Simulating the Future: A Digital Twin Framework for Rapidly Developing Mid-Size Canadian Cities: The Abbotsford Public Transit Case Study

Rapidly developing, mid-sized Canadian municipalities often suffer from a deficit in dedicated modernization capacity, leaving public infrastructure lagging behind growth and reliant on historically “grandfathered” legacy solutions. To overcome the lack of empirical, data-backed planning in these regions, this paper proposes an agile, data-driven smart city framework centered around a localized digital twin (DT) environment. The framework is evaluated through a case study of a proposed new public transit route in Abbotsford, British Columbia, a rapidly expanding city grappling with decentralized commercial zones and low-density sprawl. Our approach synthesizes heterogeneous, multi-source spatial data, including regional commuter trajectories, real-time Abbotsford International Airport (YXX) flight schedules, and points of interest (POI) business densities, to map high-resolution hourly temporal variations in traffic conditions. These streams feed into a virtual simulation framework that evaluates operational cost–benefit trade-offs for proposed transit routes. Crucially, this framework serves as a living, continuously updated system that enables resource-constrained cities to dynamically simulate transit networks as commercial footprints and transit volumes evolve. Finally, we discuss the roadmap for this framework, detailing how integrating predictive AI models and gamified interfaces can democratize urban planning, enabling municipal stakeholders and non-technical operators to interactively co-design public transit systems.