Yearly Traffic Safety Analysis

3,710 CRASHES IN
CONNECTICUT, CT
2025

All metrics benchmarked against2024

In Litchfield County, total crashes decreased by 4.8% from 3,895 in 2024 to 3,710 in 2025, with corresponding declines in fatalities and injuries. The most significant year-over-year shift was a 34% reduction in serious injury crashes, which fell from 82 to 54. Overall, the data indicates a general improvement in traffic safety metrics for the period.

3,710

▼ -4.7%was 3,895

Total Crash Events

13

▼ -7.1%was 14

Persons Killed

1,149

▼ -8.3%was 1,253

Persons Injured

272

▼ -7.2%was 293

Hit-and-Run Crashes

Note: "Persons Killed" (13) counts individual fatalities across all crash events. "Fatal" in the severity table below (13) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Litchfield County are on a downward trend. Total collisions fell by 4.8%, from 3,895 in 2024 to 3,710 in 2025. Similarly, total injuries decreased by 8.3% from 1,253 to 1,149, and fatalities dropped from 14 to 13, suggesting a general improvement in road safety outcomes for the year.

272

Hit-and-Run Crashes — 2025

▼ -7.2% vs prior (293)

Hit-and-run incidents trended downward in the current period. The total number of hit-and-run crashes decreased from 293 in 2024 to 272 in 2025. The hit-and-run rate, as a percentage of all crashes, also saw a slight reduction from 7.5% to 7.3%, indicating a modest improvement.

Vulnerable Road User Casualties

3

Pedestrians Killed

Prior: 1↑200.0%

0

Cyclists Killed

Prior: 0→0.0%

10

Motorists Killed

Prior: 13↓-23.1%

27

Pedestrians Injured

Prior: 27→0.0%

18

Cyclists Injured

Prior: 13↑38.5%

1,104

Motorists Injured

Prior: 1,213↓-9.0%

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes remained consistent year-over-year. Friday was the peak day for collisions in both 2025 (593 crashes) and 2024 (670 crashes). The 4 p.m. hour also remained the peak time for incidents in both periods, with 329 crashes in 2025 and 333 in 2024, highlighting the continued risk during the afternoon commute.

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the number of fatal crashes remained stable at 13 for both years, the overall severity of collisions lessened. There was a significant 34% decrease in serious injury crashes, from 82 in the prior year to 54 in the current year. Consequently, the proportion of crashes resulting in no injuries increased from 75.1% to 76.3% of all incidents.

Outcome by Severity (Crash Events)

Fatal13fatal crashes0.4%
→0.0%prior 13
Serious Injury54serious injury crashes1.5%
↓-34.1%prior 82
Minor Injury501minor injury crashes13.5%
↓-6.0%prior 533
Possible Injury310possible injury crashes8.4%
↓-8.8%prior 340
No Injury2,832no injury crashes76.3%
↓-3.2%prior 2,927

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The environmental conditions under which crashes occurred saw minimal change between the two periods. The vast majority of incidents in both 2025 and 2024 happened in clear weather (78.0% and 78.8% respectively) and on dry roads (76.1% in both years). The proportion of crashes during daylight hours also remained stable, accounting for 71.4% of crashes in 2025 versus 70.7% in 2024.

Weather

Clear2,894 (78.3%)
↓-5.7%prior 3,068
Rain342 (9.3%)
↑0.9%prior 339
Cloudy189 (5.1%)
↑20.4%prior 157
Snow163 (4.4%)
↓-23.8%prior 214
Freezing Rain or Freezing Drizzle38 (1.0%)
↓-2.6%prior 39
Blowing Snow26 (0.7%)
↓-7.1%prior 28
Fog, Smog, Smoke21 (0.6%)
↓-19.2%prior 26
Sleet or Hail10 (0.3%)
→0.0%prior 10
Severe Crosswinds9 (0.2%)
↑50.0%prior 6
Other5 (0.1%)

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight2,648 (71.6%)
↓-3.8%prior 2,754
Dark-Not Lighted494 (13.4%)
↓-6.1%prior 526
Dark-Lighted424 (11.5%)
↓-7.2%prior 457
Dusk66 (1.8%)
↑8.2%prior 61
Dawn37 (1.0%)
↓-32.7%prior 55
Dark-Unknown Lighting21 (0.6%)
↓-22.2%prior 27
Other7 (0.2%)
↓-12.5%prior 8

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry2,823 (76.4%)
↓-4.8%prior 2,964
Wet541 (14.6%)
↓-5.3%prior 571
Snow165 (4.5%)
↓-8.8%prior 181
Ice / Frost95 (2.6%)
↑9.2%prior 87
Slush51 (1.4%)
↓-15.0%prior 60
Mud, Dirt, Gravel13 (0.4%)
↓-27.8%prior 18
Other4 (0.1%)
↓-33.3%prior 6
Sand2 (0.1%)
Moving Water2 (0.1%)

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Ford, Honda, and Toyota leading in both years, although all saw fewer incidents. A slight change in rankings occurred as Subaru (590 crashes) overtook Chevrolet (518 crashes) for the fourth position in 2025. The total number of vehicles involved in collisions declined from 6,575 to 6,247, in line with the overall drop in crashes.

Top Vehicle Makes (6,247 vehicles)

1
FORD699 (11.2%)
↓-3.1%prior 721
2
HONDA638 (10.2%)
↓-2.6%prior 655
3
TOYOTA618 (9.9%)
↓-4.0%prior 644
4
SUBARU590 (9.4%)
↑2.1%prior 578
5
CHEVROLET518 (8.3%)
↓-10.1%prior 576
6
NISSAN324 (5.2%)
↓-5.0%prior 341
7
JEEP304 (4.9%)
↓-5.6%prior 322
8
HYUNDAI253 (4%)
↓-12.5%prior 289
9
VOLKSWAGEN162 (2.6%)
↓-6.4%prior 173
10
KIA152 (2.4%)
↑10.1%prior 138

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

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

Sex Distribution (7,138 persons with recorded sex)

Male4,095 (57.4%)
↓-5.5%prior 4,335
Female3,043 (42.6%)
↓-3.3%prior 3,148

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events

Speed Limit Zones

Crash distribution by speed zone remained similar year-over-year, with 25 mph and 35 mph zones seeing the highest crash volumes in 2025. A notable shift occurred in fatal crash locations; while fatalities in 45 mph zones decreased from 6 to 4, the 40 mph zone saw fatalities increase from 3 to 4. Two fatalities occurred in 50 mph zones in 2025, where none had occurred in the prior year.

Fatal crashes by zone: 25 mph: 2 of 847 (0.236%) · 40 mph: 4 of 407 (0.983%) · 45 mph: 4 of 385 (1.039%) · 50 mph: 2 of 77 (2.597%) · 65 mph: 1 of 185 (0.541%)

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
  • Report generated: September 10, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: connecticut, CT
  • Total crash records analyzed: 3,710
  • Total persons involved: 7,906
  • Total vehicles involved: 6,247

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: 2025." Published September 10, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2025-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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