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