Date/Time: Thursday, September 24, 2026 10:00-11:30am
Location: MAG - 586 E 800 N, Orem, Utah 84097
Virtual option available through the calendar appointment. Email utahMUG@gmail.com to request the calendar appointment.
Agenda
Welcome & Introductions
- Introductions
- User Spotlights led by Tim Baird, Fehr & Peers
Discussion Topics
- Utah-Specific resources:
- Traffic Volume Map (link)
- Household/Job Viewer (link)
- Model one-pagers and documentation (link)
- January 2027 UtahMUG Presentation solicitations (link) – due by November 9, 2026
- Others
Presentations
Why Did the Model Do That? (link)
Fiona Dunn and Kwasi Donkor, Fehr & Peers
Regional travel demand models are increasingly used to evaluate project-level impacts, but they don’t always behave as expected. Drawing from a recent statewide Caltrans study, this presentation introduces a practical framework for evaluating model reasonableness and sensitivity, highlights common pitfalls, and offers guidance for improving confidence in project-level applications.
Fiona Dunn is a transportation planner with Fehr & Peers who works on travel demand modeling, forecasting, and multimodal transportation planning across Utah.
Kwasi Donkor is a Principal with Fehr & Peers specializing in travel demand modeling, VMT analysis, and project-level forecasting, with over 20 years of experience.
Half-Time Break: Bad Transportation Jokes (link)
Jared Lillywhite, Mountainland Association of Governments
The Trips We Don’t See: Suppressed Travel and Unmet Demand in Household Travel Surveys
Matthew Palm, University of North Carolina at Chapel Hill; Sarah Grajdura, University of Illinois Chicago
Most travel analyses focus on trips that occur—but what about trips people wanted or needed to make but couldn’t? Using data from the 2023 Utah Moves Travel Survey, this presentation examines “suppressed travel,” identifies the populations and barriers most associated with missed trips, and explores how hidden travel demand can improve our understanding of transportation equity and travel behavior.
Matthew Palm is an Assistant Professor of City and Regional Planning at the University of North Carolina at Chapel Hill whose research focuses on transportation planning, accessibility, travel behavior, and transportation equity.
Sarah Grajdura is an Assistant Professor of Civil and Environmental Engineering at the University of Illinois Chicago whose research focuses on transportation equity, accessibility, travel behavior, and transportation system resilience. The work presented here was conducted while she was at Utah State University.
Next meeting
Date/Time: Thursday, January 21, 2027, 10:00-11:30 am @ UDOT.
🍽 lunchiMUG
Gather with us at Kneaders in the Riverwoods, 4833 N Edgewood Drive, Provo, UT 84604
Notes - thanks to Josh Alpers, Horrocks
Announcements
- MAG will be in a new office in a couple of months
Model Status Update
- Cache TDM
- Wasatch Front TDM
- Commercial Vehicle model: updates to the SE data have been incorporated into the beta 2 model
- New vehicle bin classification
- Truck accuracy is going up
- OpenPaths Cube is now in use; reach out to Suzie Swim for access
- v9.3 coming out in a month, with an updated freeway interchange on West Davis Corridor
- v10 to be official sometime mid next year; RTP has to be adopted
- v11 (activity-based model) under development
- Summit-Wasatch TDM
- Now going to be called the Wasatch Back TDM to match the MPO
- Truck accuracy getting updated
- Winter and Summer calibrations underway
- To be released with the LRP adoption
- Iron TDM
- Calibrated for Fall-Spring
- Truck component calibration
- Now in OpenPaths
- Utah Statewide Travel Model
- Calibrated for Spring-Fall
- Updated recreation component
- Freight updated
- Dixie TDM
- Working through the LRP to get to a financially constrained list
Why Did the Model Do That? Fiona Dunn and Kwasi Donkor, Fehr & Peers
- 2025 evaluation of models; MPOs are responsible for their own models
- Regulatory review
- Federal law vs. California-specific
- Survey and interviews
- 44 MPOs and RTPAs with a 55% response rate
- Not all agencies provide their models; many keep them in house
- What calibration and validation efforts are being used, and when was the last effort
- 68% had models
- Reasonableness and sensitivity concerns
- Transit forecasts
- Intersection turning movement forecasts
- Key theme
- Regional models are not built for project-level analysis
- Model review
- Documentation, model year alignment, performance vs. guidance, modeling detail, and sub-modules
- Do your model years align?
- Is the model credible? How does it compare to real-world conditions?
- Most places either didn’t have, or had incomplete, user guides and project-level guides
- Key limitations and focus for improvements
- Lack of user guides, outdated base years, low post-COVID sensitivity, induced travel under-represented, etc.
- Project review
- What’s missing
- Transparency – source models and edits unclear
- Analysis year – base/horizon don’t match
- Purpose alignment – regional-only validation/limited sensitivity
- ABM noise – stochasticity rarely tested
- Random seed variables with population; what’s the range of change that you can find?
- Findings and recommendations
- Recurring challenges
- Purpose misalignment, insufficient documentation, etc.
- Near term
- Coordinate, Document, and Check
- Long term
- Fund, Train, and Modernize
- Thoughts for Utah
- Utah scoring
- Development Guides are great
- Installation Guide – not very robust here in Utah
- User Guide – robust user guides, which include instructions on how to edit
- Project Application Guide – not available
- Sub-area model calibration & validation
- The WFRC model was utilized for the PEL Study
- The model had to be further refined using data from tube counts, CCS, PeMS, ClearGuide, and StreetLight
- Speeds, distribution, volumes, sensitivity testing
- Result – a sub-area of the model that was validated to recent, locally observed data
- No guidelines on how closely the model needs to be calibrated
- Takeaways
- Fewer models to align, and currently in a better spot with base and out-year alignment
- Coordination efforts such as MUG are very helpful
- Continue to prioritize documentation and standardize reporting
- Consistent validation methodologies and criteria
- Criteria for project-level application
- Discussion
- Standards are a double-edged sword
- Systemic issues vs. particular link results
- Move towards these numbers, not necessarily hit these numbers, because it’s possible to game the system
- Version control and fingerprinting
- Using big data sources (StreetLight, etc.) as ground truth might not be the best approach
- Variability in rigor for different project tasks
- UDOT and consultants
- What would you like to see?
The Trips We Don’t See: Suppressed Travel and Unmet Demand in Household Travel Surveys Matthew Palm, University of North Carolina at Chapel Hill; Sarah Grajdura, University of Illinois Chicago
- Why suppressed travel?
- Hidden mobility need – trips that people wanted to make but couldn’t
- Real consequences – missed medical appointments, missed classes and work, foregone participation in the activities that shape health and livelihoods
- This study links suppressed travel to sociodemographics, resources, accessibility, commuting, and EV ownership statewide
- A third of Utah adults reported missing a desired trip in the past week
- Three questions, one arc
- Who – what demographic and built environment factors predict who experiences suppressed travel?
- Why – which barriers prevent people from making trips, and which barriers co-occur for the same people?
- What types – can we construct a typology of trip suppression? What can that typology tell us about its causes and needed solutions?
- February – June 2023
- Fielding window; timing matters
- High amount of trips missed in February
- Weather a huge factor February–April
- First: who misses trips?
- People with disabilities – 3x more likely
- Hispanic residents – 1.5x
- Women – 1.3x, and NOT explained by childcare
- Under 35
- Worker-only models
- Extreme commuting is a threshold – commutes over 50 km
- Telework – higher odds among workers teleworking 5+ days/week. More home-based work, more unmet travel – latent demand or selection?
- EVs don’t play a factor
- Density, land-use mix, and connectivity do nothing; transit job access nudges odds up
- How barriers relate
- Structural bundle
- Transit-centered: no transit, unsafe route, and travel time
- Weather stands apart – and is seasonal
- Travel time and vehicle cost work together
- What kinds of people – latent class analysis
- Weather/circumstantial – 31%, typically older, well-resourced
- Mobility access – 25%, disability and aging, no vehicle, mobility aids
- Time & route safety – 22%, younger, higher income, time and unsafe routes
- Structural exclusion – 11%, every barrier at once; core transport poverty or transport insecurity
- Vehicle cost – 11%, cars they can’t afford to run, lower incomes
- Takeaways
- Suppressed travel is common
- Who misses trips is structural and stable: disability, gender, ethnicity, and income
- Barriers bundle – transit-safety-time and cost-time
- Policy should match the profile: operating-cost help for car-owning, cost-burdened families
- For EV adoption – cost-burdened suppressed travelers are precisely where cheap-to-run vehicles could matter and where EVs are today
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