Predicting Engine Failure from Vehicle Telematics

(1) Main activities of the partner
Geotab is a global leader in telematics specializing in fleet management solutions to enhance operational efficiency, safety, and sustainability. For this project Geotab will be providing their vast collection of data from over 80,000 customers. Additionally, Geotab will provide the intern with support and structure within their data science and maintenance/ safety team.

(2) Challenges
Diagnostic trouble codes (DTC) and the corresponding warning lights are often the first indicator of a severe mechanical problem with a vehicle. Geotab’s rich data set of ongoing vehicle metrics may allow for the detection of these issues before they become severe enough
to trigger a DTC. However historical breakdowns are recorded in a raw and unstructured timeseries dataset. Drawing insights from this will require extensive data cleaning and modeling to successfully predict severe maintenance issues before their occurrence.

(3) Social or economic benefits
A successful implementation of a predictive model will enable Geotab to empower their customers to achieve even more efficient fleet operations through:
? Enhanced Fleet Efficiency: Increased proactive maintenance will reduce vehicle downtime and repair costs for fleet operators.
? Cost Savings: Improved predictive analytics can help fleet managers optimize maintenance schedules, leading to lower operational expenses.
? Environmental Benefits: Reduction in emissions through better engine performance monitoring and operations.

Faculty Supervisor:

Meredith Franklin

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: