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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · 2022
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/connecticut/statewide/2022-annual-report
Yearly Traffic Safety Analysis
30,519 CRASHES IN
CONNECTICUT, CT
2022
In Fairfield County, total reported crashes increased slightly from 30,014 in 2021 to 30,519 in 2022, a rise of approximately 1.7%. While overall crash and injury figures remained relatively stable, the most notable year-over-year shift was a significant 46.2% increase in fatalities, which rose from 39 to 57.
30,519
▲ 1.7%was 30,014
Total Crash Events
57
▲ 46.2%was 39
Persons Killed
9,042
▲ 1.5%was 8,906
Persons Injured
3,586
▲ 0.3%was 3,575
Hit-and-Run Crashes
Note: "Persons Killed" (57) counts individual fatalities across all crash events. "Fatal" in the severity table below (54) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend indicates a slight rise in crash volume year-over-year. Total crashes increased by 1.7% from 30,014 to 30,519, and total injuries saw a similar 1.5% increase from 8,906 to 9,042. However, this modest increase in incidents was accompanied by a disproportionately large 46.2% surge in traffic fatalities, which climbed from 39 in the prior year to 57 in the current year.
3,586
Hit-and-Run Crashes — 2022
▲ 0.3% vs prior (3,575)
The volume of hit-and-run crashes remained stable year-over-year. There were 3,586 hit-and-run incidents in 2022, compared to 3,575 in 2021. The hit-and-run rate, as a percentage of all crashes, saw a negligible decrease from 11.9% in the prior period to 11.8% in the current period.
Vulnerable Road User Casualties
16
Pedestrians Killed
0
Cyclists Killed
41
Motorists Killed
395
Pedestrians Injured
83
Cyclists Injured
8,564
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes remained largely consistent between the two periods. Friday was the peak day for crashes in both 2022 (5,122 crashes) and 2021 (5,179 crashes). The afternoon commute represented the peak time for collisions in both years, with the specific peak hour shifting slightly from 3 PM in 2021 (2,452 crashes) to 5 PM in 2022 (2,522 crashes).
Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While the overall proportion of injury-related crashes was stable, the severity of outcomes worsened year-over-year. The number of fatal crashes increased from 38 to 54, and their share of all crashes rose from 0.1% to 0.2%. Crashes resulting in serious injuries decreased slightly from 322 to 309, while those with minor injuries increased from 2,697 to 2,865.
Severity is per crash event (most severe injury). 54 fatal crash events resulted in 57 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record
Road & Environmental Conditions
The environmental conditions at the time of crashes were remarkably similar across both years. In 2022, 83.5% of crashes occurred in clear weather and 69.2% during daylight, compared to 83.1% and 69.4% respectively in 2021. Similarly, 82.6% of crashes in 2022 were on dry road surfaces, nearly unchanged from 83.4% in the prior year, indicating no significant shift in the prevalence of adverse-condition crashes.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field
Vehicles & Demographics
The makes of vehicles involved in crashes remained consistent, with Honda, Toyota, and Ford being the top three in both periods. Honda-involved crashes increased from 6,809 to 7,178, while Ford-involved incidents decreased from 5,040 to 4,902. The age distribution of persons involved in crashes also showed stability, though there was a slight increase in the representation of the 65+ age group, which accounted for 9.6% of persons in 2022 compared to 9.0% in 2021.
Top Vehicle Makes (59,045 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
4,868 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (66,726 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events
Speed Limit Zones
Year-over-year data shows a slight increase in crashes occurring in zones with posted speed limits of 40 mph or higher, which rose from 7,029 to 7,339 incidents. A notable increase in fatal outcomes was observed in specific speed zones. The number of fatal crashes in 25 mph zones doubled from 9 to 18, and in 40 mph zones, they increased from 1 to 8.
Fatal crashes by zone: 1 mph: 2 of 5,957 (0.034%) · 25 mph: 18 of 10,258 (0.175%) · 30 mph: 8 of 2,093 (0.382%) · 35 mph: 3 of 2,068 (0.145%) · 40 mph: 8 of 1,078 (0.742%) · 45 mph: 3 of 210 (1.429%) · 50 mph: 1 of 132 (0.758%) · 55 mph: 11 of 5,591 (0.197%)
Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Posted speed limit at crash location
Data Sources & Methodology
Primary Data Source
All crash data in this report is sourced from Connecticut Crash Data, accessed programmatically via the Csv Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.
Data Retrieval
- Access method: Csv Open Data API (SoQL queries)
- Data format: Structured JSON via REST API
- Record types queried: Crash events, person records, and vehicle unit records
- Date filter applied: 2022-01-01 through 2022-12-31
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-12-31 (365 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 30,519
- Total persons involved: 72,204
- Total vehicles involved: 59,045
Analytical Methodology
- Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
- Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
- Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
- Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
- Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
- Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
- AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.
Limitations & Disclaimers
- Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
- Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
- Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
- AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
- Percentages are calculated from reported data and are subject to rounding.
Non-Affiliation Disclosure
This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.
Data License
The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.
Corrections & Feedback
If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.
Suggested Citation
ThatCarHitMe.com (Injuria.ai). "connecticut, CT Crash Intelligence Report: 2022." Published August 20, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2022-annual-report
About the Publisher
ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.
Questions about this report's data or methodology: data@injuria.ai
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Connecticut Crash Data · Csv
Period: 2022-01-01 – 2022-12-31
Generated: August 20, 2026 · All rights reserved
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