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Yearly Traffic Safety Analysis

3,497 CRASHES IN
CONNECTICUT, CT
2025

All metrics benchmarked against2024

In 2025, Middlesex County recorded 3,497 total crashes, a 5.0% decrease from the 3,682 crashes documented in 2024. While total collisions declined, the number of fatalities increased slightly from 14 to 15. The most significant year-over-year change was a 13.1% reduction in total injuries, which fell from 1,107 in the prior period to 962 in the current period.

3,497

▼ -5.0%was 3,682

Total Crash Events

▲ 7.1%was 14

Persons Killed

962

▼ -13.1%was 1,107

Persons Injured

290

▼ -3.7%was 301

Hit-and-Run Crashes

Note: "Persons Killed" (15) counts individual fatalities across all crash events. "Fatal" in the severity table below (15) 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

Year-over-year data indicates a downward trend in overall crash incidents in Middlesex County. Total crashes decreased by 5.0%, from 3,682 in 2024 to 3,497 in 2025. This was accompanied by a 13.1% decline in persons injured, though the number of fatalities rose by one, from 14 to 15.

290

Hit-and-Run Crashes — 2025

▼ -3.7% vs prior (301)

The total number of hit-and-run incidents decreased from 301 in 2024 to 290 in 2025. However, because the overall number of crashes fell at a greater rate, the hit-and-run rate saw a slight increase. Hit-and-runs constituted 8.3% of all crashes in the current period, up from 8.2% in the prior year, indicating a slight upward trend in the proportion of crashes involving a fleeing driver.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 2↓-50.0%

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 12↑16.7%

Pedestrians Injured

Prior: 23↓-4.3%

Cyclists Injured

Prior: 10↑40.0%

926

Motorists Injured

Prior: 1,074↓-13.8%

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 remained largely consistent year-over-year. Friday continued to be the day with the highest number of crashes, with 584 incidents in 2025 compared to 607 in 2024. The 3 PM hour also held as the peak time for collisions in both periods, accounting for 332 crashes in the current year and 327 in the prior year. Crashes on Mondays, however, increased from 496 to 550, making it the second-most frequent day for crashes in 2025.

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 total crashes decreased, the fatal crash rate rose from 0.38% in 2024 to 0.43% in 2025, with fatal crashes increasing from 14 to 15. The proportion of crashes resulting in any injury (Serious, Minor, or Possible) declined from 22.1% to 20.8%. Specifically, crashes involving possible injuries saw the largest proportional drop, falling from 8.7% to 7.3% of all incidents. Consequently, the share of crashes with no reported injuries increased from 77.5% to 78.8%.

Outcome by Severity (Crash Events)

Fatal15fatal crashes0.4%

↑7.1%prior 14

Serious Injury45serious injury crashes1.3%

↓-15.1%prior 53

Minor Injury427minor injury crashes12.2%

↓-3.4%prior 442

Possible Injury254possible injury crashes7.3%

↓-20.6%prior 320

No Injury2,756no injury crashes78.8%

↓-3.4%prior 2,853

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 conditions remained broadly similar between the two periods, with the majority of incidents in both years occurring in daylight (73.3% in 2025 vs. 74.5% in 2024) and on dry roads (81.7% vs. 81.6%). However, there was a noticeable shift in crashes related to adverse conditions. The proportion of crashes on wet roads decreased from 13.6% to 11.8%, while the share of crashes on roads with snow, ice, or slush increased from 4.2% to 5.9%. Crashes in dark, unlighted conditions also increased proportionally from 6.8% to 8.1% of all incidents.

Weather

Clear2,890 (82.9%)

↓-4.5%prior 3,025

Rain278 (8.0%)

↓-21.7%prior 355

Snow119 (3.4%)

↑21.4%prior 98

Cloudy117 (3.4%)

↓-15.8%prior 139

Blowing Snow29 (0.8%)

↑190.0%prior 10

Freezing Rain or Freezing Drizzle28 (0.8%)

↑40.0%prior 20

Fog, Smog, Smoke13 (0.4%)

↑30.0%prior 10

Other8 (0.2%)

Severe Crosswinds3 (0.1%)

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

Lighting

Daylight2,565 (73.7%)

↓-6.6%prior 2,745

Dark-Lighted561 (16.1%)

↓-2.3%prior 574

Dark-Not Lighted283 (8.1%)

↑13.2%prior 250

Dusk39 (1.1%)

↓-23.5%prior 51

Dawn20 (0.6%)

↓-20.0%prior 25

Dark-Unknown Lighting9 (0.3%)

↓-18.2%prior 11

Other3 (0.1%)

↓-40.0%prior 5

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

Road Surface

Dry2,856 (81.8%)

↓-5.0%prior 3,005

Wet412 (11.8%)

↓-17.6%prior 500

Snow114 (3.3%)

↑70.1%prior 67

Ice / Frost69 (2.0%)

↑32.7%prior 52

Slush25 (0.7%)

↓-28.6%prior 35

Mud, Dirt, Gravel9 (0.3%)

↑28.6%prior 7

Sand2 (0.1%)

Standing Water2 (0.1%)

Other1 (0.0%)

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

Vehicles & Demographics

The top five vehicle makes involved in crashes remained consistent, with Toyota (770), Honda (639), and Ford (572) leading in the current period. While the makes were the same, the number of vehicles from each of the top five manufacturers involved in crashes decreased compared to the prior year. Analysis of persons involved shows a demographic shift, with the proportion of individuals aged 65 and older decreasing from 15.2% of all persons in 2024 to 13.8% in 2025. Conversely, the 21-25 age group saw a slight proportional increase from 10.4% to 10.8%.

Top Vehicle Makes (6,285 vehicles)

TOYOTA770 (12.3%)

↓-0.4%prior 773

HONDA639 (10.2%)

↓-12.2%prior 728

FORD572 (9.1%)

↓-15.1%prior 674

NISSAN474 (7.5%)

↓-3.7%prior 492

CHEVROLET451 (7.2%)

↓-11.9%prior 512

SUBARU397 (6.3%)

↓-1.7%prior 404

JEEP297 (4.7%)

↑2.4%prior 290

HYUNDAI279 (4.4%)

↑13.9%prior 245

KIA162 (2.6%)

↓-10.0%prior 180

VOLKSWAGEN155 (2.5%)

↑6.9%prior 145

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

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

Sex Distribution (7,608 persons with recorded sex)

Male4,292 (56.4%)

↓-4.2%prior 4,482

Female3,316 (43.6%)

↓-8.2%prior 3,613

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

Speed Limit Zones

The distribution of crashes across speed zones saw minor changes year-over-year, with a slight proportional shift away from low-speed (30 mph or less) and high-speed (65 mph) zones. In 2025, 33.3% of crashes occurred in mid-range zones (35-55 mph), up from 32.6% in 2024. There was a notable shift in where fatal crashes occurred; fatalities in 65 mph zones decreased from 4 to 3, while fatal crashes in 25 mph and 35 mph zones collectively increased from 5 to 7.

Fatal crashes by zone: 25 mph: 3 of 696 (0.431%) · 30 mph: 1 of 294 (0.34%) · 35 mph: 4 of 560 (0.714%) · 40 mph: 2 of 311 (0.643%) · 45 mph: 2 of 255 (0.784%) · 65 mph: 3 of 644 (0.466%)

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: 3,497
Total persons involved: 7,974
Total vehicles involved: 6,285

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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