Highway Maintenance Workers
Construction and Extraction Occupations- Code 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.
Les intitulés des métiers et les énoncés de tâches sont affichés en anglais, tels que publiés. Les libellés, y compris les types de tâches, sont traduits.
Exposition à l'IA
- Source des données: BLSPublié: 2026-08
Faible· relative
FaibleQuatre bandes relativesTrès élevéeValeur par groupe professionnel
Échelle, base et source
Quatre bandes relatives (Faible / Modérée / Élevée / Très élevée)
831 métiers détaillés du tableau des projections d'emploi du BLS. La valeur est attribuée par code de la National Employment Matrix (NEM), si bien que les métiers partageant un code NEM reçoivent la même bande
- Source des données: AnthropicPublié: 2026-03
0.000
0.000Étendue des valeurs présentées ici0.745Échelle, base et source
Indice d'exposition observée, 0–1 tel que publié
Rapporté aux tâches O*NET
Publié par profession SOC 2018 ; chaque profession O*NET ayant le même code SOC 2018 reçoit cette valeur
- Source des données: ILOPublié: 2025
0.09
0.09Étendue des valeurs présentées ici0.70Valeur par groupe professionnel
Échelle, base et source
Indice d'exposition à l'IA générative, 0–1 tel que publié
Publié par groupe de base ISCO-08. Relié à ce métier, en tout ou en partie, en appliquant tels que publiés les tableaux de correspondance du U.S. Bureau of Labor Statistics (ISCO-08 vers SOC 2010, SOC 2010 vers SOC 2018)
Quelle est la nature de la valeur publiée par cette source
La catégorie BLS est un rang relatif et non un niveau absolu, et ce n'est pas une mesure de première main : elle regroupe en quatre bandes les rangs centiles du métier dans plusieurs études publiées. Ce n'est ni une prévision d'emploi ou de salaire, ni une probabilité d'adoption, et elle ne distingue pas automatisation et augmentation.
Exposition à l’IA (grille d’OpenAI)
19 tâches évaluées · 1 tâches avec β ≥ 0,5 (5.3%)
β = exposition directe (E1) + 0,5 × exposition lorsque des outils sont disponibles (E2), selon la définition du dépôt source.
- Unité source : tâches O*NET 27.2 → code de métier O*NET 31.0
- Toutes les tâches notées figurent dans la liste des tâches O*NET 31.0.
- Source
- OpenAI "GPTs are GPTs" exposure rubric
- Version
- gh-main-0471612
- Licence
- MIT License, Copyright (c) 2024 OpenAI
Tâches
Énoncés de tâches de l’O*NET® 31.0 Database, les tâches principales d’abord.
| Tâche | Type | β (OpenAI) |
|---|---|---|
| Flag motorists to warn them of obstacles or repair work ahead. | Principale | 0 |
| Set out signs and cones around work areas to divert traffic. | Principale | 0 |
| Dump, spread, and tamp asphalt, using pneumatic tampers, to repair joints and patch broken pavement. | Principale | 0 |
| Drive trucks to transport crews and equipment to work sites. | Principale | 0 |
| Inspect, clean, and repair drainage systems, bridges, tunnels, and other structures. | Principale | 0 |
| Haul and spread sand, gravel, and clay to fill washouts and repair road shoulders. | Principale | 0 |
| Erect, install, or repair guardrails, road shoulders, berms, highway markers, warning signals, and highway lighting, using hand tools and power tools. | Principale | 0 |
| Remove litter and debris from roadways, including debris from rock and mud slides. | Principale | 0 |
| Clean and clear debris from culverts, catch basins, drop inlets, ditches, and other drain structures. | Principale | 0 |
| Perform roadside landscaping work, such as clearing weeds and brush, and planting and trimming trees. | Principale | 0 |
| Perform preventative maintenance on vehicles and heavy equipment. | Principale | 0 |
| 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. | Principale | 0 |
| Paint traffic control lines and place pavement traffic messages, by hand or using machines. | Complémentaire | 0 |
| Inspect markers to verify accurate installation. | Complémentaire | 0,5 |
| Apply poisons along roadsides and in animal burrows to eliminate unwanted roadside vegetation and rodents. | Complémentaire | 0 |
| Measure and mark locations for installation of markers, using tape, string, or chalk. | Complémentaire | 0 |
| Apply oil to road surfaces, using sprayers. | Complémentaire | 0 |
| Blend compounds to form adhesive mixtures used for marker installation. | Complémentaire | 0 |
| Place and remove snow fences used to prevent the accumulation of drifting snow on highways. | Complémentaire | 0 |
Informations professionnelles
Sources et attribution
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.