National OD products will be made available for public use at no cost to data users.

Item Date
2020 National Passenger OD Data 7/16/21
2020 National Passenger Quality Memo 7/16/21
2020 National Passenger Data Dictionary 7/16/21
2020 National Truck OD Data 7/16/21
2020 National Truck Quality Memo 7/16/21
2020 National Truck Data Dictionary 7/16/21
2020 Pooled Fund Package 8/31/21
2021 National Passenger OD Data 5/13/22
2021 National Passenger Quality Memo 5/13/22
2021 National Passenger Data Dictionary 5/13/22
2021 National Truck OD Data 5/13/22
2021 National Truck Quality Memo 5/13/22
2021 National Truck Data Dictionary 5/13/22
2021 Pooled Fund Package 6/30/22
2022 - 2024 Data Products To Be Determined

 

The table below shows the minimum data quality specification for national OD products of this program.

Selected Performance Metric Validation Data or Method Target
Trip rates and trip totals NHTS core survey and/or ACS Same trend and ±10%
Vehicle miles traveled (car and truck) HPMS Same trend and ±10%
Air passenger trips DB1B, T100 Same trend and ±10%
Rail transit trips NTD Same trend and ±10%
NHTS core survey and OD data consistency Annual coordination meeting with NHTS core survey contractor TBD
Data product reasonableness check Cross tabulation between mode share, trip distance, and trip purpose, etc. Consistency and reasonableness in cross-tabulated data
Extreme values Extreme value examination and logical reasonableness check No extreme value or logical reasonableness issue in final products
Additional pooled fund program OD data quality metrics State and local travel surveys, small area estimates from NTD, HPMS, NHTS, FAF, etc. Similar to national OD targets or customized targets

 

Ensuring Data Quality

  • A high-quality, consistent, and robust passenger travel “raw data panel” that fuses data from multiple mobile device data providers.
  • Comprehensive truck travel data from both ATRI and INRIX.
  • Our advanced methodology and rigorous OD data validation procedure lead to high product quality:
    • Home and work location identification and worker type identification algorithms;
    • Tour-based methods for trip identification;
    • Machine learning methods for mode and purpose imputation;
    • Trip distance estimation based on observed transportation network distance;
    • Multi-level weighting with device and trip level weights;
    • Comprehensive algorithm validation at both individual and aggregate levels; and
    • Multiple internal/external OD product QA/QC steps, guided by a rigorous validation plan.

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