AI for Safer Roads Innovation Challenge

Methodological robustness and technical soundness (25%)

Is the analytical approach well-designed, reproducible, and grounded in sound data science principles? We will assess the quality of the methodology, the appropriateness of the techniques used, and how well the model handles the data provided. 

Innovation and scalability across countries (25%)

Does the solution go beyond existing approaches, and can it realistically be applied in other countries across Asia and the Pacific with different data environments and road contexts?

Accuracy, transparency, and interpretability (20%)

Does the model produce reliable results, and can its outputs be understood and trusted by non-technical users such as government officials and transport planners?

Policy relevance and practicality (20%)

Are the findings actionable? We will assess whether the solution produces outputs that transport ministries and road safety agencies can realistically use to prioritize interventions and inform policy decisions. 

Visualization clarity and communication (10%)

Is the geospatial output clear, accessible, and effective at communicating risk to a policy audience? The map and supporting visuals should make complex data easy to understand and act on.