Applying OHSA to Detect Road Accident Blackspots.

Area of Science:

• Transportation Engineering
• Urban Planning
• Public Health

Background:

• Road safety is a growing concern due to increasing traffic crashes and injuries.
• Identifying spatial patterns of traffic accidents, known as blackspots, is vital for effective safety interventions.
• Existing methods for blackspot detection may not fully capture the nuances of different accident types.

Purpose of the Study:

• To detect traffic accident blackspots using an optimized hot spot analysis (OHSA) method.
• To explore the relationship between blackspots and various accident types (participant, severity).
• To evaluate the performance of OHSA in assessing road safety levels.

Main Methods:

• Optimized Hot Spot Analysis (OHSA) was employed to identify accident blackspots.
• Traffic accidents were classified by participants and severity for detailed analysis.
• Incremental spatial autocorrelation was used to determine optimal scale distances for OHSA.
• Binary correlation analysis (R² = 0.89) was used to validate OHSA's effectiveness.

Main Results:

• Optimal scale distances for OHSA varied between 0.6 km and 2.8 km, influenced by travel mode.
• Central cities accounted for 54.6% of total accidents, indicating higher traffic safety challenges.
• Vulnerable communities exhibited diverse blackspots, including specific non-motor vehicle accident clusters.

Conclusions:

• OHSA is an effective tool for identifying traffic accident blackspots and evaluating road safety.
• Central cities and vulnerable communities are priority areas for traffic safety improvements.
• Targeted interventions are needed, especially for non-motor vehicle safety in specific zones.