EXPOSED
The detection half of this job — spotting expired meters, reading plates, matching permits, printing and mailing citations — is already done better by license-plate-recognition cameras, in-ground sensors, and pay-by-app systems, and cities have been cutting routes accordingly. What survives is physical: walking a beat where cameras don't reach, booting and tagging vehicles for tow, clearing fire lanes and disabled spaces, handling the angry driver at the windshield, and testifying at adjudication hearings. This is a municipal job protected more by union contracts and city procurement inertia than by anything AI can't do.
Headcount grew steadily across the period.
Median pay $40,920 → $46,730 -8.6% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
-1.5% 8,400 → 8,200 on the projections basis
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -1.5% by 2034. This is the case where the score and the forecast agree, and it is the one worth taking seriously.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~700 openings a year on average, including replacing people who leave.
PatrollerMeter MaidTicket WriterParking ManagerParking OfficerParking EnforcerParking SupervisorParking TechnicianEnforcement OfficerParking Lot AttendantRamp Service EmployeeParking Control OfficerParking Meter AttendantSecurity Patrol OfficerTraffic Control OfficerParking Services OfficerTraffic Control AttendantEnforcement Safety OfficerParking Enforcement SpecialistParking Enforcement TechnicianParking Enforcement Officer (PEO)Parking Regulation Enforcement OfficerCivilian Pay Technician (Civilian Pay Tech)
Holding it up: embodiment . Weakest point: trust premium .
Mixed — a routine tier and a judgment tier At 8 the split is roughly even: meter checking, permit verification, and citation issuance are already replaced by LPR vans and app-based enforcement in most mid-size cities, but immobilizing with a boot, chalking tires on residential permit streets, coordinating tow trucks, and physically clearing a blocked hydrant still require someone on the curb — that's why it isn't a 4, and why it isn't 12.
Hands-on in uncontrolled environments A 13 reflects eight-hour foot or scooter patrols in whatever weather the route delivers, kneeling in traffic to fit a wheel clamp, and standing between a tow hook and its owner — uncontrolled public street, but on a fixed and mapped beat rather than an unpredictable interior, which keeps it off the high end.
Certification preferred, not legally required At 5 you may carry a municipal enforcement certification or state-mandated training, and your citation is a sworn document, but you hold no license that can be revoked and the city — not you — defends and voids the ticket when the adjudicator dismisses it.
Executes defined procedures on defined inputs At 5 nearly every call is spelled out — grace periods, curb-color codes, boot thresholds by number of unpaid tickets, ADA placard checks — with the real discretion limited to whether to void, whether to escalate to tow, and how you describe the violation when it reaches adjudication.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (8/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (5/20) is whether the law requires a licensed human to sign. Trust premium (4/20) is whether buyers specifically pay for a person. Judgment and accountability (5/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 14 of this occupation's 35 points (40%).
Embodiment (13/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
No occupation passed every test: close enough to parking enforcement workers on skills and subject matter, at least 10 points more resistant, no big jump in training, no new licence, no pay cut, and not shrinking on its own. That happens for 223 of the 654 occupations here that aren't SAFE, and it is worth stating plainly rather than leaving the section off.
The usual reason is that exposure travels with the skill profile. The jobs most similar to yours tend to be exposed for the same reasons yours is, so the near neighbours don't clear the gap — and the ones that do are a different kind of work, not a transfer of what you already know. Read that as a limit of this method, not a verdict that you're stuck: it only compares whole occupations, and it cannot see specialisation, industry, or anything you'd bring that isn't in a federal skill survey.
Here is that claim on your own job rather than in the abstract. These are the three occupations closest to this one by skill and subject matter — the places the work would most naturally transfer — with what the register scores them:
That is the whole problem in three lines. The nearest work is not meaningfully safer, so there is no move here that trades a similar skill set for a better verdict. This is not us running out of ideas — it is what the neighbourhood looks like.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 55/100, still EXPOSED.
State or municipal rules requiring a sworn/certified human officer to personally attest to automated citations before they are mailed — as in New York's requirement that camera-based violations be reviewed by a technician or officer, and California AB 645 (speed cameras) mandating human review of each image. If parking LPR citations are brought under equivalent attestation rules, the human reviewer/affiant becomes legally necessary and subject to cross-examination at adjudication.
Formal role shift to exception-handling and hearing testimony: ADA/disabled-placard fraud determinations, fire-lane and hydrant clearance calls, contested-permit and medical-emergency discretion, and appearing as the accountable witness at adjudication. This is a genuine second tier that grows as routine detection is automated.
Due-process litigation or state legislation voiding citations lacking a named human issuer of record — plus tow/boot statutes that already require a certified officer's signature before immobilization or removal, extended to AI-flagged vehicles.
Expansion of duties AI cannot reach: booting/immobilization, abandoned-vehicle and derelict tagging, EV-charger blocking enforcement, street-sweeping and snow-emergency clearance, and scooter/curb-management sweeps — assignments cities are actively adding to enforcement units as curb use densifies.
Task-mix shift as meters and LPR absorb routine detection: what remains is confrontation management, evidence documentation that survives appeal, and unpredictable-street judgment. Also rises if cities respond to camera-revenue backlash (e.g. municipalities that suspended automated programs after error scandals) by restoring officer-issued citations.
The limit. No plausible route to a higher trust premium — no driver or resident pays extra for a human ticket-writer; demand is purely municipal. Ceiling is low overall: headcount can fall even as the surviving role's per-worker judgment content rises, so score gains here coexist with continued route cuts.
| New York-Newark-Jersey City, NY-NJ | 970 | $45,040 -4% |
| Los Angeles-Long Beach-Anaheim, CA | 670 | $55,820 +19% |
| San Francisco-Oakland-Fremont, CA | 510 | $86,040 +84% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 460 | $46,730 +0% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 320 | $60,640 +30% |
| Boston-Cambridge-Newton, MA-NH | 260 | $51,980 +11% |
| Baltimore-Columbia-Towson, MD | 140 | $45,380 -3% |
| Seattle-Tacoma-Bellevue, WA | 130 | $76,870 +64% |
| San Francisco-Oakland-Fremont, CA | 510 | $86,040 +84% |
| Seattle-Tacoma-Bellevue, WA | 130 | $76,870 +64% |
| Portland-Vancouver-Hillsboro, OR-WA | 50 | $76,750 +64% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 35. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.