Town and Traffic Planners

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Town and Traffic Planners (ISCO-08 2164) score an average of 0.41 on a 0–1 exposure scale — more exposed than about 78% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Minimal band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Planning and advising on routing and control of road traffic and public transportation systems for efficiency and safety.”

Scores 0.51 on the 2025 scale. The task of planning and advising on routing and control of road traffic and public transportation systems for efficiency and safety involves complex decision-making, real-time data analysis, and stakeholder interaction. Generative AI can significantly assist with data processing, route optimization, and predictive analysis, similar to tasks like "Maintaining digital records of road network signage" (adjusted score of 0.6) and "Developing technical specifications for road traffic safety" (adjusted score of 0.45). While AI can automate components such as traffic simulations and schedule adjustments, human intervention is crucial for nuanced judgment, regulatory considerations, and adapting to unforeseen conditions. The revised score accounts for Poland's technological infrastructure, supporting high AI integration, but acknowledges the indispensable role of human oversight, given the strategic and operational complexities involved. Therefore, a score of 0.575 reflects AI's role in augmenting, but not fully automating, such planning and advisory tasks.

Moving fastest, 2023 → 2025

“Planning layout and coordinating development of urban areas;”

Model capability on this task changed by +0.09 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 2164, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Urban and Regional Planners

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Town and Traffic Planners sit at the 78th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Town and Traffic Planners rank in the 78th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.06 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Planning and advising on routing and control of road traffic and public transportation systems for efficiency and safety.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Town and Traffic Planners". https://singulariki.com/gradient/2164-town-and-traffic-planners.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.