1 ODOT, Greg Giaimo and Rebekah Anderson OKI, Andrew Rohne Laurie Wargelin, Abt SRBI, Prime Peter Stopher, PlanTrans, GPS Kevin Tierney, Cambridge Systematics, Sampling Sharon O’Connor, Resource Systems Group, Internet The Greater Cincinnati Area Large-Scale (100%) GPS-Based Household Travel Survey

2 Introduction Quoting from the RFP Research Objectives.. “This is the first large scale GPS-based survey conducted in the United States, and therefore, beyond the various logistical issues, it is uncertain to what extent a GPS-based survey is able to capture all the information available in a diary-based survey. “ This presentation describes the processes used for the Spring 2009 GPS survey pilot, and discusses early findings.

3 Goals...  Reduce substantial respondent burden inherent in traditional travel diary recordings  Reduce under-reporting of trip data  Increase representative response rates  Provide the detailed geographic information on route, speed, and location not captured by traditional diary methods that can influence the way travel is modeled

4 Sample Design  Sample Size – Record three days travel -- Complete a minimum of one-day trip records for all members of 4,000 households. No diary recordings (if over age 12)--GPS RECORDING ONLY.  Address-Based Sampling - so that cell-only households are included. Exclusion of these households is an increasing problem with traditional RDD sampling. Estimates of cell-only households are 15-30% depending on metropolitan area.

5 Phone “Matched” and “Un-Matched” Sample with Address-Based Sampling  The randomly selected address-based sample is from US Postal Service delivery sequencing files.  Sample addresses are matched with known land based phone numbers. (For the pilot, 60% of addresses were matched with a land based phone number–-about the same as reverse matching for RDD sample.)  Addresses without known phone numbers (“Un- matched”) consist of households with unlisted numbers, no phone, or increasingly of cell phone-only households (total 40%).

6 Recruitment With an Address-Based Sample

7  Address-based sample allows oversampling of transit access areas and University off-campus areas (student households) by census block group— not fully possible with RDD sampling.  PUMS data (aggregated by block group) can be used for monitoring recruits by combinations of household size, # of workers, # of vehicles, and HH lifestyle cycles (student, with/without children, retired).  Advance letters can be sent to all sample households—not possible with an RDD sample. Other Advantages of Address-Based Sampling

8 Survey Design- GPS Capabilities  Personal GPS units so that all travel, not just vehicle trips, are recorded. Can be carried in a pocket or purse, or clipped on a belt or a wrist band.  Goal of recording three days of travel.  Every member of the household 13 years and older carries a GPS unit for three days.  The GPS devices will be deployed over a one year time period beginning in July of 2009.

9  Besides GPS units for all over 12, we collect limited children’s activities and travel information in a diary format (to link with other household members’ travel). Objective is to reduce burden and meet child privacy concerns.  Six-minute phone or Internet recruitment interview-- three-day travel periods assigned.  Short-form household and person information forms distributed and collected with GPS units. Forms collect: (1) work and school locations, (2) two most frequent household shopping locations, and (3) GPS usage status for each member, each day. Survey Design – Pilot Implementation

10 Short-Form Materials Piloted with Deployment GPS Units

11 Goal is to develop an efficient (low cost) GPS data collection process:  GPS unit and forms packages sent by Fed Ex  GPS units return methods piloted: (1) Participants provided with pre-paid shipping packages that can be deposited in either Fed Ex or US Postal Service drop boxes (2) Call the project number to arrange a Fed Ex or personal courier pick-up. (3) Follow-up phone calls and Internet reminders to arrange courier pick-ups as necessary. Survey Design – Pilot Implementation

12 GPS Data Imputation and Verification – PlanTrans Processing Methodology  Imputation of Trip Ends and Mode - Using a set of rules that include movement of the GPS for 2 minutes or more-or lack of movement, or a significant change in speed, indicating a different mode being used.  Prompted Recall (PR) Verification - Return of respondents’ travel (in Google Map form) in a web- based format for verification. Detailed ability to correct travel and purpose information, and add travel cost (fare, driving and parking costs) and vehicle occupancy for each trip.

13 Prompted Recall Web Format

14 Survey Design - GPS Data Processing and Imputation  PlanTrans imputes purpose using the frequency and duration of visits, the match to one of the collected addresses (home, workplace, school, frequent shops), and to available GIS land use data.  PlanTrans is also developing an additional rule-based procedure for occupancy by family members by matching trips from different family members by time, location, and mode.  With the aid of the prompted recall, Artificial Intelligence software is being trained, and these results will be applied to rule-based software. In this process, PlanTrans will attempt to add the capabilities to impute occupancy, driver/passenger status, and possibly parking costs and bus fares.

15 Pilot Sample Plan Designed to Test Response Rates and Incentives  Equal sample for Higher Transit Access and Lower Transit Access geographic areas  Equal sample for Phone Matched and Non- Matched Sample  Matched Sample offered $0 or $10 incentive to complete  Non-Matched Sample offered $10 or $25 incentive to complete

16 Pilot Sample Results % %

17 Pilot Sample Results- Incentives Matched Sample Un-Matched Sample % %

18 Pilot Sample Results- by Income Overall Completes to Recruits by Incentive % %

19 Processing and Verification of GPS Data Files for Pilot PlanTrans Processes the GPS Data Files Twice:  First for the Prompted Recall Survey  After the Prompted Recall Survey: Deletions or additions are made to fix trips Mode of travel is rechecked and identified for each trip Purpose of trip is rechecked and identified  Trip File is Created  Monthly or Bi-Monthly Completed Data is Delivered to the Client for Rechecking

20 Pilot Results - Address-Based Phone Match vs. Non-Match  Internet was the most viable means of obtaining recruits from households without land-based phones.  Additionally, 19% of recruits from the phone matched sample responded to the advance letter by completing the recruitment on-line.  Only one phone number was obtained from the unmatched sample via a return postcard/reply to a hot button issue survey.  Regardless of recruitment method, completion rates for matched and unmatched sample were equivalent – once recruited.

21 Demographics of Pilot Internet Responders  The Advance Letter to the Un-Matched address-based sample (households without known land phones) was successful at recruiting a substantial percent of households to the GPS-Based Survey via the Internet.  This was particularly true for younger age group households (18 to 34 years old) with only cell phones. These households are typically under-represented in traditional Household Travel Surveys.  As would be expected, there were also a higher number of student households in this group.

22 Demographics of Pilot Internet Responders - Age Matched Phone Recruits Matched Internet Recruits Un-Matched Internet Recruits Contact Person Years Old 22%17%58% Almost Entirely Cell- Only Households These households are typically under-represented in Diary Household Travel Surveys and subsample GPS surveys also show their trips and tours to be under-reported.

23 Pilot Representativeness of Completed Households  A very representative sample was recruited and completed by HH Size. The requirement that all household members age 13 or older carry GPS units did not prove to be a “respondent burden” barrier.  A representative sample was completed by number of vehicles. However, while an appropriate percent of zero vehicle households were recruited, extra effort (incentives?) will be needed to complete zero vehicle households.

24 Pilot Representativeness of Completed Households

25 Pilot Representativeness of Completed Households – Con’t  The completed pilot sample was fully representative by lifestyle/family type.  The higher percent of households with zero workers was not due to oversampling of retirees. May be attributable to current economic conditions.  The pilot was successful at recruiting low income households, but incentives/extra effort will be required to complete these households.

26 Pilot Representativeness of Completed Households – Con’t

27 Pilot Representativeness of Completed Households – Con’t

28 Primary Logistical Problems – Return of GPS Units and Some GPS Battery Outages  Retrieving GPS units in a timely manner for redeployment – with minimum loss - is a logistical and cost problem  Loss rate for pilot was 2.7 percent--mostly among low income/urban households.  More units needed, higher incentives, longer field time?  Battery outages over three days – need to supply chargers

29 ODOT GPS-Based Pilot Summary to Date  Address-based sampling can be successful in recruiting cell-phone only households to a GPS-Based HTS via an Internet recruit.  GPS household completion rates are adequate and representative.  Requiring every household member (over 12) to carry a GPS unit for three days was not considered an undue burden – paperwork was greatly reduced.

30 ODOT GPS-Based Pilot Summary to Date  The child diary needs to be kept simple - perhaps only one day travel is needed.  Significant incentives and additional efforts are needed to complete unmatched households, and households with low-incomes and/or zero vehicles.  Added trip accuracy reporting and the value of route and location with speed data (as collected via GPS) needs to be demonstrated upon completion of the pilot PR and trip files in early June 2009.