TRLUP – From Sparse Data to Actionable Insights: A Transit Ridership Platform

Public transit agencies in Canada often struggle to accurately measure and predict ridership patterns due to limited and incomplete data. Traditional methods typically rely on a single data source, which makes it hard to get a clear view of how people move across the transit network. Without this information, agencies find it challenging to match service levels to actual demand, plan future routes, and respond effectively to changing conditions.
This project aims to solve these issues by developing a scalable service that combines multiple sources of data such as automated passenger counting, mobile phone signals, and ticket validation into a unified view of ridership across the transit network. By integrating these different sources, the platform can produce detailed estimates of passenger movements, even when some data are missing or unreliable.
Using these estimates, transit agencies can make more informed decisions about service delivery, route planning, and operational adjustments. The platform can help match service to actual demand, reduce overcrowding, cut waste, and improve the overall passenger experience.
This technology has the potential to be a valuable tool for transit providers across Canada, from large metropolitan transit systems to small and medium-sized agencies. It supports smarter, data-informed decisions that can help make public transit more efficient, reliable, and sustainable, strengthening Canadian communities and reducing their carbon emissions in the process.

Faculty Supervisor:

Jamie McInnis;Omar Wahdan

Student:

Partner:

The ETC Foundation

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Southern Alberta Institute of Technology

Program: