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Talking Freight: Establishment Surveys. State and Local Experience. Johanna Zmud Mia Zmud Chris Simek. State and Local Freight Surveys. Purposes For modeling For policy, decision-making For improved understanding of freight movements Sample Units Drivers / Carriers - PowerPoint PPT Presentation
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Talking Freight:
Establishment Surveys
State and Local Experience
Johanna Zmud
Mia Zmud
Chris Simek
2 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
3 NuStats, Austin, Texas – 12/ 10/ 08
Defining the universe and survey population
Adequacy of sampling frames and coverage errors
Sample size calculation
Sampling Challenges
4 NuStats, Austin, Texas – 12/ 10/ 08
Complexity and extent of data elements
Limitations to respondent knowledge
Specificity of data required
Instrument Development Challenges
5 NuStats, Austin, Texas – 12/ 10/ 08
Data Collection Challenges
6 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
7 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
8 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
9 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
10 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
11 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
12 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
13 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
14 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
15 NuStats, Austin, Texas – 12/ 10/ 08
Driver Survey Instrumentation
16 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
17 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
18 NuStats, Austin, Texas – 12/ 10/ 08
Further Information
Johanna [email protected]
Chris [email protected]