Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian...

Area of Science:

• Road safety
• Transportation engineering
• Data science

Background:

• Global highway accidents cause over 1.3 million deaths annually, disproportionately affecting developing countries.
• Poor data collection hinders accurate assessment of accident causes and safety measures in developing nations.
• Identifying primary accident causes is crucial for reducing escalating death and injury rates.

Purpose of the Study:

• To demonstrate the Delphi Technique's suitability for generating highway traffic accident data in data-scarce environments.
• To identify major causes of highway traffic accidents in developing countries.
• To model and forecast accident rates using the Delphi Technique and Bayesian Network Model.

Main Methods:

• The Delphi Technique was employed to gather expert consensus on accident causes.
• Korea's highway safety data was used to validate the Delphi Technique's efficacy.
• The Delphi Technique was combined with the Bayesian Network Model for accident modeling and forecasting.

Main Results:

• The Delphi Technique proved to be a viable method for obtaining reliable accident data.
• Validation using Korean data confirmed the technique's applicability in developing countries.
• The integrated approach successfully modeled highway traffic accidents and predicted future rates.

Conclusions:

• The Delphi Technique offers a practical solution for accident data generation in developing countries.
• This methodology can significantly contribute to understanding and mitigating highway accident causes.
• The combined Delphi Technique and Bayesian Network Model provide a robust framework for road safety analysis and planning.