Open Science in Canadian Transportation Research
Kickoff Meeting
2026-09-03
Welcome
Thanks for joining — since we all know each other, let’s get straight into it.
Today’s goals:
- Ground everyone in the objectives of the funded project
- Introduce a shared way of talking about “how open” our work can (should) be
- Sketch the structure of the working sessions ahead
- Open the floor for discussion
The Project
Canadian Framework for Open Science in Urban Modelling
- Funded by the University of Calgary VPR Open Science Fund (Round 1, June 2026)
- $10,000 — August 2026 to July 2027
Team:
- Jason Hawkins (University of Calgary)
- Eric Miller (University of Toronto)
- Khandker Nurul Habib (University of Toronto)
- Catherine Morency (Polytechnique Montreal)
- Mahmudur Fatmi (University of British Columbia)
- Ahsan Habib (Dalhousie University)
What the Fund Supports
The Open Science Fund is meant to add an Open Science layer to work we’re already doing — not to start from scratch.
That layer can include:
- Practices
- Workflows
- Tools
- Policies
- Infrastructure
…that make our research and its outputs more open, accessible, and reusable — and build a culture of open science across Canadian transportation research groups.
Why This, Why Now
Across Europe, the EAABM position paper (2026) points to a familiar pattern:
- Strong research capability, but slow practical adoption
- Institutional silos between agencies and developers
- A steep divide between academic frontiers and mainstream practice
- Almost no shared curriculum for activity-based modelling
Canada has a similar fragmentation — every group has its own survey instrument, its own model codebase, its own conventions. This project asks: what would it take to change that?
What “Open Science” Means, Federally
Canada’s Roadmap for Open Science (Office of the Chief Science Advisor, 2020) describes open science as making the inputs, outputs, and processes of research as freely available as possible, with minimal restriction — while still fully respecting privacy, security, ethical considerations, and appropriate IP protection.
The federal lifecycle model has four stages, feeding back into each other:
- Ideation — open, community-engaged research questions
- Data Collection & Analysis — open methods, open data, citizen science
- Publication — preprints, open access
- Knowledge Mobilization — sharing results so they’re actually usable
Note: openness is a practice across the whole lifecycle, not just “put the data online at the end.”
What “Open Science” Means
- Openness is a spectrum, not a binary — a project can be further along on some dimensions (e.g., open-access publication) and less far along on others (e.g., shared code).
Four pillars UCalgary uses to organize this:
- Open scientific knowledge
- Open science infrastructures
- Open engagement of societal actors
- Open dialogue with other knowledge systems
Our project — and the spectrum on the next slide — is essentially us asking what these pillars look like specifically for transportation survey and model development.
The Spectrum of Open Science
Share What We Do
Publish methods, papers, documentation. Code and data stay private.
Common Language
Same programming language / platform conventions, so code is at least legible across groups.
Shared Structure
Common survey instrument design, common model schema — outputs are comparable, still built independently.
Common Model
One shared model / instrument, with modules independently developed and maintained by different groups.
Not every research project (or even field) needs to sit at the far end — but we should be intentional about where we sit, and why.
A Model From Another Field: Climate Science
Steve Easterbrook (University of Toronto) has spent two decades studying how climate modelling groups build and share their models — most recently summarized in his 2023 book Computing the Climate.
A few things climate science worked out decades before transportation modelling:
- Institutions build separate components (atmosphere, ocean, ice, land surface) that plug into a shared coupler — a modular architecture where components can be swapped without breaking the whole system
- International model intercomparison projects (e.g., CMIP) run the same benchmark experiment across dozens of independently developed models, so the community can see exactly where models agree and where they diverge
- Shared coupling frameworks (e.g., ESMF/NUOPC) standardize how components talk to each other — groups don’t have to agree on everything, just on the interfaces
Barriers Climate Modelling Had to Solve
Easterbrook’s research on climate code-sharing identified recurring barriers to openness — several will sound familiar:
- Portability — code that only runs on one lab’s specific setup
- Configurability — local tweaks and edge cases baked into the code
- Entrenchment — decades of institutional investment in existing tools
- Model–data blur — model code tangled up with specific input datasets
- Provenance — losing track of where a result came from once code is modified and passed around
Discussion: which of these look most like our situation with survey instruments and ABMs?
Structure of Future Meetings
Two parallel tracks, each built around the same set of questions:
Track 1 — Surveys
- What questions do we include?
- What are the common elements?
- Where do we already agree?
- Where do we disagree on methods / implementation?
Track 2 — Models
- What is an ABM, for our purposes?
- What are the common elements?
- Where do we already agree?
- Where do we disagree on methods / implementation?
Each session: a short presentation from one or two groups, followed by open discussion.
Track 1: Survey Fundamentals
Possible starting topics:
- Core question modules (trips, activities, household roster, attitudes)
- Recruitment and sampling approaches across our cities
- Definitions: what counts as a “trip purpose”?
- Data formats and codebooks — could we agree on a common schema?
- GDPR/PIPEDA-equivalent handling and anonymization practices
Track 2: Model Fundamentals
Possible starting topics:
- Population synthesis approaches in use across groups
- Tour vs. activity-based scheduling logic
- Software stacks: internal code (C#/Python/Java) vs. assignment (Emme/MatSim)
- Where do our models diverge most — mode choice? scheduling? assignment?
- What would a “reference” Canadian ABM even look like?
Beyond Surveys and Models
Other threads worth carving out sessions for:
- ABM in graduate curricula — EAABM’s teaching committee found ABM development is rarely taught outside PhD programs; is it worth covering in courses?
- Interdisciplinary frontiers — energy systems, public health, EV charging demand
- Software ecosystem — where do we build vs. adopt vs. contribute upstream?
- Emerging methods — generative AI / ML approaches to schedule synthesis
Discussion
- Does this structure make sense?
- Which track should we start with?
- Who wants to give the first short presentation, and on what?
- What’s missing from this list?
Thank You
I look forward to learning from everyone!