1. How Busy is Too Busy? Investigating the Participation of “Busy” Households in Metro Area Household Travel Surveys 14th TRB Planning Applications Conference 5-9 May 2013 Columbus, OH Authors: Jason Minser, Abt SRBI Tim Yeo, Abt SRBI Randal ZuWallack, Abt SRBI Mindy Rhindress, Ph.D, Abt SRBI Jonathan Ehrlich, Metropolitan Council Kimon Proussaloglou, Cambridge Systematics

2. Abt SRBI | pg 2 Household Travel Survey (HTS) Overview Sponsoring agencies include MPOs, DOTs, and other planning agencies Comprehensive inventory of households’ 24-hour travel Two phase study design Recruitment: Inventory of household, vehicle and person characteristics Follow-up: Inventory of individual household member travel for a 24 hour period Data used for travel demand forecasting

3. Abt SRBI | pg 3 Typical HTS Protocols Advance Letter (Unmatched only or Both) Advance Letter (Unmatched only or Both) Recruitment (Phone and/or Web) Recruitment (Phone and/or Web) Reminder to Travel (Phone and/or Mail) Reminder to Travel (Phone and/or Mail) Follow-up/Retrieval (Phone, Web, Mail) Follow-up/Retrieval (Phone, Web, Mail)

4. Abt SRBI | pg 4 Points of Response / Non-Response in HTS Advance Letter (Unmatched only or Both) Advance Letter (Unmatched only or Both) Recruitment (Phone and/or Web) Recruitment (Phone and/or Web) Reminder to Travel (Phone and/or Mail) Reminder to Travel (Phone and/or Mail) Follow-up/Retrieval (Phone, Web, Mail) Follow-up/Retrieval (Phone, Web, Mail) ? ? ? ? ? ? ? ?

5. Abt SRBI | pg 5 Factors Affecting Non-Response Rates Trust of sponsoring government agency/agencies Ability to reach household representative(s) Perceived importance of survey Burden of reporting Household composition Travel day specifics (e.g., day of week, planned activities) Busyness?

6. Abt SRBI | pg 6 What is Busyness? Is actual or perceived influencers that obstruct a household from reporting on their travel day Influencers could include, but not be limited to: Hours worked Types of activities Household composition Home ownership Presence of children Employment status Occupation status Filling out diaries is not an “essential task” for a household, if busyness is perceived, little to no recourse

7. Abt SRBI | pg 7 Importance of Understanding Busyness Helps transportation researchers: Determine the most effective corrective measures in order to improve study participation Better predict trip characteristics of non-respondents Evaluate possible correlation between busyness and quality of respondent-provided travel data Research Questions How do households’ travel days differ? What are they doing to be so “busy”? Who are these households? What do they look like? What we know from who responded, can we predict what kind of travel we missed?

8. Abt SRBI | pg 8 About the Data Address-based sampling – three tiered stratification by region, household size, and number of vehicles Multiple Methods Recruitment – phone and web Retrieval/Follow-up – phone, web, mail back 25,000+ households were recruited to participate in 24- hour travel diary (all persons 6 years of age or older) A total of 20 activities were available to choose from 14,000+ households returned travel diaries Households were randomly assigned a weekday and distributed evenly throughout the week

9. Abt SRBI | pg 9 What we did... Analyze activities (from trip diary) to measure busyness – busyness classes Associate busyness with household characteristics Predict busyness based on household characteristics Apply model to non-responding and responding households Compare busyness distribution for non-responding and responding households

10. Abt SRBI | pg 10 Busyness Classes Grouped household days into activity classes Latent cluster analysis (LCA) Multinomial model predicting class membership based on activity participation Examined 3-6 classes Chose 5 classes based on best model fit

11. Abt SRBI | pg 11 Activity Type* Busyness Classes HecticRoutineDay out All work, no funEasy day Work ↑↑↓↑↓ School ↑↑↓↓↓ Errands ↑↓↑↓↓ Recreation ↑↓↑↓↓ Family ↑↑↓↓↓ Busyness Classes Activities *Activity type breakdowns available

12. Abt SRBI | pg 12 Busyness Class HH Composition Hectic (15%)—larger households, high percentage with older children, with an average of 19 HH trips Routine (10%)—larger households, high percentage with younger children, with an average of 10 HH trips Day out (25%)—high percentage of retirees, married, with an average of 9 HH trips All work, no play (21%)—Smaller household size, low percentage with children, with an average of 8 HH trips Easy day (29%)—high percentage of retirees, singletons, with an average of 3 HH trips

13. Abt SRBI | pg 13 Day of Week Is day of the week driving busyness class? Reference day in M-F Distribution of days for each class is similar Mondays slightly over-represented HecticRoutineDay out All work, no funEasy day Monday23.727.025.227.828.3 Tuesday21.224.419.421.720.2 Wednesday19.117.318.617.917.7 Thursday18.615.916.917.817.6 Friday17.415.420.014.816.2 100%

14. Abt SRBI | pg 14 Predicting Busyness Anybody can have a day like any of these, but there are household characteristics that we can use to predict busyness Build a model to estimate the probability of having a hectic day, routine day, day out, an “all work, no fun day”, or an easy day

15. Abt SRBI | pg 15 Logistic Regression Model HecticRoutineDay out All work, no funEasy day Kids between 6-17xxxx Kids under 6xxxxx Own housex Number of driversxxx Number of vehiclesxxxx Presence of a person with a disabilityxxxx Homemaker present in the housexxxx At least one person telecommutes one or more times a weekxx Number retired in hhxxx Number employed full time in hhxxx Number unemployed in hhxxx Max education attainment in hhxxxxx Min education attainment in hhxxx Youngest adult in hhxxx HH Typexxxx

16. Abt SRBI | pg 16 Logistic Regression Model, cont’d Logistic regression model provides household probabilities of having each class of day Use probabilities as weights: i.e., HH 1 would count more toward “Day out” and “Easy day”; HH 2 would count more toward “Hectic” and “Routine” HH 1HH 2 Hectic10%65% Routine15%25% Day out40%3% All work5% Easy day30%2%

17. Abt SRBI | pg 17 Logistic Regression Model, cont’d Sum of the probabilities for the responding households = estimated distribution of days Applied model to non-responding households Busyness is clearly driving non-response for at least some households HecticRoutineDay out All work, no funEasy day 14.7%10.0%25.4%20.7%29.3% 19.2%11.6%21.5%22.0%25.7%

18. Abt SRBI | pg 18 Discussion Stark differences between busyness classes Trip making Number of trips Large households are indeed the busiest Especially when older children are present Seniors are primary demographic in Day out and Easy day Represents two leisure day types: active and less active Both are overrepresented No universal truths in this group Busyness is obstructing participation in at least some households Missing these households is driving down trip totals Better consideration given to how much we ask households to tell us about their day

19. Abt SRBI | pg 19 Next Steps Apply busyness model to other regional HTS data to look for differences and similarities Apply model to population statistics to pre-determine potential make-up of travel prior to fielding Examine the impact on the recruitment survey Offer incentives based on multiple characteristics of households Identify data reporting issues across the different classes

20. Abt SRBI | pg 20 Contact Information Jason Minser Abt SRBI j.minser@srbi.com

21. Abt SRBI | pg 21 Activity Breakdowns