Skip to sources
Time Machine

Highway Maintenance Workers

Scrub through 243years of this role's history, from when it first emerged, through every wave of technology that reshaped it, to the cited projections for where it's heading next.

2026drag to travel through time
180018251850187519001925195019752000now
2026
Known today as Highway Maintenance Workers (BLS SOC 47-4051)
Latest actual · 2024
152K
OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.
Latest actual · 2024
$49,070
Source: BLS-OEWS
Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.
Tools of the era

The tools that defined the work

Select an era to see how it reshaped the work.

  • Hand tools and horse-drawn road scraper (pick, shovel, stone hammer, drag)

    The first road maintenance crews worked entirely by hand and animal power. Stone breakers used hammers to fracture rock to McAdam's specification: no fragment larger than could fit in a person's mouth. Workers packed layers by tamping and relied on traffic to compact the surface. The horse-drawn road drag, a simple log or plank pulled diagonally across a dirt surface, was the primary grading tool on unpaved county roads into the late 19th century. There was no separation between maintenance and construction labor; the same crew that built a road patrolled and repaired it.

    Work toolChanging equipment
  • Steam roller and early road machine (Adams road machine, steam-powered compaction)

    The Adams road machine, widely adopted by county road departments after the 1870s, gave a single operator with a horse team the ability to grade and crown a road surface in a fraction of the time a hand crew required. Steam rollers from the 1860s onward replaced tamping labor on macadam roads, compressing stone to final grade mechanically. Neither machine eliminated the road crew; both created new operator roles while reducing the number of hand laborers per lane-mile. The Good Roads Movement of the 1890s, backed by bicycle manufacturers and the League of American Wheelmen, accelerated mechanization of county road maintenance as it lobbied for improved road standards.

    Effect on the work

    Mechanization of grading and compaction reduced the number of hand laborers required per lane-mile of maintenance but created new machine-operator roles. The net workforce impact through the 1920s was an overall increase in the size of the road maintenance workforce, driven by rapid expansion of the road network, not any contraction from individual productivity gains.

    Work toolChanging equipment
  • Motor grader, asphalt paver, and pneumatic-tire roller (diesel mechanization era)

    The shift from macadam and brick to hot-mix asphalt, accelerated by the interstate construction program of the late 1950s and 1960s, transformed what maintenance workers had to know. Asphalt patching required an asphalt kettle or later a hot-box trailer, infrared heaters, and pneumatic tampers rather than stone-breaking hammers. Diesel motor graders replaced horse-drawn road machines for shoulder and drainage work. Snowplow attachments on trucks became standard in northern-state fleets from the 1940s. The work became equipment-intensive rather than labor-intensive: a two-person crew with a grader and a dump truck could maintain five times the lane-mileage a 1910 hand crew could. State highway departments formalized route-based patrol systems in which each crew was responsible for a defined highway segment, creating the recognizable modern structure of the role.

    Work toolChanging equipment
  • ISTEA and 3R/4R programs (federal Interstate Maintenance funding; systematic pavement management)

    The 1991 Intermodal Surface Transportation Efficiency Act established the Interstate Maintenance (IM) Program, replacing ad-hoc resurfacing appropriations with a systematic federal funding stream for resurfacing, restoration, rehabilitation, and reconstruction of Interstate routes. For maintenance workers, this meant a shift from crisis-response patching to scheduled pavement management: pavement condition index surveys drove work orders, and crews were deployed according to data rather than just visible deterioration. The IM program also introduced performance standards: states had to demonstrate that the Interstate system met minimum pavement quality thresholds to maintain eligibility. This era marks the beginning of the data-driven work assignment that AI-based pavement scanning tools would accelerate in the 2010s.

    Effect on the work

    The systematic pavement management model increased the productivity of maintenance crews by reducing wasted trips to roads not yet needing treatment. However, it also made workforce size more legible to budget analysts, contributing to periodic DOT staff reduction efforts in states under fiscal pressure.

    Work toolChanging equipment
  • GPS fleet management and AI pavement scanning (Pavemetrics LCMS, road-condition mapping)

    The first commercial deployment of high-speed laser pavement condition survey vehicles in the 2000s, reaching widespread DOT use by the early 2010s, changed how maintenance crews received their work orders. Instead of a supervisor walking a section and noting defects by observation, a laser survey vehicle running at highway speed produced a full pavement condition index map of the route, flagging cracks, ruts, and delamination at millimeter resolution. GPS fleet management systems simultaneously let dispatchers track crew locations, assign work orders from a tablet, and log completed repairs with coordinates. Together these tools shifted a portion of the diagnostic work from field crews to remote analysts, while giving crews more precise location information for where to apply the next ton of hot mix.

    Effect on the work

    AI-assisted pavement scanning reduces the need for dedicated inspection crews but does not reduce the need for repair crews; it changes what repair crews are doing and in what sequence. Over 500 Pavemetrics LCMS-2 systems have been deployed globally as of 2024, suggesting the technology is now a standard DOT tool rather than an early-adopter experiment.

    Work toolChanging equipment
  • Smart work zones and Automated Flagger Assistance Devices (AFADs)

    Automated Flagger Assistance Devices, which control one-way traffic through work zones without a human standing in the travel lane, began reaching commercial scale around 2020-2022. The Site 20/20 Guardian SmartFlagger won the ATSSA Innovation Award in 2022. Smart work zone systems combining portable radar, LiDAR-camera pods, and queue-warning displays were deployed by Florida and Texas DOT with documented 18-45% reductions in rear-end crash potential. These technologies address the most dangerous aspect of the highway maintenance worker's job: traffic exposure. The direction is not replacement of the worker but relocation of the worker to a safer position while technology handles the traffic interface. As of 2025-26, AFADs are used at high-risk one-lane alternating-traffic points, but crew members remain on site to monitor, respond to anomalies, and perform the physical repair work.

    Effect on the work

    Work zone fatality data from FHWA shows that roadway workers are killed by vehicles in work zones at a rate of roughly 100-150 per year nationally. AFADs directly target this hazard by removing the human flagger from the travel lane. The technology does not reduce crew size but changes crew positioning and risk profile.

    Work toolChanging equipment
Projection cone · present → 2034

What credible sources project

Scrub the slider past now to anchor each scenario on the scrubber. The spread is the range of futures credible sources project for this role.

Employment outlook
Projected change in the number of people doing this work.
IIJA Bipartisan Infrastructure Law (2021) demand driver
2030
+5%
Qualitative demand-side projection. The Infrastructure Investment and Jobs Act (IIJA) of 2021 authorized $110 billion for roads, bridges, and major projects over five years, with significant funding directed at pavement rehabilitation and bridge preservation -- work types that employ highway maintenance workers rather than heavy construction crews. FHWA apportionments to state DOTs have accelerated maintenance work orders across the country through 2024-26. A sustained multi-year funding stream of this scale, flowing predominantly to state DOT maintenance budgets, supports at minimum a 3-5% upward revision to the baseline BLS employment trajectory for the 2024-30 period. This projection is based on funding analysis rather than a formal econometric model; it should be read alongside the BLS baseline, not as a replacement for it.
BLS National Employment Matrix 2024-34
2034
+3%
BLS industry-occupation matrix projections using replacement-need modeling and sector growth assumptions. The 2024-34 cycle projects 3.0% employment growth for 47-4051, from 159,100 (2024) to approximately 163,900 (2034) -- an increase of roughly 4,800 positions. This is classified as "about as fast as average" against all occupations. The BLS methodology applies infrastructure investment trajectory (including IIJA Bipartisan Infrastructure Law funding flowing to state DOT maintenance programs), lane-mileage growth, and an aging infrastructure stock that requires more repair work per lane-mile. The projection does not explicitly model autonomous maintenance vehicles or the pace of AFAD adoption, which could affect the per-mile crew-size ratio over the decade. Approximately 12,300 annual job openings are projected through 2034, primarily driven by retirement in an occupation with a median worker age well above the national median.
AI task exposure
Share of the role’s tasks that researchers estimate AI can do. This is a measure of task exposure, not a forecast of jobs lost.
Eloundou et al. (2023) — "GPTs are GPTs"
2028
5%
of tasks
GPT-4 task-by-task LLM exposure labeling on O*NET tasks. Highway maintenance workers score among the lowest LLM-exposure occupations in the Eloundou dataset. The dominant tasks (patching pavement, plowing snow, clearing debris, repairing guardrails, operating heavy equipment) require physical presence and real-time situational judgment in uncontrolled outdoor environments that LLMs cannot provide. The minimal exposure that exists comes from administrative tasks (logging work orders, reading AI-generated pavement condition reports) that account for a small share of working hours. The estimated 5% exposure figure reflects this: AI enters the workflow at the planning and reporting margins, not the execution core. This is consistent with construction and extraction occupations generally ranking at the low end of the LLM-exposure distribution.
Today, in this role

What's shifting in the work right now

The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.

What's changing in your day

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereOperate Automated Flagger Assistance Devices (AFADs) to control one-way traffic through active work zones, replacing manual flagging at high-risk locations while monitoring live camera feeds from a safe standoff position.

Operate Automated Flagger Assistance Devices (AFADs) to control one-way traffic through active work zones, replacing manual flagging at high-risk locations while monitoring live camera feeds from a safe standoff position.[4],[6]

Where your edge is

Get certified on AFAD operation and remote-monitoring interfaces; understand when to override automated signals manually based on site conditions like emergency vehicles or equipment breakdowns.

AI is sitting alongside you hereConduct visual and hands-on inspections of drainage systems, culverts, bridges, and tunnel surfaces, cross-referencing findings against drone-captured image datasets and AI defect-classification outputs to triage which repairs need crews immediately vs

Conduct visual and hands-on inspections of drainage systems, culverts, bridges, and tunnel surfaces, cross-referencing findings against drone-captured image datasets and AI defect-classification outputs to triage which repairs need crews immediately vs. next maintenance cycle.[7],[5]

Where your edge is

Develop the ability to verify or override AI defect-severity scores by understanding what ground-truth inspection reveals vs. what camera-based detection misses (subsurface delamination, hidden rebar corrosion).

AI is sitting alongside you hereSet up and manage smart work zone sensor arrays, including portable radar speed sensors, Bluetooth/Wi-Fi readers, and LiDAR-camera pods that feed queue-warning systems (QWS) and dynamic message signs to alert approaching drivers.

Set up and manage smart work zone sensor arrays, including portable radar speed sensors, Bluetooth/Wi-Fi readers, and LiDAR-camera pods that feed queue-warning systems (QWS) and dynamic message signs to alert approaching drivers.[8],[4]

Where your edge is

Learn to interpret real-time dashboards from queue-warning systems and understand how to adjust sensor placement for site geometry; study FHWA and MUTCD guidelines on smart work zone deployment.

Where this role is heading

Natural next steps for someone with your foundation: not exits, evolutions.

A direction you could grow

Civil Engineering Technologists and Technicians

Civil Engineering Technologists apply the same road, drainage, and structure knowledge in design-support and QA roles, working with survey data, pavement models, and construction documents. The pivot rewards workers who have built strong field intuition and are willing to add CAD and engineering-tech coursework.

What you'd add
  • · AutoCAD Civil 3D or MicroStation basics
  • · Construction materials testing (soils, concrete, asphalt)
  • · Associate degree or NICET certification in Civil Engineering Technology
  • · Reading and interpreting design plans and specifications
What it takesA real upskill, but a natural one
Share this year
Drops anyone you send it to straight into 2026.
Preview card
Part of Construction · see all 24roles →
Different role?

See the same long-arc view for your own profession.

Browse the directory by industry, or search by title or SOC code. New roles ship every few weeks. Every profile cites every claim.

Browse all roles

The data behind this timeline

On record since1793
Latest tracked employment151,750 (US, 2024)
Latest median pay$49,070 (2024)
Outlook+5% by 2030 (IIJA Bipartisan Infrastructure Law (2021) demand driver)
View all 26 cited data points
YearUS employmentMedian annual paySource
1935137,000n/aESTIMATE
1936n/a$900ESTIMATE
196085,000n/aESTIMATE
2000130,000$30,000BLS-OEWS, ESTIMATE
2003140,450$28,650BLS-OEWS
2004136,550$29,550BLS-OEWS
2005140,600$30,250BLS-OEWS
2006138,670$31,540BLS-OEWS
2007137,140$32,600BLS-OEWS
2008136,420$34,000BLS-OEWS
2009139,490$34,250BLS-OEWS
2010142,530$34,780BLS-OEWS
2011143,760$35,220BLS-OEWS
2012141,180$35,260BLS-OEWS
2013139,070$35,870BLS-OEWS
2014140,650$36,580BLS-OEWS
2015142,300$36,930BLS-OEWS
2016143,320$38,130BLS-OEWS
2017146,580$38,700BLS-OEWS
2018149,260$39,690BLS-OEWS
2019150,860$40,730BLS-OEWS
2020149,890$41,660BLS-OEWS
2021141,150$45,880BLS-OEWS
2022143,330$44,930BLS-OEWS
2023150,860$47,360BLS-OEWS
2024151,750$49,070BLS-OEWS
Embed this timeline on your site

Free for any site. Paste this where the timeline should appear; it stays interactive, every datapoint stays cited, and it sets no cookies on your page. How embedding works

<iframe src="https://futurehistory.earth/embed/47-4051"
  width="100%" height="430" style="border:0"
  title="Highway Maintenance Workers, a Future History timeline"
  loading="lazy"></iframe>

See all roles in Construction