Will AI replace Transportation Planners?
In theory, AI could do about 52% of the work in Transportation Planners. In practice, as of late 2025, almost no one is actually using it that way — yet.
O*NET-SOC 19-3099
How your 21 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a moderate share of this job's tasks (~52%). By late 2025, real-world AI use had reached about 3% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
Don't trust a single AI-risk score here
For this job, the signals disagree sharply. AI's theoretical reach looks moderate (~52%), but real-world use is only ~3%, and how much AI "can" do shifts wildly by model — one 2026 study found the share of "high-risk" jobs swung 2.7% to 51.5% just by changing which AI did the rating. This page shows the spread instead of pretending there's one number.
See all 21 tasks, ratedBased on real task-level AI scores — click to collapse
- Prepare necessary documents to obtain planned project approvals or permits.
- Develop computer models to address transportation planning issues.
- Recommend transportation system improvements or projects, based on economic, population, land-use, or traffic projections.
- Define regional or local transportation planning problems or priorities.
- Design transportation surveys to identify areas of public concern.
- Interpret data from traffic modeling software, geographic information systems, or associated databases.
- Prepare reports or recommendations on transportation planning.
- Design new or improved transport infrastructure, such as junction improvements, pedestrian projects, bus facilities, or car parking areas.
- Analyze information related to transportation, such as land use policies, environmental impact of projects, or long-range planning needs.
- Collaborate with engineers to research, analyze, or resolve complex transportation design issues.
- Evaluate transportation project needs or costs.
- Collaborate with other professionals to develop sustainable transportation strategies at the local, regional, or national level.
- Analyze information from traffic counting programs.
- Develop or test new methods or models of transportation analysis.
- Prepare or review engineering studies or specifications.
- Review development plans for transportation system effects, infrastructure requirements, or compliance with applicable transportation regulations.
- Evaluate transportation-related consequences of federal or state legislative proposals.
- Produce environmental documents, such as environmental assessments or environmental impact statements.
- Direct urban traffic counting programs.
- Represent jurisdictions in the legislative or administrative approval of land development projects.
- 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.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.