Yearly Traffic Safety Analysis

3,799 CRASHES IN
CONNECTICUT, CT
2022

All metrics benchmarked against2021

In Litchfield County, total traffic crashes remained stable, increasing by just 1.2% from 3,755 in 2021 to 3,799 in 2022. While overall crash and injury numbers saw minimal change, the most significant year-over-year shift was a 31.6% increase in traffic fatalities, which rose from 19 to 25. Crashes involving pedestrians and bicyclists also saw notable increases.

3,799

▲ 1.2%was 3,755

Total Crash Events

25

▲ 31.6%was 19

Persons Killed

1,140

▼ -1.0%was 1,151

Persons Injured

264

▲ 0.4%was 263

Hit-and-Run Crashes

Note: "Persons Killed" (25) counts individual fatalities across all crash events. "Fatal" in the severity table below (25) 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

Overall crash volume in Litchfield County showed a slight upward trend, increasing by 44 incidents from 3,755 in 2021 to 3,799 in 2022. Despite this small rise in total crashes, the number of fatalities increased significantly by 31.6% (from 19 to 25), while total injuries saw a minor decrease of 1.0% (from 1,151 to 1,140).

264

Hit-and-Run Crashes — 2022

▲ 0.4% vs prior (263)

The frequency of hit-and-run crashes remained stable between the two periods. There were 264 hit-and-run incidents in 2022, compared to 263 in 2021. The corresponding rate showed a negligible change, decreasing slightly from 7.0% of all crashes in the prior year to 6.9% in the current year.

Vulnerable Road User Casualties

3

Pedestrians Killed

Prior: 1↑200.0%

0

Cyclists Killed

Prior: 1↓-100.0%

22

Motorists Killed

Prior: 17↑29.4%

32

Pedestrians Injured

Prior: 25↑28.0%

13

Cyclists Injured

Prior: 4↑225.0%

1,095

Motorists Injured

Prior: 1,122↓-2.4%

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 timing of crashes showed general consistency between the two periods, with collisions peaking during the afternoon commute. The peak hour shifted slightly later, from 3 p.m. in 2021 (330 crashes) to 4 p.m. in 2022 (337 crashes). Friday was the day with the most incidents in 2022, recording 619 crashes, compared to Thursday being the peak day in 2021 with 576 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 crashes resulting in some form of injury was unchanged at 23.4%, the severity of outcomes worsened year-over-year. The number of fatal crashes increased from 19 to 25, raising the fatal crash rate from 0.5% to 0.7% of all incidents. Crashes resulting in serious injuries also rose from 53 in 2021 to 58 in 2022.

Outcome by Severity (Crash Events)

Fatal25fatal crashes0.7%
↑31.6%prior 19
Serious Injury58serious injury crashes1.5%
↑9.4%prior 53
Minor Injury462minor injury crashes12.2%
↓-1.1%prior 467
Possible Injury370possible injury crashes9.7%
↑3.1%prior 359
No Injury2,884no injury crashes75.9%
↑0.9%prior 2,857

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 conditions under which crashes occurred were very similar year-over-year, with the majority of incidents in both periods happening during daylight (70.8% in 2022 vs. 69.4% in 2021) and on dry roads (76.0% in 2022 vs. 76.6% in 2021). The proportion of crashes on snowy or icy roads increased slightly, from 7.1% of all crashes in 2021 to 7.9% in 2022.

Weather

Clear2,911 (76.8%)
↑1.5%prior 2,867
Rain327 (8.6%)
↓-3.5%prior 339
Cloudy230 (6.1%)
↓-6.9%prior 247
Snow176 (4.6%)
↓-5.9%prior 187
Freezing Rain or Freezing Drizzle74 (2.0%)
↑51.0%prior 49
Blowing Snow41 (1.1%)
↑105.0%prior 20
Fog, Smog, Smoke23 (0.6%)
↓-8.0%prior 25
Sleet or Hail3 (0.1%)
Other2 (0.1%)
Severe Crosswinds1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash

Lighting

Daylight2,691 (71.0%)
↑3.3%prior 2,605
Dark-Not Lighted546 (14.4%)
↓-6.0%prior 581
Dark-Lighted418 (11.0%)
↓-4.1%prior 436
Dusk59 (1.6%)
↑13.5%prior 52
Dawn48 (1.3%)
↑14.3%prior 42
Dark-Unknown Lighting28 (0.7%)
↑27.3%prior 22
Other2 (0.1%)

Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field

Road Surface

Dry2,888 (76.2%)
↑0.3%prior 2,878
Wet534 (14.1%)
↓-0.9%prior 539
Snow177 (4.7%)
↓-5.9%prior 188
Ice / Frost123 (3.2%)
↑59.7%prior 77
Slush41 (1.1%)
↑46.4%prior 28
Mud, Dirt, Gravel12 (0.3%)
↓-33.3%prior 18
Other9 (0.2%)
Sand4 (0.1%)
↓-50.0%prior 8
Standing Water2 (0.1%)
Moving Water1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field

Vehicles & Demographics

The top five vehicle makes involved in crashes remained consistent, led by Ford in both years, though Toyota's involvement increased from 511 to 626 incidents, moving it from fifth to second place. Analysis of persons involved shows a demographic shift, with the proportion of those aged 16-20 decreasing from 11.6% to 10.5% and the 65+ age group increasing its share from 12.4% to 13.6%.

Top Vehicle Makes (6,326 vehicles)

1
FORD735 (11.6%)
↓-2.1%prior 751
2
TOYOTA626 (9.9%)
↑22.5%prior 511
3
HONDA616 (9.7%)
↑10.4%prior 558
4
SUBARU533 (8.4%)
↓-5.8%prior 566
5
CHEVROLET517 (8.2%)
↓-0.2%prior 518
6
NISSAN386 (6.1%)
↑6.0%prior 364
7
JEEP350 (5.5%)
↑2.3%prior 342
8
HYUNDAI241 (3.8%)
↑9.5%prior 220
9
VOLKSWAGEN179 (2.8%)
↑27.0%prior 141
10
DODGE177 (2.8%)
↓-1.7%prior 180

Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records

384 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (7,279 persons with recorded sex)

Male4,174 (57.3%)
↓-1.8%prior 4,251
Female3,105 (42.7%)
↓-3.6%prior 3,221

Source: Connecticut Crash Data · Csv Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes by speed limit saw an increase in incidents in 40 mph and 45 mph zones and a decrease in 30 mph zones. More significantly, the fatal crash rate in 40 mph zones more than doubled, from 1.0% in 2021 (4 fatalities) to 2.1% in 2022 (9 fatalities). The fatality rate in 65 mph zones also jumped from 1.0% to 2.3% year-over-year.

Fatal crashes by zone: 25 mph: 5 of 938 (0.533%) · 35 mph: 1 of 587 (0.17%) · 40 mph: 9 of 429 (2.098%) · 45 mph: 6 of 402 (1.493%) · 65 mph: 4 of 176 (2.273%)

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: September 10, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-12-31 (365 days)
  • Geographic scope: connecticut, CT
  • Total crash records analyzed: 3,799
  • Total persons involved: 8,074
  • Total vehicles involved: 6,326

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 September 10, 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

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