1. Transportation leadership you can trust. presented to TRB Planning Applications Conference Houston, Texas presented by John (Jay) Evans, P.E., AICP Cambridge Systematics, Inc. May 18, 2009 Exploring Travel Preferences of Transit and Shared Ride Users in a Proposed HOV to HOT Conversion

2. 1 Presentation Outline Introduction Methods Findings Conclusions

3. 2 Introduction I-95/I-395 HOT Project 56 miles of I-95/I-395 Corridor from Massaponax, Virginia to Washington, D.C. Expansion of existing 2-lane HOV 3+ facility to 3-lane HOT facility Construction of new 2-lane HOT facility from current HOV terminus in Dale City to Massaponax Explore likely travel demand response, especially transit and carpool usage changes HOV = High-Occupancy Vehicle HOT = High-Occupancy/Toll 3+ = 3 or more occupants required

4. 3 Introduction Demand Assessment Objectives Two frameworks were applied, each with specific objectives 1.Regional forecasting framework Forecast mode choice and highway facility usage under the different alternative scenarios Use best available regionally accepted tools for the job −MWCOG Regional Forecasting Model (Version 2.1D#50) −WMATA Nested-Logit Mode Choice Post-Processor Model −FAMPO Subzone Highway Assignment Post-Processor MWCOG = Metropolitan Washington Council of Governments WMATA = Washington Metropolitan Area Transit Authority FAMPO = Fredericksburg Area Metropolitan Planning Organization

5. 4 Introduction Demand Assessment Objectives 2.Consumer market research Profile current travel patterns by mode in the corridor Measure current level of awareness, familiarity, and beliefs regarding HOT lanes Assess propensity of commuters to change commute behavior in response to HOT lane availability Identify relative appeal of specific enhancements and programs (transit/TDM alternatives) needed to be in place to increase the likelihood of using non-SOV modes SOV = Single-Occupant Vehicle TDM = Transportation Demand Management

6. 5 Presentation Outline Introduction Methods Findings Conclusions

7. 6 Methods Regional Forecasting Framework Three alternatives tested with different intensities of transit service enhancement Refined alternative also tested – represented specific level of transit enhancement investment Parameters for HOT lanes checked and adjusted to ensure forecasted conditions met policy standards Mode choice summaries reviewed for traveler response

8. 7 Methods Consumer Market Research – Overview Online survey instrument Questionnaire elements Scaled attitude and opinion questions Open-ended questions Scenario testing −HOT lane price points and time savings scenarios to explore mode choice

9. 8 Methods Consumer Market Research – Sample Residents (SOV and other modes): Mailed 75,000 survey invitation postcards with unique codes to residents in study area Carpools: Emailed survey invitation to registrants of region’s GRH database living in study area and mailed postcard invitations to other carpool lists Vanpools: GRH database and mailing invitations to available lists of vanpool drivers who originate from study area Sluggers: Some slugs entered via resident postcard mailing and others through announcement on slug-lines.com Bus: Emailed survey invitation to list provided by major public provider and other bus riders participated via postcard mailing VRE: Posted survey invitation in VRE’s electronic newsletter Each mode was targeted specifically GRH = Guaranteed Ride Home VRE = Virginia Railway Express

10. 9 Methods Consumer Market Research – Sample

11. 10 Presentation Outline Introduction Methods Findings Conclusions

12. 11 Findings Regional Forecasting Framework Ridership levels varied with transit service levels Largest changes were observed among submode results (e.g., bus, commuter rail, Metrorail) Overall impacts of new transit investment muted due to baseline high transit service levels Over 140 buses per hour using the facility inside the Beltway Over 65 buses per hour using the facility near Springfield Low occupancy vehicle mode share predicted to increase above 2000 level in 2015 horizon, but falls below 2000 in 2030 horizon with increased congestion levels HOT lane introduction did not severely impact transit and carpool usage

13. 12 Findings Consumer Market Research Analyses performed Profiles of commuters within mode groups HOT lane awareness, perceptions, and usage Transit and TDM improvement preferences and usage Mode and facility choice under scenario testing

14. 13 Findings Consumer Market Research – Commuter Profiles Commuter profiles developed for each mode segment Trip start times and locations −Vanpools had the earliest start times Trip lengths −Average SOV commute 24 miles (48 minutes) −Average bus commute 29 miles (64 minutes) −Average vanpool commute 48 miles (64 minutes) Modes, services, and facilities used Regularity of commute −Over 80% of non-SOVs use the HOV facility five days a week Demographics of respondents Confirmation of corridor-specific behavior

15. 14 Findings Consumer Market Research – Commuter Profiles Sluggers are most likely to use a different mode for their afternoon commute

16. 15 Findings Consumer Market Research – Commuter Profiles Most often, morning sluggers who do not slug home in the afternoon use an alternate mode because slug lines are not available or convenient in the afternoon

17. 16 Findings Consumer Market Research – HOT Perceptions Awareness of the HOV lanes was universal Awareness of the HOT lanes project was lower, but still quite high (ranging from 76% among SOVs to 94% among sluggers) Most respondents did not think HOT lanes would allow traffic to flow faster, allow commuters to save time, or create new transit or carpool opportunities Commuters from Prince William County tended to view the HOT lanes more negatively – especially carpoolers and sluggers. Spotsylvania/Stafford residents were more likely to see the positives – especially vanpoolers

18. 17 Findings Consumer Market Research – HOT Perceptions A majority of respondents did not agree that HOT lanes would encourage slugging, especially current sluggers Question asked of half of the survey respondents

19. 18 Findings Consumer Market Research – HOT Perceptions Sluggers tended to believe that drivers would pay the toll to use the HOT facility rather than pick up sluggers Question asked of half of the survey respondents

20. 19 Findings Consumer Market Research – HOT Usage Stated interest in using the HOT lanes was highest among vanpoolers (64%) and HOV 3+ carpools (51%) and lowest among Metrorail riders (9%) Question asked of all survey respondents

21. 20 Findings Consumer Market Research – HOT Usage Expressed likelihood of using the HOT lanes was highest among commuters from Spotsylvania and Stafford Counties Longest distance commutes to the core Markets currently without HOV lanes Strongest intent was expressed among those whom would be toll-free users (i.e., carpoolers, vanpoolers, etc.) Expressed frequency of use (without reference to the actual toll) was highest among toll-free users 50% of SOV would use HOT lanes four or five days a week Compare with 91% of vanpoolers, 88% of sluggers, and 86% of current HOV 3+ carpoolers

22. 21 Findings Consumer Market Research – HOT Usage SOV users expressed the greatest interest in changing their commutes with the introduction of HOT lanes 53% would not change their commute in any way 30% would pay to use the HOT lanes occasionally 8% said they would change their mode to use facility free Some had other plans (shift times, ride motorcycle, etc.) 95% of vanpoolers, 91% of bus riders, 88% of HOV 3+ carpoolers, and 82% of sluggers said they would not change their commute in any way

23. 22 Findings Consumer Market Research – Choice Modeling Utilized preference data from scenario tests Customized questions based on prior answers Added “realism” to scenarios Current commute patterns and choice experiments Time savings – 5 to 20 percent of current total travel time (capped for HOV and transit) Randomly generated cost of $0.08 to $0.50 per minute of time savings presented as a total price 3-4 choice experiments per respondent

24. 23 Findings Consumer Market Research – Choice Modeling Drive-alone respondents Pay to use HOT lane Switch to an HOV mode to use HOT lane* Continue to use regular lanes Other Non-drive-alone respondents Switch to drive alone and pay to use HOT lane Switch to drive alone and use regular lanes Continue to use HOV mode Other * A follow-up question was asked regarding HOV mode chosen Mode Choices Available Developed post-processor mode shift model

25. 24 Findings Consumer Market Research – Choice Modeling

26. 25 Findings Consumer Market Research – Choice Modeling Binomial logit model specification

27. 26 Presentation Outline Introduction Methods Findings Conclusions

28. 27 Conclusions Apparent paradox in traveler opinion about what other travelers would do given the introduction of HOT lanes as compared with their own personal preferences Most current shared-ride commuters expressed concern that HOT lanes would damage usage of transit, formal carpooling, and, especially, slugging But, most continued selecting their current mode when offered scenarios incorporating the cost and benefits of using the lanes for their current commute (including values tailored around their experiences) Most said they would not change their own commute even without cost and benefit information

29. 28 Conclusions Survey findings and regional modeling suggested that the concerns about dramatic shifts in traveler behavior in the corridor were less of an issue than originally thought Confirmed that if enhanced shared-ride and transit services were provided, there would continue to be large volumes of such users Consumer market research element provided additional information to planners and elected officials than would have been otherwise available

30. 29 Conclusions More could be done with the collected data Further modeling work identified Alternate model specifications −Multinomial logit −Inclusion of more variables Market segmentation −Socioeconomic −Geographic −Response group Transferability −Comparison of model parameters across areas could enhance understanding of potential for transferability of findings

31. 30 Acknowledgements Tanya Husick and Corey Hill Virginia Department of Rail and Public Transportation Karen Smith Southeastern Institute of Research Laura McWethy and Kimon Proussaloglou Cambridge Systematics Questions? John (Jay) Evans, P.E., AICP Cambridge Systematics, Inc. 4800 Hampden Lane, Suite 800 Bethesda, MD 20814 (301) 347-0100 jevans@camsys.com End Notes