Highway Maintenance Workers

Construction and Extraction Occupations
Código O*NET-SOC
47-4051.00

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.

Los nombres de las ocupaciones y las descripciones de tareas se muestran en inglés, tal como se publicaron. Las etiquetas, incluidos los tipos de tarea, están traducidas.

Exposición a la IA

  • Fuente de datos: BLSPublicado: 2026-08

    Bajo· relativa

    BajoCuatro bandas relativasMuy alto

    Valor por grupo ocupacional

    Escala, base y fuente

    Cuatro bandas relativas (Bajo / Moderado / Alto / Muy alto)

    831 ocupaciones detalladas de la tabla de proyecciones de empleo de BLS. El valor se asigna por código de la National Employment Matrix (NEM), de modo que las ocupaciones que comparten un código NEM reciben la misma banda

    Conjunto de datos de origen (descarga XLSX)

  • Fuente de datos: AnthropicPublicado: 2026-03

    0.000

    0.000Rango de los valores aquí recogidos0.745
    Escala, base y fuente

    Índice de exposición observada, 0–1 tal como se publica

    Mapeado sobre tareas de O*NET

    Publicado por ocupación SOC 2018; toda ocupación O*NET con el mismo código SOC 2018 recibe este valor

    Conjunto de datos de origen (descarga CSV)

  • Fuente de datos: ILOPublicado: 2025

    0.09

    0.09Rango de los valores aquí recogidos0.70

    Valor por grupo ocupacional

    Escala, base y fuente

    Índice de exposición a la IA generativa, 0–1 tal como se publica

    Publicado por grupo primario ISCO-08. Vinculado a esta ocupación, total o parcialmente, aplicando tal como se publicaron las tablas de correspondencia de la U.S. Bureau of Labor Statistics (ISCO-08 a SOC 2010, SOC 2010 a SOC 2018)

    Conjunto de datos de origen (descarga PDF)

Qué tipo de cifra publica esta fuente

La categoría de BLS es un rango relativo, no un nivel absoluto, y tampoco es una medición de primera mano: agrupa en cuatro bandas los rangos percentiles de la ocupación en varios estudios publicados. No es una previsión de empleo ni de salarios, no es una probabilidad de adopción y no distingue entre automatización y aumento.

Exposición a la IA (rúbrica de OpenAI)

19 tareas evaluadas · 1 tareas con β ≥ 0,5 (5.3%)

β = exposición directa (E1) + 0,5 × exposición con herramientas disponibles (E2), según la definición del repositorio de origen.

  • Unidad de origen: tareas de O*NET 27.2 → código de ocupación de O*NET 31.0
  • Todas las tareas puntuadas figuran en la lista de tareas de O*NET 31.0.
Fuente
OpenAI "GPTs are GPTs" exposure rubric
Versión
gh-main-0471612
Licencia
MIT License, Copyright (c) 2024 OpenAI

Tareas

Descripciones de tareas de la O*NET® 31.0 Database, primero las tareas principales.

TareaTipoβ (OpenAI)
Flag motorists to warn them of obstacles or repair work ahead.Principal0
Set out signs and cones around work areas to divert traffic.Principal0
Dump, spread, and tamp asphalt, using pneumatic tampers, to repair joints and patch broken pavement.Principal0
Drive trucks to transport crews and equipment to work sites.Principal0
Inspect, clean, and repair drainage systems, bridges, tunnels, and other structures.Principal0
Haul and spread sand, gravel, and clay to fill washouts and repair road shoulders.Principal0
Erect, install, or repair guardrails, road shoulders, berms, highway markers, warning signals, and highway lighting, using hand tools and power tools.Principal0
Remove litter and debris from roadways, including debris from rock and mud slides.Principal0
Clean and clear debris from culverts, catch basins, drop inlets, ditches, and other drain structures.Principal0
Perform roadside landscaping work, such as clearing weeds and brush, and planting and trimming trees.Principal0
Perform preventative maintenance on vehicles and heavy equipment.Principal0
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.Principal0
Paint traffic control lines and place pavement traffic messages, by hand or using machines.Complementaria0
Inspect markers to verify accurate installation.Complementaria0,5
Apply poisons along roadsides and in animal burrows to eliminate unwanted roadside vegetation and rodents.Complementaria0
Measure and mark locations for installation of markers, using tape, string, or chalk.Complementaria0
Apply oil to road surfaces, using sprayers.Complementaria0
Blend compounds to form adhesive mixtures used for marker installation.Complementaria0
Place and remove snow fences used to prevent the accumulation of drifting snow on highways.Complementaria0

Información ocupacional

Fuentes y atribución

This page includes information from the O*NET® 31.0 Database (https://www.onetcenter.org/database.html) by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. AI Changing Work has modified all or some of this information: the O*NET-SOC code, title and task statements are reproduced in English without change; task-type labels are shown in the page's language and tasks are listed core first; any Korean occupation title shown on the Korean-language page is AI Changing Work's translation; any KSCO-8 unit groups linked to this occupation were paired with it by AI Changing Work's judgment, and the relation labels and statuses are AI Changing Work's additions. USDOL/ETA has not approved, endorsed, or tested these modifications.

Any AI exposure figures on this page are published by third parties, not by AI Changing Work, and none is part of the O*NET information. OpenAI publishes task-level scores (MIT License) for O*NET 27.2 task statements; each is shown next to the O*NET 31.0 task statement with the same task ID, whose wording can differ from the 27.2 statement that was scored. OpenAI also publishes occupation-level scores for O*NET-SOC codes in the same release, and any such score is shown on the O*NET occupation with the same code. Anthropic publishes an observed exposure index in the Anthropic Economic Index (CC-BY), and the U.S. Bureau of Labor Statistics publishes relative AI exposure categories (public domain); both are published per SOC code, and each value is shown on every O*NET occupation with that code. The International Labour Organization publishes a generative AI exposure index in ILO Working Paper 140 (CC BY 4.0) for ISCO-08 unit groups; AI Changing Work links those groups to O*NET occupations by applying the U.S. Bureau of Labor Statistics ISCO-08 to 2010 SOC and 2010 SOC to 2018 SOC crosswalks as published, without case-by-case selection, and these crosswalks match many groups only in part. Where several unit groups are linked, each group's published value is listed, and any summary shows only the lowest and highest of those values with the number of groups; no exposure figure is averaged or recalculated. Any employment figures are published by the U.S. Bureau of Labor Statistics for the SOC group containing this occupation. Each source is credited where its figures are shown.

O*NET OnLine: 47-4051.00 Highway Maintenance Workers

KSCO 코드·명칭: 한국표준직업분류(제8차 개정) — 통계청 고시 제2024-328호 (2024-07-01 고시, 2025-01-01 시행). 저작권법 제7조 제2호의 고시 항목이다. 명칭 표기(가운뎃점·띄어쓰기)는 해설서 2차 정오 반영판의 표기를 따랐으며, 고시 항목표와는 18개 명칭에서 가운뎃점 글리프나 띄어쓰기만 다르다. 통계청은 2025년 10월 국가데이터처로 개편되었다. 이 페이지의 KSCO 연결은 통계청·국가데이터처의 공식 연계표가 아니다.

Atribución y licencias completas