# Parking Enforcement Workers

> Patrol assigned area, such as public parking lot or city streets to issue tickets to overtime parking violators and illegally parked vehicles.

- **SOC code:** 33-3041.00
- **Canonical URL:** https://singulariki.com/roles/role-33-3041-00
- **Also known as:** Parking Control Officer, Parking Enforcement Officer (PEO), Parking Enforcer, Ticket Writer, Parking Enforcement Technician, Parking Officer, Parking Regulation Enforcement Officer, Parking Technician
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Enter and retrieve information pertaining to vehicle registration, identification, and status, using hand-held computers.
- Patrol an assigned area by vehicle or on foot to ensure public compliance with existing parking ordinance.
- Write warnings and citations for illegally parked vehicles.
- Appear in court at hearings regarding contested traffic citations.
- Respond to and make radio dispatch calls regarding parking violations and complaints.
- Maintain assigned equipment and supplies, such as hand-held citation computers, citation books, rain gear, tire-marking chalk, and street cones.
- Maintain close communications with dispatching personnel, using two-way radios or cell phones.
- Perform simple vehicle maintenance procedures, such as checking oil and gas, and report mechanical problems to supervisors.
- Observe and report hazardous conditions, such as missing traffic signals or signs, and street markings that need to be repainted.
- Identify vehicles in violation of parking codes, checking with dispatchers when necessary to confirm identities or to determine whether vehicles need to be booted or towed.
- Train new or temporary staff.
- Mark tires of parked vehicles with chalk and record time of marking, and return at regular intervals to ensure that parking time limits are not exceeded.

**Emerging tasks** (O*NET):
- Perform traffic control duties such as setting up barricades and temporary signs, placing bags on parking meters to limit their use, or directing traffic or pedestrians.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- English Language _(knowledge)_
- Public Safety and Security _(knowledge)_
- Oral Expression _(ability)_
- Speech Clarity _(ability)_
- Law and Government _(knowledge)_
- Speaking _(essential_skill)_
- Monitoring _(essential_skill)_
- Oral Comprehension _(ability)_
- Information Ordering _(ability)_
- Near Vision _(ability)_
- Computers and Electronics _(knowledge)_
- Active Listening _(essential_skill)_

**Skills in demand:**
- English Language _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Speech Recognition _(Specialized Skill)_
- Social Perceptiveness _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Active Listening _(Common Skill)_
- Time Management _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft Windows _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Access _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Windows _(hot technology)_
- Microsoft Word _(hot technology)_
- Complus Data Innovations FastTrack
- Integrated Parking Solutions MApp
- Ticket issuing software
- Vehicle information databases
- Web browser software

## AI exposure & outlook

- **AI task-overlap index:** 40th percentile (Moderate) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 31st percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 42nd percentile (Moderate) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 52nd percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 69th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -1.5% growth (Declining); 0.7k annual openings; 8.4k → 8.2k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $47,150; 7,770 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-33-3041-00_