Research project title
Predictive analysis of road crashes
Education level
Master or doctorate
Director/co-director
Director: Nicolas Saunier
End of display
August 30, 2025
Areas of expertise
Primary sphere of excellence in research
Sustainable Transport and Infrastructures
Unit(s) and department(s)
Department of Civil, Geological and Mining Engineering
Conditions
The student must have or be willing to develop strong programming skills, i.e., a working knowledge of a high-level language such as Python.
While the student does not need to be a software engineer, they must be rigorous and dedicated to developing computer code. An interest in transportation is an asset.
Please fill in the following form to apply : https://forms.office.com/r/u7xCEDMiH8
Detailed description
Road collisions are complex phenomena, associated with several factors related to the three components of the road system: infrastructure, vehicles, road users, and environmental conditions (e.g., weather). Determining the relationships between these factors is the subject of studies based on classical statistical approaches, and more recently, on approaches from the fields of artificial intelligence (AI) and machine learning.
Several projects applied to road safety are available, particularly for predictive analysis of road accidents. AI-based approaches could help better predict accident occurrence and severity, and enable various proactive interventions that can reduce the number and severity of accidents using historical collision data, data on static accident factors, as well as real-time data such as traffic conditions, weather conditions, user behavior, and incidents (e.g., infrastructure failures).
Financing possibility
Masters and doctoral scholarships at the Polytechnique rate are available.
Nicolas Saunier
Full Professor
