Abstract

This thesis proposes a novel approach to enhance road safety by integrating historical vehicle crash data with the International Road Assessment Program (iRAP) road protection scores. The primary objective is to identify high-risk locations and prioritize remedial works effectively.
The research involves two key tasks: first, employing hotspot analysis to pinpoint significant locations based on road protection deficiencies and accident density per type of crash; second, using a rule set to identify road segments that should receive remedial works by comparing the crashes on the map based on injury-severity itself (fatal, minor, serious) with star rating score in iRAP data for different users (pedestrian, vehicles, cyclist, motorcyclist) for per type of crash. iRAP’s Star Rating Scores (SRS) quantify the relative risk to road users, providing a basis for identifying priority road sections and comparing performance over time.
The methodology integrates iRAP assessments with historical crash data to produce a spatial safety analysis, highlighting areas with a high frequency of severe accidents and road deficiencies. This combined approach allows for more comprehensive identification of risk locations, guiding the deployment of targeted remedial works
The results demonstrate that integrating iRAP scores with crash data provides a clearer identification of hazardous locations, supporting more informed decision-making for road safety improvements. The findings suggest that prioritizing remedial works at locations with high crash rates and low iRAP scores can significantly enhance road safety.
In conclusion, this thesis contributes to the field of road safety management by presenting a robust method for combining historical crash data with iRAP assessments. The proposed approach not only identifies high-risk areas but also prioritizes them for remedial actions.