Crossing Guards and Flaggers
In short: today's best AI models could do about 10% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about the same by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 90% of the time, stays with people.
Guide or control vehicular or pedestrian traffic at such places as streets, schools, railroad crossings, or construction sites.
94,360 people do this job in the US. $38,100 a year.
Reviewed on September 30, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on0 h
- A person checks AI's work4 h
- Desk work AI can't do yet0 h
- Stays with people36 h
Crossing Guards and Flaggers, now: of a 40-hour week, AI could take on 0 hours, 4 hours become checking its work, 0 hours are desk work it can't do yet, and 36 hours stay with people.
The largest pieces: Learn the signs in the patrol area (about 3 h, a person checks); Record and report violators (under an hour, a person checks).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 9%
- 1%
- 3%
- 35%
- 51%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 10% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 0%. With Claude Mythos Preview (early), measured in April 2026, 10%. Today's estimate: 10%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, no reportable share went to this job's tasks in May 2026; 0 of its 12 tasks showed up. Within each of Anthropic's methods, never across a change: 0.0028% to 0.003% (released February and March 2025).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.0028% | 664 of 708 | 1 of 12 |
| Released March 2025 | 0.003% | 626 of 703 | 1 of 12 |
| The week of Aug 4, 2025 (released September 2025) | none | n/a | 0 of 12 |
| The week of Nov 13, 2025 (released January 2026) | none | n/a | 0 of 12 |
| The week of Feb 5, 2026 (released March 2026) | none | n/a | 0 of 12 |
| April 2026 (released June 2026) | none | n/a | 0 of 12 |
| May 2026 (released June 2026) | none | n/a | 0 of 12 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates 71% of this job's working time as physical work. For 71% of the working time, a robot has been shown doing most of the task somewhere: 71% in a purpose-built cell. 0.71 of 3, the 257th highest of the 923 jobs.
- Not physical 29%
- In a purpose-built cell 71%
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
90% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Control traffic at crossings, the hands-on work, 31%
- Escort pedestrians across, the hands-on work, 12%
- Direct traffic with signs and flags, the hands-on work, 11%
- Learn the signs in the patrol area, the in-person work, 9%
- Explain crossing rules, the in-person work, 8%
- Watch traffic for safe gaps, the hands-on work, 7%
What becomes checking the machine
10% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it.
- Learn the signs in the patrol area, costly if wrong, 9%
- Record and report violators, costly if wrong, 1%
What to learn
- Judgment where errors are severe. Some tasks are severe if wrong. Knowing when not to trust an output matters more there than speed.
- The hands-on craft. About 51% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- The conversations. About 39% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- Dealing with people outside the organization. Customers, patients, suppliers or the public: trust with them is built by a person.
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check: spotting what a draft gets wrong is a skill of its own.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 12 tasks from .
| Task | ||||
|---|---|---|---|---|
| Guide or control vehicular or pedestrian traffic at such places as street and railroad crossings and construction sites.Presence at the crossing, directing vehicles and people.Done daily by most who do it. Robots: in a purpose-built cell, CrossBot (Fresno State (Hovannes Kulhandjian) / Mineta Transportation Institute) (demonstrated). Anthropic's rating. | 31% | 25 min13 min to 1.3 h | Hands-on 70%, in person 30% | Stays with people |
| Learn the location and purpose of street traffic signs within assigned patrol areas.Learning only the guard can do.Done daily by most who do it. | 17% | 50 min25 min to 1.7 h | Documents 50%, in person 50% | 50%Desk part half by now |
| Direct or escort pedestrians across streets, stopping traffic, as necessary.Stopping traffic and walking people across.Done daily by most who do it. Robots: in a purpose-built cell, CrossBot (Hovannes Kulhandjian, Fresno State (California State University), with Mineta Transportation Institute) (demonstrated). Anthropic's rating. | 12% | 8 min4 min to 17 min | Hands-on 60%, in person 40% | Stays with people |
| Direct traffic movement or warn of hazards, using signs, flags, lanterns, and hand signals.Signs, flags and hand signals at the site.Done daily by most who do it. Robots: in a purpose-built cell, Temporary/portable traffic control signals (MUTCD Part 4/6 devices) (FHWA MUTCD (2023) / signal manufacturers) (deployed). Anthropic's rating. | 11% | 17 min4 min to 50 min | Hands-on 100% | Stays with people |
| Communicate traffic and crossing rules and other information to students and adults.Rules explained to students and adults.Done daily by most who do it. | 8% | 10 min4 min to 21 min | In person 100% | Stays with people |
| Monitor traffic flow to locate safe gaps through which pedestrians can cross streets.Watching the traffic at the crossing.Done daily by most who do it. | 7% | 7 min3 min to 13 min | Hands-on 100% | Stays with people |
| Discuss traffic routing plans and control-point locations with superiors.Conversations about control points, some on site.Done yearly or less by most who do it. | 4% | 25 min13 min to 50 min | Conversation 60%, in person 40% | Stays with people |
| Record license numbers of vehicles disregarding traffic signals, and report infractions to appropriate authorities.Plate numbers noted and reported.Done more than monthly by most who do it. | 2% | 13 min4 min to 25 min | Systems 50%, hands-on 50% | 50%Desk part half by now |
| Inform drivers of detour routes through construction sites.Directions given at the site.Done yearly or less by most who do it. | 2% | 13 min4 min to 25 min | In person 100% | Stays with people |
| Distribute traffic control signs and markers at designated points.Signs and markers set out.Done daily by most who do it.Robots: in a purpose-built cell, Falcon 100 ACLM (Automated Cone Laying Machine) (Highway Care Ltd with SENN Engineering) (deployed). Anthropic's rating. | 2% | 17 min8 min to 38 min | Hands-on 100% | Stays with people |
| Report unsafe behavior of children to school officials.A call or note to the school.Done yearly or less by most who do it. | 1% | 8 min4 min to 17 min | Conversation 70%, documents 30% | 30%Desk part half by now |
| Stop speeding vehicles to warn drivers of traffic laws.Stopping the vehicle and a word with the driver.Done daily by most who do it.Robots: in a purpose-built cell, Intelligent Police Unit R001 (AI traffic policing robot) (AiMOGA Robotics / Wuhu traffic police) (deployed). Anthropic's rating. | 1.0% | 8 min4 min to 17 min | In person 60%, hands-on 40% | Stays with people |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Crossing Guards and Flaggers is within reach of AI now?
- An estimated 10% of the working time is within reach of AI models now and 10% by the end of 2028, on METR's long-run pace at four in five. Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Crossing Guards and Flaggers come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Learn the signs in the patrol area (50% of the task's time now); Record and report violators (50% of the task's time now); and Report unsafe children to the school (30% of the task's time now).
- Which tasks of Crossing Guards and Flaggers stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Control traffic at crossings; Escort pedestrians across; and Direct traffic with signs and flags. Together such tasks are about 79% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Crossing Guards and Flaggers learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are judgment where errors are severe; the hands-on craft; and the conversations. By the end of 2028 on the long-run pace at four in five, about 90% of the working time stays with people, 10% becomes checking a model's work and 0% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Crossing Guards and Flaggers?
- In the Claude.ai conversations Anthropic sampled for May 2026, no reportable share went to this job's tasks. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 0%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Crossing Guards and Flaggers?
- Anthropic's robot exposure rating (September 2026) finds 71% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 71% of the working time. Its index is 0.71 of 3. One example: CrossBot for "control traffic at crossings". A robot shown somewhere is not one in every workplace; this site's own estimates still count no physical work as within reach.
- How is this estimated?
- From the 12 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on September 30, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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The full personal report is $19, to print or save as a PDF: every task with when it comes within reach, which ones your description names, the parts of your description the task list does not cover, what stays with you, what becomes checking the machine and what to learn.