The intersection of industry and academia often leads to innovations with wide-reaching impacts, as demonstrated by Simone Percy’s applied research internship at Geotab. Simone is in the Data Science concentration of the University of Toronto’s MSc in Applied Computing program and is currently showcasing how data science can be used for societal good — by enhancing Geotab’s Intelligent Transportation Systems (ITS) platform, Altitude.
Geotab is a global leader in fleet management and vehicle tracking. The company manages over 4 million connected vehicles, forming one of the world’s largest mobile IoT sensor networks. Using the fleet and vehicle data, the Altitude platform provides transportation insights, offering cities and businesses the ability to optimize fleet operations, improve urban planning, and make transportation more sustainable.
Percy’s work strives to improve one of the key features of Altitude — Stop Analytics — by developing a machine learning framework to categorize vehicle stops, based on vehicle behaviour, land use, and surrounding infrastructure.
This added functionality has significant social implications. By providing actionable insights to city planners and fleet managers, Simone’s work on Stop Analytics helps reduce congestion, improve road safety, and supports the sustainability goals of businesses and municipalities alike.
Addressing Real-World Challenges
Percy’s project aimed to segment vehicle stop locations by analyzing telematics and spatial data. By using Geotab’s vast data environment, she developed a machine learning framework to categorize these stops based on vehicle behaviour, land use, and surrounding infrastructure – ultimately making cities smarter and safer for society.
Knowing where vehicles stop and for how long is crucial for planning infrastructure, especially in urban areas facing challenges related to congestion, emissions, and last-mile delivery. Simone’s innovative work also provides municipalities and businesses with the ability to optimize their use of space and resources and improve efficiency, while reducing environmental impact. This directly supports efforts to make cities smarter, safer, and more sustainable.
Additionally, the solution developed by Percy supports the growing demand for electrification infrastructure by identifying where charging stations for electric vehicles are most needed. In an era where environmental sustainability is a top priority, having accurate data on where to deploy such infrastructure can accelerate the transition to greener transport systems.
Bridging Academia and Industry
Throughout her internship, Percy was supervised by Geotab’s Senior Data Scientist, Natalie Smith, with additional support from Professor Scott Sanner at the University of Toronto.
“We leaned into Simone’s technical expertise to drive out innovation that would support Geotab’s development of an enhanced classification model to identify and refine commercial domiciles for commercial vehicles,” says Smith. “Today, she continues to drive out the “productionalization” of the model with the full confidence of the Geotab team in its success and valuable contribution to our product offerings and the continued enhancement of intelligent transportation insights.”
On a broader scale, the partnership between MScAC and Geotab underscores the power of applied research to create tangible business and societal benefits. Geotab has long recognized the value of these internships, having recruited over 20 MScAC interns since 2014. “The MScAC program has been an exceptional source of talent for our Data & Analytics team at Geotab. We are proud to have hired many of our students for full-time positions following their internships, recognizing their exceptional talent and the high-quality education they have received,” says Willem Petersen, manager, data science. These project collaborations have ranged from accident detection to vehicle servicing predictions, all contributing to a safer, more efficient transportation ecosystem and ultimately shaping a better future for cities and communities.
