# Highway Maintenance Workers

> Maintain highways, municipal and rural roads, airport runways, and rights-of-way. Duties include patching broken or eroded pavement and repairing guard rails, highway markers, and snow fences. May also mow or clear brush from along road, or plow snow from roadway.

- **SOC code:** 47-4051.00
- **Canonical URL:** https://singulariki.com/roles/role-47-4051-00
- **Also known as:** Equipment Operator (EO), Highway Maintainer, Highway Maintenance Worker, Transportation Maintenance Specialist (TMS), Highway Maintenance Crew Worker, Highway Maintenance Technician, Maintenance Technician, Maintenance Worker
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Set out signs and cones around work areas to divert traffic.
- Flag motorists to warn them of obstacles or repair work ahead.
- Perform preventative maintenance on vehicles and heavy equipment.
- Drive trucks to transport crews and equipment to work sites.
- Erect, install, or repair guardrails, road shoulders, berms, highway markers, warning signals, and highway lighting, using hand tools and power tools.
- Clean and clear debris from culverts, catch basins, drop inlets, ditches, and other drain structures.
- Drive heavy equipment and vehicles with adjustable attachments to sweep debris from paved surfaces, mow grass and weeds, remove snow and ice, and spread salt and sand.
- Haul and spread sand, gravel, and clay to fill washouts and repair road shoulders.
- Inspect, clean, and repair drainage systems, bridges, tunnels, and other structures.
- Remove litter and debris from roadways, including debris from rock and mud slides.
- Dump, spread, and tamp asphalt, using pneumatic tampers, to repair joints and patch broken pavement.
- Apply poisons along roadsides and in animal burrows to eliminate unwanted roadside vegetation and rodents.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Public Safety and Security _(knowledge)_
- Control Precision _(ability)_
- Multilimb Coordination _(ability)_
- Operation and Control _(transferable_skill)_
- English Language _(knowledge)_
- Operations Monitoring _(transferable_skill)_
- Oral Comprehension _(ability)_
- Problem Sensitivity _(ability)_
- Arm-Hand Steadiness _(ability)_
- Static Strength _(ability)_
- Transportation _(knowledge)_
- Manual Dexterity _(ability)_

**Skills in demand:**
- English Language _(Common Skill)_
- Visualization _(Specialized Skill)_
- Information Ordering _(Specialized Skill)_
- Finger Dexterity _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Depth Perception _(Common Skill)_
- Deductive Reasoning _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Word _(hot technology)_
- Database software
- Web browser software

## AI exposure & outlook

- **AI task-overlap index:** 3rd percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 9th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 6th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 3rd percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 74th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 3.0% growth (About average); 12.3k annual openings; 159.1k → 163.9k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $49,070; 151,750 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-47-4051-00_