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Yearly Traffic Safety Analysis

28,943 CRASHES IN
CONNECTICUT, CT
2025

All metrics benchmarked against2024

In 2025, Fairfield County recorded 28,943 vehicle crashes, a 4.0% decrease from the 30,156 crashes reported in 2024. This overall reduction in collisions was accompanied by a significant 25.4% year-over-year drop in total fatalities, which fell from 59 to 44.

28,943

▼ -4.0%was 30,156

Total Crash Events

▼ -25.4%was 59

Persons Killed

8,259

▼ -7.1%was 8,888

Persons Injured

3,508

▼ -0.9%was 3,540

Hit-and-Run Crashes

Note: "Persons Killed" (44) counts individual fatalities across all crash events. "Fatal" in the severity table below (34) 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

Overall traffic safety trends in Fairfield County showed improvement from 2024 to 2025. Total crashes decreased by 4.0%, from 30,156 to 28,943. Correspondingly, total injuries fell by 7.1% from 8,888 to 8,259, and total fatalities saw a substantial 25.4% decline from 59 to 44.

3,508

Hit-and-Run Crashes — 2025

▼ -0.9% vs prior (3,540)

The total number of hit-and-run crashes in Fairfield County decreased slightly from 3,540 in 2024 to 3,508 in 2025. However, because total crashes declined at a faster pace, the hit-and-run rate as a percentage of all crashes increased. In 2025, hit-and-runs accounted for 12.1% of all collisions, up from 11.7% in the previous year.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 13↑15.4%

Cyclists Killed

Prior: 0→%

Motorists Killed

Prior: 45↓-40.0%

Other Killed

Prior: 1↓-100.0%

393

Pedestrians Injured

Prior: 424↓-7.3%

Cyclists Injured

Prior: 99↓-15.2%

7,781

Motorists Injured

Prior: 8,365↓-7.0%

Other Injured

Prior: 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 temporal patterns of crashes in Fairfield County remained largely consistent year-over-year. Friday was the day with the most crashes in both 2025 (4,690) and 2024 (4,864). Similarly, the 5 PM hour was the peak time for collisions in both periods, with 2,472 crashes in 2025 and 2,516 in the prior year, indicating no significant shift in daily or weekly crash timing.

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

The severity of crashes saw a positive shift from 2024 to 2025, with the rate of fatal crashes decreasing from 0.16% to 0.12% of all collisions. While the proportion of serious injury crashes remained stable at 1.2% in both periods, there was a slight decrease in 'Possible Injury' crashes (from 10.5% to 10.1%). Crashes resulting in no injury increased proportionally from 78.1% to 78.5% of all incidents.

Severity is per crash event (most severe injury). 34 fatal crash events resulted in 44 persons killed.

Outcome by Severity (Crash Events)

Fatal34fatal crashes0.1%

↓-29.2%prior 48

Serious Injury352serious injury crashes1.2%

↑1.1%prior 348

Minor Injury2,938minor injury crashes10.2%

↓-3.2%prior 3,035

Possible Injury2,912possible injury crashes10.1%

↓-8.0%prior 3,165

No Injury22,707no injury crashes78.5%

↓-3.6%prior 23,560

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 distribution of crashes across different environmental conditions remained remarkably stable between 2024 and 2025. In both years, approximately 84% of all crashes occurred in clear weather and on dry road surfaces. Crashes during daylight hours also held steady, accounting for 70.6% of incidents in 2025 compared to 69.9% in 2024. There were no significant year-over-year shifts in the proportion of crashes occurring in adverse weather, lighting, or road surface conditions.

Weather

Clear24,344 (84.6%)

↓-4.0%prior 25,356

Rain2,190 (7.6%)

↓-17.5%prior 2,656

Cloudy1,123 (3.9%)

↓-2.5%prior 1,152

Snow814 (2.8%)

↑51.6%prior 537

Freezing Rain or Freezing Drizzle107 (0.4%)

↓-0.9%prior 108

Blowing Snow106 (0.4%)

↑53.6%prior 69

Fog, Smog, Smoke44 (0.2%)

↑12.8%prior 39

Other19 (0.1%)

→0.0%prior 19

Sleet or Hail15 (0.1%)

↓-11.8%prior 17

Severe Crosswinds13 (0.0%)

→0.0%prior 13

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

Lighting

Daylight20,420 (71.1%)

↓-3.2%prior 21,089

Dark-Lighted5,919 (20.6%)

↓-5.5%prior 6,265

Dark-Not Lighted1,646 (5.7%)

↓-6.0%prior 1,751

Dusk317 (1.1%)

↓-7.8%prior 344

Dark-Unknown Lighting192 (0.7%)

↓-3.0%prior 198

Dawn166 (0.6%)

↓-8.3%prior 181

Other53 (0.2%)

↓-20.9%prior 67

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

Road Surface

Dry24,111 (83.8%)

↓-4.5%prior 25,247

Wet3,412 (11.9%)

↓-11.1%prior 3,839

Snow710 (2.5%)

↑82.5%prior 389

Ice / Frost332 (1.2%)

↑9.2%prior 304

Slush146 (0.5%)

↑60.4%prior 91

Mud, Dirt, Gravel16 (0.1%)

↑45.5%prior 11

Other12 (0.0%)

↓-25.0%prior 16

Standing Water8 (0.0%)

↓-50.0%prior 16

Moving Water6 (0.0%)

↓-57.1%prior 14

Sand5 (0.0%)

↓-50.0%prior 10

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

Vehicles & Demographics

Vehicle and person demographics involved in crashes showed little change year-over-year. The top five vehicle makes involved in collisions were identical in both 2025 and 2024: Honda, Toyota, Ford, Nissan, and Chevrolet, in that order. The age distribution of persons involved in crashes also remained consistent, with the 35-44 age group being the largest cohort in 2025 (16.5% of persons) compared to 16.3% in 2024.

Top Vehicle Makes (55,768 vehicles)

HONDA7,104 (12.7%)

↓-5.5%prior 7,519

TOYOTA6,630 (11.9%)

↓-2.4%prior 6,793

FORD4,410 (7.9%)

↓-7.3%prior 4,758

NISSAN3,732 (6.7%)

↓-8.5%prior 4,079

CHEVROLET3,264 (5.9%)

↓-6.2%prior 3,479

JEEP2,689 (4.8%)

↓-3.1%prior 2,774

SUBARU2,688 (4.8%)

↓-2.9%prior 2,768

HYUNDAI2,226 (4%)

↑0.6%prior 2,213

BMW1,818 (3.3%)

↓-4.1%prior 1,896

MAZDA1,203 (2.2%)

↑7.1%prior 1,123

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

4,229 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (63,191 persons with recorded sex)

Male36,925 (58.4%)

↓-4.3%prior 38,577

Female26,266 (41.6%)

↓-6.0%prior 27,939

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

Speed Limit Zones

While the overall distribution of crashes across different speed zones remained similar year-over-year, there was a notable shift in where fatal crashes occurred. Crashes in 25 mph zones saw a significant reduction in fatalities, dropping from 21 in 2024 to 8 in 2025. Fatalities in 40 mph zones also decreased from 8 to 5. The number of fatal crashes in 55 mph zones remained relatively stable, with 8 in 2025 compared to 9 in the prior year.

Fatal crashes by zone: 1 mph: 2 of 6,462 (0.031%) · 25 mph: 8 of 9,564 (0.084%) · 30 mph: 5 of 1,626 (0.308%) · 35 mph: 5 of 1,885 (0.265%) · 40 mph: 5 of 1,005 (0.498%) · 55 mph: 8 of 5,335 (0.15%) · 65 mph: 1 of 385 (0.26%)

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: August 20, 2026

Data Coverage

Reporting period: 2025-01-01 through 2025-12-31 (365 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 28,943
Total persons involved: 68,187
Total vehicles involved: 55,768

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 August 20, 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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