Transportation Planners
Transportation & Material MovingAI exposure
- Data source: BLSPublished: 2026-08
Very high· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: 2026-03
0.033
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.47
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 23% score at or above this value.
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
Task-level exposure
| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Interpret data from traffic modeling software, geographic information systems, or associated databases.19-3099 | 0.00000.0 | 0.00000.0 |
Prepare necessary documents to obtain planned project approvals or permits.19-3099 | 0.00000.0 | 0.00000.0 |
Prepare or review engineering studies or specifications.19-3099 | 0.00000.0 | —0 |
Produce environmental documents, such as environmental assessments or environmental impact statements.19-3099 | 0.00000.0 | —0 |
Evaluate transportation project needs or costs.19-3099 | 0.00000.0 | —0 |
| Not observed on any surface — 17 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Direct urban traffic counting programs. | —0 | —0 |
Develop or test new methods or models of transportation analysis. | —0 | —0 |
Define or update information such as urban boundaries or classification of roadways. | —0 | —0 |
Analyze information from traffic counting programs. | —0 | —0 |
Review development plans for transportation system effects, infrastructure requirements, or compliance with applicable transportation regulations. | —0 | —0 |
Prepare reports or recommendations on transportation planning. | —0 | —0 |
Participate in public meetings or hearings to explain planning proposals, to gather feedback from those affected by projects, or to achieve consensus on project designs. | —0 | —0 |
Develop computer models to address transportation planning issues. | —0 | —0 |
Design transportation surveys to identify areas of public concern. | —0 | —0 |
Collaborate with engineers to research, analyze, or resolve complex transportation design issues. | —0 | —0 |
Recommend transportation system improvements or projects, based on economic, population, land-use, or traffic projections. | —0 | —0 |
Define regional or local transportation planning problems or priorities. | —0 | —0 |
Analyze information related to transportation, such as land use policies, environmental impact of projects, or long-range planning needs. | —0 | —0 |
Collaborate with other professionals to develop sustainable transportation strategies at the local, regional, or national level. | —0 | —0 |
Design new or improved transport infrastructure, such as junction improvements, pedestrian projects, bus facilities, or car parking areas. | —0 | —0 |
Evaluate transportation-related consequences of federal or state legislative proposals. | —0 | —0 |
Represent jurisdictions in the legislative or administrative approval of land development projects. | —0 | —0 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Develop computer models to address transportation planning issues.O*NET Task ID 16948 | 1.0 |
Prepare necessary documents to obtain planned project approvals or permits.O*NET Task ID 20979 | 1.0 |
Prepare or review engineering studies or specifications.O*NET Task ID 16934 | 0.5 |
Represent jurisdictions in the legislative or administrative approval of land development projects.O*NET Task ID 16935 | 0.5 |
Direct urban traffic counting programs.O*NET Task ID 16937 | 0.5 |
Develop or test new methods or models of transportation analysis.O*NET Task ID 16938 | 0.5 |
Define or update information such as urban boundaries or classification of roadways.O*NET Task ID 16939 | 0.5 |
Analyze information from traffic counting programs.O*NET Task ID 16941 | 0.5 |
Review development plans for transportation system effects, infrastructure requirements, or compliance with applicable transportation regulations.O*NET Task ID 16942 | 0.5 |
Prepare reports or recommendations on transportation planning.O*NET Task ID 16943 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page