How Busy is Too Busy?

Authors: Jason Minser, Abt SRBI Tim Yeo, Abt SRBI Randal ZuWallack, Abt SRBI Mindy Rhindress, Ph.D, Abt SRBI Jonathan Ehrlich, Metropolitan Council KimonProussaloglou, Cambridge Systematics 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

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

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

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

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?

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 notan “essential task” for a household, if busyness is perceived, little to no recourse

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?

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

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

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

Busyness Classes Activities *Activity type breakdowns available

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

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

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

Logistic Regression Model

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”

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

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

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

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

Activity Breakdowns

How Busy is Too Busy?