Research and implementation of machine learning model deployment and management platform based on IoT big data environment for safer fleets and smart cities

Road safety has been a major concern to everyone. Geotab has collected driving and environmental data from over 2 million connected vehicles, looking for the solution for safe communities and cities. To reduce accidents, it is needed to understand both driving behavioral patterns that are predictive of accidents, and the environmental factors involved. To achieve this, the data infrastructure should be capable of processing a series types of real- time video and telematic data, as well as time-series historical records generated from existing machine learning models to respond to real world incidents within a short period of latency. The objectives of this project is to research and develop algorithms to enhance aggregation efficiency for telematics data with different sampling rates and volumes. This project also involves developing and implementing data processing infrastructure components for real-time and historical video/telematics data in a distributed system manner.

Faculty Supervisor:

Ben Liang

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: