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Monthly Traffic Safety Analysis

7,979 CRASHES IN
CONNECTICUT, CT
FEBRUARY 2025

All metrics benchmarked againstFebruary 2024

In February 2025, Connecticut recorded 7,979 vehicle crashes, a 4.2% increase from the 7,655 crashes in February 2024. Despite the rise in total collisions, the most notable year-over-year change was a significant decrease in traffic fatalities, which fell by 75% from 32 to 8.

7,979

▲ 4.2%was 7,655

Total Crash Events

▼ -75.0%was 32

Persons Killed

2,094

▼ -4.7%was 2,197

Persons Injured

949

▼ -2.7%was 975

Hit-and-Run Crashes

Note: "Persons Killed" (8) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2025-02-01 to 2025-02-28 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic collisions in Connecticut showed a slight upward trend in February 2025 compared to the previous year, increasing by 324 incidents. However, the severity of these crashes decreased, with total injuries falling by 4.7% from 2,197 to 2,094 and fatalities dropping from 32 to 8.

949

Hit-and-Run Crashes — February 2025

▼ -2.7% vs prior (975)

Hit-and-run incidents saw a modest decline in February 2025 compared to the previous year. The total number of hit-and-run crashes decreased from 975 to 949. As a percentage of all collisions, the hit-and-run rate also trended downward, falling from 12.7% in February 2024 to 11.9% in February 2025.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 4→0.0%

Cyclists Killed

Prior: 0→%

Motorists Killed

Prior: 28↓-89.3%

Pedestrians Injured

Prior: 96↓-39.6%

Cyclists Injured

Prior: 9↓-11.1%

2,028

Motorists Injured

Prior: 2,092↓-3.1%

Source: Connecticut Crash Data · Csv Open Data · 2025-02-01 to 2025-02-28 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted slightly year-over-year. The peak day for crashes moved from Thursday (1,365 crashes) in the prior period to Friday (1,283 crashes) in the current period. Similarly, the peak hour for collisions shifted an hour later, from the 3 p.m. hour (635 crashes) in February 2024 to the 4 p.m. hour (658 crashes) in February 2025.

Source: Connecticut Crash Data · Csv Open Data · 2025-02-01 to 2025-02-28 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2025-02-01 to 2025-02-28 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity markedly decreased in February 2025 compared to the same month in 2024. The number of fatal crashes dropped from 26 to 5, and the fatal crash rate fell from 0.34% to 0.06%. The proportion of crashes resulting in any injury (Serious, Minor, or Possible) also declined from 21.3% to 19.9% of all crashes, while no-injury crashes increased as a share of the total from 78.4% to 80.1%.

Severity is per crash event (most severe injury). 5 fatal crash events resulted in 8 persons killed.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.1%

↓-80.8%prior 26

Serious Injury58serious injury crashes0.7%

↓-15.9%prior 69

Minor Injury762minor injury crashes9.6%

↓-3.7%prior 791

Possible Injury763possible injury crashes9.6%

↓-1.0%prior 771

No Injury6,391no injury crashes80.1%

↑6.6%prior 5,998

Source: Connecticut Crash Data · Csv Open Data · 2025-02-01 to 2025-02-28 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2025-02-01 to 2025-02-28 · Most severe injury per crash record

Road & Environmental Conditions

There was a notable shift in the conditions under which crashes occurred. In February 2025, crashes in snow conditions more than doubled, from 516 to 1,053, and collisions on roads with snow, ice, or slush increased from 717 to 1,837. Consequently, the share of crashes on dry road surfaces fell from 80.9% in the prior year to 65.9% in the current period. Lighting conditions remained proportionally consistent between the two periods.

Weather

Clear5,923 (74.6%)

↓-6.7%prior 6,351

Snow1,053 (13.3%)

↑104.1%prior 516

Rain279 (3.5%)

↓-24.6%prior 370

Freezing Rain or Freezing Drizzle251 (3.2%)

↑991.3%prior 23

Cloudy246 (3.1%)

↑5.1%prior 234

Blowing Snow129 (1.6%)

↑51.8%prior 85

Sleet or Hail35 (0.4%)

Severe Crosswinds10 (0.1%)

Fog, Smog, Smoke9 (0.1%)

↓-67.9%prior 28

Other7 (0.1%)

↑40.0%prior 5

Source: Connecticut Crash Data · Csv Open Data · 2025-02-01 to 2025-02-28 · Weather condition at time of crash

Lighting

Daylight5,086 (64.3%)

↑1.8%prior 4,998

Dark-Lighted1,966 (24.8%)

↑5.6%prior 1,861

Dark-Not Lighted601 (7.6%)

↑18.3%prior 508

Dusk103 (1.3%)

↑2.0%prior 101

Dawn74 (0.9%)

↑39.6%prior 53

Dark-Unknown Lighting64 (0.8%)

↑3.2%prior 62

Other18 (0.2%)

↑80.0%prior 10

Source: Connecticut Crash Data · Csv Open Data · 2025-02-01 to 2025-02-28 · Lighting condition field

Road Surface

Dry5,258 (66.2%)

↓-15.1%prior 6,193

Snow1,002 (12.6%)

↑92.7%prior 520

Wet827 (10.4%)

↑20.9%prior 684

Ice / Frost460 (5.8%)

↑240.7%prior 135

Slush375 (4.7%)

↑504.8%prior 62

Sand5 (0.1%)

↓-16.7%prior 6

Other4 (0.1%)

↓-33.3%prior 6

Mud, Dirt, Gravel3 (0.0%)

↓-50.0%prior 6

Moving Water2 (0.0%)

Standing Water1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2025-02-01 to 2025-02-28 · Road surface condition field

Vehicles & Demographics

The profile of vehicles and persons involved in crashes remained largely consistent year-over-year. The top three vehicle makes involved in collisions were Honda, Toyota, and Ford in both February 2024 and February 2025, with minimal change in their respective counts. Similarly, the age distribution of persons involved in crashes showed no significant shifts, with the 26-34 age group representing the largest cohort in both periods.

Top Vehicle Makes (14,473 vehicles)

HONDA1,588 (11%)

↓-2.9%prior 1,635

TOYOTA1,566 (10.8%)

↑0.4%prior 1,559

FORD1,313 (9.1%)

↑6.9%prior 1,228

NISSAN967 (6.7%)

↓-6.3%prior 1,032

CHEVROLET936 (6.5%)

↑4.9%prior 892

SUBARU739 (5.1%)

↑4.7%prior 706

JEEP631 (4.4%)

↓-4.8%prior 663

HYUNDAI600 (4.1%)

↓-2.4%prior 615

KIA346 (2.4%)

↑0.9%prior 343

BMW318 (2.2%)

↑2.3%prior 311

Source: Connecticut Crash Data · Csv Open Data · 2025-02-01 to 2025-02-28 · Vehicle unit records

1,079 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (16,494 persons with recorded sex)

Male9,599 (58.2%)

↑2.3%prior 9,386

Female6,895 (41.8%)

↓-3.3%prior 7,132

Source: Connecticut Crash Data · Csv Open Data · 2025-02-01 to 2025-02-28 · Person-level records linked to crash events

Speed Limit Zones

Year-over-year data shows a shift in crashes toward higher speed zones, with collisions in 55 mph and 65 mph zones increasing by 123 and 185, respectively. In contrast, crashes in 25 mph zones decreased from 2,316 to 2,217. Despite the increase in crashes at higher speeds, the number of associated fatal crashes fell significantly, with fatalities in 65 mph zones dropping from 6 to 0 and fatalities in 25 mph zones falling from 8 to 1.

Fatal crashes by zone: 25 mph: 1 of 2,217 (0.045%) · 30 mph: 1 of 618 (0.162%) · 35 mph: 1 of 912 (0.11%) · 55 mph: 1 of 757 (0.132%)

Source: Connecticut Crash Data · Csv Open Data · 2025-02-01 to 2025-02-28 · 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: 2025-02-01 through 2025-02-28
Report generated: August 20, 2026

Data Coverage

Reporting period: 2025-02-01 through 2025-02-28 (28 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 7,979
Total persons involved: 17,868
Total vehicles involved: 14,473

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: February 2025." Published August 20, 2026. Reporting period: 2025-02-01 to 2025-02-28. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/february-2025-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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