National OD products will be made available for public use at no cost to data users.
- 2020 National Passenger OD Data
- 2020 National Passenger Quality Memo
- 2020 National Passenger Data Dictionary
- 2020 National Truck OD Data
- 2020 National Truck Quality Memo
- 2020 National Truck Data Dictionary
- 2020 Pooled Fund Package
- 2021 National Passenger OD Data
- 2021 National Passenger Quality Memo
- 2021 National Passenger Data Dictionary
- 2021 National Truck OD Data
- 2021 National Truck Quality Memo
- 2021 National Truck Data Dictionary
- 2021 Pooled Fund Package
- 2022 - 2024 Data Products
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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