1. VMT Reduction Programs: Time for a Change? Stacey Bricka, PhD, NuStats sbricka@nustats.com 12 th TRB Planning Applications Conference Products of Your Environment May 18, 2009 This dissertation research was funded wholly or in part by the United States Environmental Protection Agency (EPA) under the Science to Achieve Results (STAR) Graduate Fellowship Program. EPA has not officially endorsed this publication and the views expressed herein may not reflect the views of the EPA

2. Research Objectives This research seeks to identify the factors that influence trip chaining in order to better understand the policy implications of this travel behavior pattern with regard to VMT reduction programs. 61% of all working age adults trip chain This 61% generates 68% of avg daily VMT Most trip chaining is done by automobile

3. Definition of Trip Chaining A sequence of trips bounded by stops of 30 minutes or less. -McGuckin and Nakamoto 2004 (page 1) HomeDaycareWorkDaycare Grocery Store HomeWorkHome

4. Prevailing Trends* Increase in Working Women Changing Household Structures ◦ Proportionately fewer Nuclear Families ◦ More Single Parent and Single Person HH Increase in Immigrants (“instant commuters”) Growth in Automobile Travel ◦ All other modes declined in numbers and in share 11% Increase VMT from 1995 to 2001** *Alan Pisarski, Commuting in America III **Center for Urban Transportation Research

5. Prevailing Hypotheses 62% of Working Women 71% Non-Working Women 67% Non-Working Men 54% Working Men Working Women Larger HH (More Kids) Higher # of Escort Trips per Day 66% of Adults w/ Kids 57% of Adults w/ No Kids Presence of Children

6. Research Questions 1. What are the factors that influence trip chaining? 2. What are the implications of trip chaining for employer-based VMT reduction programs? Hypothesis: Trip Chaining is a function of WHO the traveler is and WHERE the traveler lives.

7. Research Framework Proportion of Trips Chained Destination Choices Transportation Options Rules & Hours of Operation Activity Setting Commuter Characteristics HH Factors Work Factors Demographic Factors Societal Expectations

8. TRIP CHAINING INFLUENCERS

9. Data 2001 National HH Travel Survey ◦ National Sample Only ◦ Working Age Adults (18-65) ◦ Reported Weekday Travel ◦ N=24,626 2000 Census ◦ # Employees per Industry per Tract ◦ Proxy for Land Use

10. Market Segmentation Analytical Approach - Triangulation ◦ Logit Model ◦ Automatic Interaction Detection (AID) ◦ Factor Analysis Results ◦ Workers – no Kids ◦ Workers – w/ Kids ◦ Non-Workers – no Kids ◦ Non-Workers w/ Kids

11. Segmentation Findings Workers w/ Kids (63%) + Females - # Adults Workers/0 Kids (54%) + Education - # Adults Non-Workers w/Kids(74%) + Female + # Kids Non-Workers/0 Kids(66%) + Middle Age (35 to 44) - # Adults

12. TRIP CHAINING IMPLICATIONS

13. Time for a Change? Employer-Based Programs Commuters Reduce VMT through Mode Shifts Assumes “Typical Commute” Assumes Flexibility to Change Mode of Travel Focus only on Travel Time and Costs Significant Time Constraints Fitting Non-Work Travel into Commute No Flexibility in Travel Mode

14. Estimate of Reach 174 million Working-Age Adults Employer-Based Programs = Employees Only  63 million non-workers excluded 111 million workers ◦ 42% have traditional commute ◦ 30% live in households with 1+ Adult and 1.0+ veh/wrkr ratio 33.3 to 46.6 million targets for programs  64.4 to 77.7 million workers not targets for programs

15. Options Refocus Existing Programs Consider Broader Focus

16. Refocusing Current Program Eliminate trips for ALL workers, regardless of work mode VMT Elimination Strategies ◦ Eliminate Trips ◦ Main Tool = Employer Amenities ◦ Food Service, ATM, Postal Services, Day Care, Convenience Stores

17. Broaden Focus Shift to Household Focus Benefits ◦ Capture 174 million working age adults (100%) ◦ Recognize differences across and within segments Reach via Targeted Marketing ◦ Explicit recognition of household composition ◦ Tailor based on age of youngest child ◦ Educate on VMT reduction techniques and Mode Options Current Programs ◦ TravelSmart (Australia) ◦ SmartTrips (Portland)

18. Conclusions Trip Chaining Influencers ◦ Vary based on traveler and activity setting characteristics ◦ Household and demographic characteristics primary ◦ Differences within segments ◦ More influencers to be identified Trip Chaining Implications ◦ Some market viability for employer-based programs ◦ Stronger results if focus on households not employers

19. Thank you! Questions and Comments? sbricka@nustats.com