Traffic Technicians

Transportation & Material Moving

AI exposure

  • Data source: BLSPublished: 2026-08

    High· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: 2026-03

    0.000

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: 2025

    0.26

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 76% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Show 4 hidden tasks

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Prepare work orders for repair, maintenance, or changes in traffic systems.

O*NET Task ID 8634

1.0
Gather and compile data from hand count sheets, machine count tapes, or radar speed checks and code data for computer input.

O*NET Task ID 8640

1.0
Compute time settings for traffic signals or speed restrictions, using standard formulas.

O*NET Task ID 8647

1.0
Prepare drawings of proposed signal installations or other control devices, using drafting instruments or computer-automated drafting equipment.

O*NET Task ID 8631

0.5
Plan, design, and improve components of traffic control systems to accommodate current or projected traffic and to increase usability and efficiency.

O*NET Task ID 8632

0.5
Analyze data related to traffic flow, accident rates, or proposed development to determine the most efficient methods to expedite traffic flow.

O*NET Task ID 8633

0.5
Study factors affecting traffic conditions, such as lighting or sign and marking visibility, to assess their effectiveness.

O*NET Task ID 8635

0.5
Lay out pavement markings for striping crews.

O*NET Task ID 8637

0.5
Operate counters and record data to assess the volume, type, and movement of vehicular or pedestrian traffic at specified times.

O*NET Task ID 8638

0.5
Provide technical supervision regarding traffic control devices to other traffic technicians or laborers.

O*NET Task ID 8639

0.5

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information