Identifying Risk Factors for Hazardous Driving and Accident Propensity

Road safety affects everyone, Geotab has several years of driving and environmental data from over 2 million
connected vehicles providing the opportunity to make customers safer, as well as our communities and cities.
This project will leverage data and existing methods to build a model that can identify causal risk factors for
hazardous driving and accident propensity. The model will output a safety score representing the risk level of
fleets/drivers, which can facilitate high efficiency and safety management of light, medium and heavy duty
vehicles. With better understanding of environmental causality, safety concerns can be addressed proactively to
prevent accidents.

Faculty Supervisor:

Andrei Badescu;Sheldon Lin

Student:

Partner:

Geotab Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

University:

University of Toronto

Program: