Travel route safety estimation based on conflict simulation.

Area of Science:

• Transportation Engineering
• Traffic Safety Analysis
• Machine Learning Applications

Background:

• Assessing travel route safety is crucial for informed traveler decisions.
• Existing methods may not adequately capture the complexities of road safety.
• A robust and adaptable safety estimation model is needed.

Purpose of the Study:

• To develop and validate a conflict-based approach for travel route safety estimation (TRSE).
• To integrate dynamic and static factors for comprehensive risk assessment.
• To provide travelers with reliable information on the safety levels of selectable routes.

Main Methods:

• A conflict-based approach integrating classical safety evaluation with risk exposure and unit risk.
• Utilized dynamic and static factors (traffic flow, roadway features) for conflict estimation.
• Employed machine learning models (Random Forest, KNN, SVM) and fuzzy logic for route safety assessment.

Main Results:

• The proposed TRSE method demonstrated practicability and effectiveness in a simulated Chinese road network.
• Conflict simulation using Vissim and SSAM validated the approach's performance.
• The model successfully estimated safety levels for component and intersection links.

Conclusions:

• The developed TRSE approach offers a reliable method for evaluating travel route safety.
• Integrating diverse data factors and machine learning enhances safety estimation accuracy.
• This research contributes to safer transportation planning and traveler information systems.