January Utah Statewide Household Travel Study Study overview and results

Utah Travel Study $1.8 M Sponsoring agencies o Wasatch Front Regional Council o Mountainland Association of Governments o Cache Metropolitan Planning Organization o Dixie Metropolitan Planning Organization o Utah Transit Authority o Utah Department of Transportation

3 Purpose of the Utah Travel Study o Better understand daily travel activities & travel patterns of Utah residents o Serve as the basis for estimating future travel activities o Vital for planning future transportation needs

4 Random sample of 124,888 Utah households invited Invitations via first-class mail Option to complete survey: ‒ Online (advanced web-based survey instrument) ‒ Telephone Survey in English & Spanish $10 Amazon gift card incentive Participants identified travel on one of 33 pre-assigned days Days assigned were on Tue, Wed or Thu Sampling Plan

5 2,800Salt Lake Co 1,556Utah Co 112Wasatch Co 173Summit Co Number of Completed Household Surveys 80% of households who completed the travel survey volunteered to do further surveys

6 o Basic household information Life cycle Household size # and type of vehicles available # worker Income o Log of all trips made over a 24-hour period Trip purpose (work, shop, school, etc.) Origin & destination Mode & auto occupancy (whether or not they traveled with other people) Time of day (AM, midday, PM, night) Type of Information Collected

7 Example Trip Diary Questions - 1

8 Example Trip Diary Questions - 2

9 General Findings o Person travel behavior has been relatively consistent since 1993, even with: High population growth Land use changes (e.g. Gateway-type development did not exist in 1993) Major transportation investment (TRAX, Front Runner, I-15 reconstruction) o 2012 results will allow us to ‘true-up’ existing forecasting models to current data o 2012 survey data help us better understand interactions between urbanized and emerging areas (e.g. Utah County and Wasatch and Summit Counties)

Transportation Ranking Question  Preferences suggest focusing on current facilities PriorityCacheDixieMAGWFRCRuralTOTAL Efficiency* Maintenance/ Preservation* Safety* Mode Choice Capacity Note: *The rankings for these priorities are a virtual tie in the data PriorityCacheDixieMAGWFRCRuralALL Efficiency* Maintenance/ Preservation* Safety* Mode Choice Capacity High Priority Low Priority

Statewide Attitudes: Taxation  Stark Regional Differences Regarding Taxation  “I would be willing to pay higher taxes in order to build a transportation system that resulted in less traffic congestion”  Net Willingness to Pay Taxes – Difference between Agree/Disagree Note: *UDOT = rural counties not included in the other regions

MAG Attitudes: Utah Lake Crossing  Attitudes are uniformly distributed  “I support a road or a bridge across Utah Lake to connect our growing population (west of Utah Lake) to the city centers (east of Utah Lake)”

Attitudes: Driving on Bad Air Quality Days  “I try to carpool, ride transit, and otherwise reduce my driving habits during bad air quality days”

Travel Diary: Bike/Ped Travel The main diary dataset has approximately 9,000 bike/ped trips in it, which should provide insights into the relationship between urban form and non-motorized travel. Trip Purpose for Walk/Bike Trips in Utah County

Potential Model Enhancements: Bike/Ped & Urban Form The red dots are home- based other trips (e.g. exercise, visiting neighbors, walking the dog). The blue dots are home- based work trips. Clearly there are stark differences in where these trips occur.

Mobile Phone Tracking Data - AirSage 16 To complement the Household Travel Diary AirSage wireless data was purchased to give a more robust sample of origins and destinations (more refined geography)

AirSage District-to-District Validation o 148 Districts statewide — 50 Wasatch Front — 21 Cache — 20 Dixie — 20 Urban Fringe — 30 Rest of rural Utah o 2 months of Data (Jan & Apr 2012) o Trace trips where Interstates cross Utah boarders o Use in conjunction with 2012 Survey to validate spatial distribution in models