Talking Freight: Establishment Surveys State and Local Experience Johanna Zmud Mia Zmud Chris Simek

1 NuStats, Austin, Texas – 12/ 10/ 08  Purposes  For modeling  For policy, decision-making  For improved understanding of freight movements  Sample Units  Drivers / Carriers  Shippers / Receivers (firms and households)  Database Contents  Vehicle characteristics, freight characteristics, driver characteristics, commodity type and quantity  Origins, destinations, routes traveled, stops, mode shares, travel times and distance  Satisfaction, attitudes, opinions State and Local Freight Surveys

2 NuStats, Austin, Texas – 12/ 10/ 08  Defining the universe and survey population  Adequacy of sampling frames and coverage errors  Sample size calculation Sampling Challenges

3 NuStats, Austin, Texas – 12/ 10/ 08  Complexity and extent of data elements  Limitations to respondent knowledge  Specificity of data required Instrument Development Challenges

4 NuStats, Austin, Texas – 12/ 10/ 08 Data Collection Challenges FactorsSolutions Schedule / Budget Objectives Nonresponse Privacy, Safety Operator Distributor Establishment

5 NuStats, Austin, Texas – 12/ 10/ 08  Policy-making  Value of Time Survey of Shippers, Georgia I-75  Modeling  Commercial Vehicle Travel Diary Survey, Phoenix  Understanding Freight movements  NYS DOT Commercial Vehicle Driver Survey Example Projects

6 NuStats, Austin, Texas – 12/ 10/ 08  Truck-only Toll (TOT) Lane Study  Assess opinions of shippers and drivers that use corridor regarding TOTs  Determine pricing structure for TOT  Universe  Commercial Users of the I-75 Corridor  Data  Descriptive and Preference (VOT) #1 Value of Time Survey of Shippers

7 NuStats, Austin, Texas – 12/ 10/ 08  Universe  Trucking companies that contain transport vehicles with 4 or more axles that operate on the corridor  Dual Sampling Frame  FMCA Commercial database – subset of carriers in Georgia, Alabama, Florida, South Carolina, Tennessee, and North Carolina (N=8409)  Database developed in-field during operator survey (N=215)  Instrument  Screening (recruitment)  Attitude / opinion, Trips, Stated Preference  Data Collection  CATI – 176 completed interviews  Web – 156 completed interviews Shipper Survey Methods

8 NuStats, Austin, Texas – 12/ 10/ 08  Lack of statistical control  Sample from unknown population  Time-consuming  47% Noncontacts  An average of 10.3 contact attempts per CATI complete  CATI length: 13.4 minutes  Web application after-the-fact to enable shippers to participate on their own time  Web length: 14.6 minutes  Overcoverage of sampling units in FMCA database  43% of sample records were not qualified to participate in survey  Nonresponse  32% response rate  40% refusal rate Shipper Survey Challenges / Lessons

9 NuStats, Austin, Texas – 12/ 10/ 08  Purpose  Recalibrate Maricopa Association of Governments (MAG) truck model to reflect emerging travel realities and address new planning challenges  Survey to provide data for the model update  Approaches  Trip Diaries  Operator Surveys  Service Truck Activity #2 Commercial Vehicle Travel Diary Survey

10 NuStats, Austin, Texas – 12/ 10/ 08  Universe  Firms in modeled area that own and operate trucks (FHWA Class 5 and larger; two axel-six wheels)  NAICS: mail/parcel, local pickup and delivery, construction, retail, for-hire  Sampling Frame  MAG Employer Database (N=11,652)  Probability sample stratified by number of employees  Instrument  Screening (eligibility & recruitment)  Diary: Driver information, Truck information, Trip information Travel Diary Survey Methods

11 NuStats, Austin, Texas – 12/ 10/ 08  Incidence  Types and number of trucks, firms often performed distribution-related delivery services (warehouse distribution)  Supplemental Frames: FleetSeek, ATA Fleet Directory, US Data Corp.  Non- Contacts / Qualified Sample Slowed Recruitment  Research updated numbers; 15 call attempts  In-person visits used to boost recruitment  Multiplicity in-field sampling  Diary Retrievals  Retraction of agreement to participate  Low participation by truck drivers (Spanish version necessary)  Extend data collection from 4 to 8 weeks to allow for temporal effects  Nonresponse  21% response rate, 66% refusal rate Travel Diary Survey Challenges / Lessons

12 NuStats, Austin, Texas – 12/ 10/ 08  Truck Drivers at NYSDOT Rest Areas, NYSTA Travel Plazas, Private Truck Stops  Strategic planning study  Supplement Transport Canada interviews at CA/NY border  Purposes: Facility locating, assess parking shortage, commercial vehicle routing, placement of NYSDOT traffic counters, etc. #3 Commercial Vehicle Driver Survey

13 NuStats, Austin, Texas – 12/ 10/ 08  Universe  FHWA vehicle class 8-13  30-total sites, with two days of collection at each  Instrument  Tablet PC with used to collect detailed information from over 1,000 truck drivers  Real-time geocoding and route verification  Data Elements  Truck, freight, facility characteristics  Driver attitudes and opinions regarding parking availability  Reasons why they stopped at this facility  Route choice Driver Survey Methods

14 NuStats, Austin, Texas – 12/ 10/ 08 Driver Survey Instrumentation

15 NuStats, Austin, Texas – 12/ 10/ 08  Logistical  Sites spread out across the state and, at times, separated by more than 100-miles. Lots of travel costs.  Need to coordinate with interviewers, state police, NYSDOT and NYSTA personnel, facility operators and traffic count contractors to ensure everyone knows schedule and expectations.  Survey Participation  Survey is long, and it can be difficult to keep drivers on track (participation rate high, but key was listening to them “vent”)  Good field staff and proper training one of the key’s to success. The more they know, the better driver response you will have.  Data Collection  Pilot is vital to success Driver Survey Challenges/ Lessons

16 NuStats, Austin, Texas – 12/ 10/ 08 Concluding Remarks  Overlap in challenges at national / state local levels  Solutions unique to information needs  Vehicle activity (travel patterns) is most often primary focus  Commodity flow has been less important  No single type (e.g., establishment, operator, distributor) or mode (e.g., intercept, telephone, web) meets needs at local level  The value of each is leveraged when used together  Wide variation in response rates and factors impacting response

17 NuStats, Austin, Texas – 12/ 10/ 08 Further Information  Johanna Zmud  Mia Zmud  Chris Simek