Predictive Traffic Risk Scoring for Safer Intersections
Software tool, Transportation, Urban planning

Unmet need

Road traffic injuries remain a major global health burden and a leading cause of death, particularly among vulnerable populations such as children. Yet safety management remains largely reactive, relying on historical collision data to identify dangerous locations. This retrospective approach overlooks “traffic hazard”, the potential for severe harm that can exist even where few incidents are reported, since risky areas are often avoided, masking true danger. Current assessment methods are limited: they can be resource-intensive, rely on oversimplified models of complex urban systems, and rarely incorporate stakeholder perspectives or lived experience. As a result, there is a pressing need for a comprehensive, participatory approach that integrates diverse risk factors, captures their interactions, and proactively identifies high-risk locations across urban networks before serious incidents occur.

Technology overview

This next-generation, proactive Traffic Hazard Assessment Tool is designed to help cities implement preventive safety measures by identifying high-risk locations. By integrating a Multi-Criteria Decision-Aiding model with Geographic Information Systems (GIS), the system quantifies and maps the inherent potential for severe harm across street segments and intersections, independent of historical collision data. It evaluates a comprehensive set of qualitative and quantitative criteria, including traffic volume, heavy vehicle presence, visibility, intersection design, and land use. Importantly, the weighting of these factors is established through structured input from experts, planners, and vulnerable users such as children and parents, ensuring that hazard scores reflect both technical evidence and lived experience. Unlike traditional crash-based or linear predictive models, the tool explicitly captures nonlinear interactions among risk factors, enabling the detection of hidden or systemic hazards that standard approaches often miss. The system automatically generates clear, five-level hazard maps that support prioritization, scenario analysis, and transparent decision-making. Scalable and adaptable to diverse urban contexts and data environments, this solution empowers municipalities, transportation agencies, and infrastructure partners to move from reactive safety management to proactive, equitable, and data-driven urban risk prevention.

Principal Investigator

Pr. Owen Waygood

Full Professor titulaire
Department of Civil, Geological and Mining Engineering
Expertise

Eric Proietti
Technology Transfer Manager