Identifying Causal Risk Factors for Hazardous Driving and Accident Propensity for Safer Fleets and Smart Cities

Road safety affects everyone. In an effort to reduce accidents, we need to understand both the driving behavioural patterns that are predictive of accidents, and the environmental factors involved. In order to analysis risks, at first, collect a rich set of data: including Latitude/Longitude, engine RPM, accelerometer data in the X, Y, and Z plane, ambient temperature, and much more. Then, several derived datasets were created from aggregate customer information which provide metadata about the surrounding environment and pulled in numerous third-party data sources. The research will be focused on processing this data in such a way that useful features representing driving behaviours can be extracted for training machine learning models.

Faculty Supervisor:

Andrei Badescu

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: