Yearly Traffic Safety Analysis

3,666 CRASHES IN
CONNECTICUT, CT
2023

All metrics benchmarked against2022

In Litchfield County, total traffic crashes decreased by 3.5% from 3,799 in 2022 to 3,666 in 2023. Despite this overall reduction in collisions, the number of people injured increased by 7.9% from 1,140 to 1,230, and total fatalities rose from 25 to 27 year-over-year. This divergence, with fewer total crashes but more severe outcomes, represents the most notable shift in the county's crash data.

3,666

▼ -3.5%was 3,799

Total Crash Events

▲ 8.0%was 25

Persons Killed

1,230

▲ 7.9%was 1,140

Persons Injured

240

▼ -9.1%was 264

Hit-and-Run Crashes

Note: "Persons Killed" (27) counts individual fatalities across all crash events. "Fatal" in the severity table below (26) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety trends in Litchfield County show a mixed picture. While the total number of crashes declined by 3.5% from 2022 to 2023, the severity of these incidents worsened. The number of people killed in crashes increased by 8.0% (from 25 to 27), and the number of people injured grew by 7.9% (from 1,140 to 1,230).

240

Hit-and-Run Crashes — 2023

▼ -9.1% vs prior (264)

Hit-and-run incidents saw a decline between the two periods. The absolute number of hit-and-run crashes fell by 9.1%, from 264 in 2022 to 240 in 2023. Correspondingly, the hit-and-run rate, which measures these events as a percentage of total crashes, decreased from 6.9% to 6.5%.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 3↓-100.0%

Cyclists Killed

Prior: 0→%

Motorists Killed

Prior: 22↑18.2%

Pedestrians Injured

Prior: 32↑3.1%

Cyclists Injured

Prior: 13→0.0%

1,184

Motorists Injured

Prior: 1,095↑8.1%

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · 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 year-over-year. Friday was the peak day for crashes in both 2023 (594 crashes) and 2022 (619 crashes). There was a slight shift in the peak hour of the day, moving from the 4 p.m. hour in 2022 (337 crashes) to the 5 p.m. hour in 2023 (327 crashes).

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased from 2022 to 2023. The fatal crash rate rose from 0.66 to 0.71 fatal crashes per 100 total crashes. The proportion of crashes resulting in any type of injury also increased, from 23.4% in 2022 to 24.8% in 2023. This was driven primarily by a rise in minor injury crashes, which accounted for 13.5% of all crashes in 2023 compared to 12.2% in the prior year.

Severity is per crash event (most severe injury). 26 fatal crash events resulted in 27 persons killed.

Outcome by Severity (Crash Events)

Fatal26fatal crashes0.7%

↑4.0%prior 25

Serious Injury51serious injury crashes1.4%

↓-12.1%prior 58

Minor Injury494minor injury crashes13.5%

↑6.9%prior 462

Possible Injury362possible injury crashes9.9%

↓-2.2%prior 370

No Injury2,733no injury crashes74.5%

↓-5.2%prior 2,884

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The conditions under which crashes occurred were broadly similar between 2022 and 2023. In both years, crashes predominantly occurred in daylight (71.1% in 2023 vs. 70.8% in 2022) and on dry roads (78.6% in 2023 vs. 76.0% in 2022). There was a slight increase in the proportion of crashes happening on wet roads, which rose from 14.1% of crashes in 2022 to 16.0% in 2023.

Weather

Clear2,893 (79.1%)

↓-0.6%prior 2,911

Rain379 (10.4%)

↑15.9%prior 327

Cloudy211 (5.8%)

↓-8.3%prior 230

Snow107 (2.9%)

↓-39.2%prior 176

Fog, Smog, Smoke27 (0.7%)

↑17.4%prior 23

Blowing Snow19 (0.5%)

↓-53.7%prior 41

Freezing Rain or Freezing Drizzle13 (0.4%)

↓-82.4%prior 74

Other6 (0.2%)

Severe Crosswinds2 (0.1%)

Sleet or Hail1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight2,608 (71.5%)

↓-3.1%prior 2,691

Dark-Not Lighted469 (12.9%)

↓-14.1%prior 546

Dark-Lighted434 (11.9%)

↑3.8%prior 418

Dusk59 (1.6%)

→0.0%prior 59

Dawn43 (1.2%)

↓-10.4%prior 48

Dark-Unknown Lighting25 (0.7%)

↓-10.7%prior 28

Other8 (0.2%)

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry2,880 (78.6%)

↓-0.3%prior 2,888

Wet585 (16.0%)

↑9.6%prior 534

Snow85 (2.3%)

↓-52.0%prior 177

Ice / Frost56 (1.5%)

↓-54.5%prior 123

Slush26 (0.7%)

↓-36.6%prior 41

Mud, Dirt, Gravel13 (0.4%)

↑8.3%prior 12

Oil4 (0.1%)

Standing Water4 (0.1%)

Sand3 (0.1%)

Moving Water3 (0.1%)

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes showed little change, with Ford, Toyota, Honda, Chevrolet, and Subaru being the top five in both 2023 and 2022. An analysis of persons involved in crashes reveals a demographic shift. The proportion of individuals aged 16-20 increased from 10.5% of all persons involved in 2022 to 11.8% in 2023, representing the most significant change in age group representation.

Top Vehicle Makes (6,225 vehicles)

FORD682 (11%)

↓-7.2%prior 735

TOYOTA634 (10.2%)

↑1.3%prior 626

HONDA626 (10.1%)

↑1.6%prior 616

CHEVROLET542 (8.7%)

↑4.8%prior 517

SUBARU536 (8.6%)

↑0.6%prior 533

NISSAN369 (5.9%)

↓-4.4%prior 386

JEEP334 (5.4%)

↓-4.6%prior 350

HYUNDAI246 (4%)

↑2.1%prior 241

DODGE165 (2.7%)

↓-6.8%prior 177

VOLKSWAGEN151 (2.4%)

↓-15.6%prior 179

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (7,233 persons with recorded sex)

Male4,164 (57.6%)

↓-0.2%prior 4,174

Female3,069 (42.4%)

↓-1.2%prior 3,105

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events

Speed Limit Zones

The overall distribution of crashes across different speed zones remained relatively stable year-over-year. However, the locations of fatal crashes shifted. In 2022, the 40 mph zone had the highest number of fatal crashes at nine. In 2023, fatal crashes in the 40 mph zone decreased to five, while the 45 mph zone saw the most fatal crashes, with seven.

Fatal crashes by zone: 25 mph: 2 of 917 (0.218%) · 30 mph: 3 of 488 (0.615%) · 35 mph: 5 of 559 (0.894%) · 40 mph: 5 of 402 (1.244%) · 45 mph: 7 of 345 (2.029%) · 50 mph: 1 of 63 (1.587%) · 65 mph: 2 of 189 (1.058%) · 88 mph: 1 of 225 (0.444%)

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
Report generated: September 10, 2026

Data Coverage

Reporting period: 2023-01-01 through 2023-12-31 (365 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 3,666
Total persons involved: 8,101
Total vehicles involved: 6,225

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: 2023." Published September 10, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2023-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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3,666 CRASHES INCONNECTICUT, CT2023