fatalities. If hotspots are reduced to normal road conditions, total monetary costs can be reduced by up to 5% per year as a theoretical upper bound. Targeting the top 10% most frequent, costly, injurious, or deadly hotspots yeilds different results for different cost types, with accident number and monetary cost targets creating the highest reductions overall.
Language
English
Keywords
costing
data analysis
data mining
pattern clustering
road accidents
road safety
road traffic
road vehicles
traffic engineering computing
DBSCAN
density-based spatial clustering of application with noise
data mining clustering
spatial data analysis
Switzerland
road safety
traffic accident costs
high-accident-density locations
road traffic accident hotspots
Injuries
Roads
Standards
Spatial databases
Road accidents
Law enforcement
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
IEEE
Publisher place
Auckland, New Zealand, New Zealand
Volume
22
Start page
883
End page
888
Pages
6
Event Title
2019 IEEE Intelligent Transportation Systems Conference (ITSC)