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Monthly Traffic Safety Analysis

8,806 CRASHES IN
CONNECTICUT, CT
NOVEMBER 2025

All metrics benchmarked againstNovember 2024

In November 2025, there were 8,806 total traffic crashes, a 3.8% decrease from the 9,154 crashes recorded in November 2024. Despite the overall reduction in collisions, the most notable year-over-year shift was a 22.7% increase in fatalities, which rose from 22 to 27. This suggests that while fewer crashes occurred, they were more severe in outcome.

8,806

▼ -3.8%was 9,154

Total Crash Events

▲ 22.7%was 22

Persons Killed

2,619

▼ -6.4%was 2,799

Persons Injured

1,201

▲ 4.1%was 1,154

Hit-and-Run Crashes

Note: "Persons Killed" (27) counts individual fatalities across all crash events. "Fatal" in the severity table below (25) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2025-11-01 to 2025-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash and injury trends were downward in November 2025 compared to the previous year. Total crashes fell by 3.8% from 9,154 to 8,806, and total injuries decreased by 6.4% from 2,799 to 2,619. However, this positive trend was countered by a significant 22.7% increase in fatalities, which grew from 22 to 27, indicating a rise in crash severity.

1,201

Hit-and-Run Crashes — November 2025

▲ 4.1% vs prior (1,154)

The number of hit-and-run incidents increased from 1,154 in November 2024 to 1,201 in November 2025. This rise, coupled with the decrease in overall collisions, caused the hit-and-run rate to climb from 12.6% to 13.6%. This indicates an upward trend for hit-and-run crashes, both in absolute numbers and as a proportion of all collisions.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 7↑28.6%

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 15↑20.0%

127

Pedestrians Injured

Prior: 127→0.0%

Cyclists Injured

Prior: 34↓-20.6%

2,465

Motorists Injured

Prior: 2,638↓-6.6%

Source: Connecticut Crash Data · Csv Open Data · 2025-11-01 to 2025-11-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak hour for crashes remained consistent at 5 p.m. in both periods, with 1,059 crashes in November 2025 and 1,011 in November 2024. However, the peak day for collisions shifted from Friday (1,771 crashes) in the prior year to Saturday (1,348 crashes) in the current period. Sunday crashes also increased notably, rising from 866 to 1,161 year-over-year.

Source: Connecticut Crash Data · Csv Open Data · 2025-11-01 to 2025-11-30 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2025-11-01 to 2025-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes worsened year-over-year, with the fatal crash rate increasing from 0.22% to 0.28%. The absolute number of fatal crashes rose from 20 in November 2024 to 25 in November 2025. The number of serious injury crashes also increased slightly from 90 to 95. In contrast, crashes resulting in minor injuries decreased from 1,055 to 965.

Severity is per crash event (most severe injury). 25 fatal crash events resulted in 27 persons killed.

Outcome by Severity (Crash Events)

Fatal25fatal crashes0.3%

↑25.0%prior 20

Serious Injury95serious injury crashes1.1%

↑5.6%prior 90

Minor Injury965minor injury crashes11%

↓-8.5%prior 1,055

Possible Injury924possible injury crashes10.5%

↓-2.7%prior 950

No Injury6,797no injury crashes77.2%

↓-3.4%prior 7,039

Source: Connecticut Crash Data · Csv Open Data · 2025-11-01 to 2025-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2025-11-01 to 2025-11-30 · Most severe injury per crash record

Road & Environmental Conditions

The proportion of crashes occurring in daylight decreased from 57.0% in November 2024 to 53.0% in November 2025, while crashes in dark-but-lighted conditions increased their share from 30.7% to 34.2%. Collisions on wet road surfaces also saw a proportional increase, accounting for 14.8% of crashes compared to 13.9% in the prior year. The distribution of crashes by weather condition remained largely stable, with clear conditions present in over 83% of incidents in both periods.

Weather

Clear7,385 (84.3%)

↓-6.6%prior 7,909

Rain883 (10.1%)

↑1.1%prior 873

Cloudy431 (4.9%)

↑105.2%prior 210

Freezing Rain or Freezing Drizzle33 (0.4%)

↓-42.1%prior 57

Fog, Smog, Smoke11 (0.1%)

↓-59.3%prior 27

Other6 (0.1%)

Blowing Snow5 (0.1%)

↓-28.6%prior 7

Severe Crosswinds3 (0.0%)

↓-57.1%prior 7

Blowing Sand, Soil, Dirt2 (0.0%)

Snow2 (0.0%)

↓-80.0%prior 10

Source: Connecticut Crash Data · Csv Open Data · 2025-11-01 to 2025-11-30 · Weather condition at time of crash

Lighting

Daylight4,672 (53.4%)

↓-10.4%prior 5,213

Dark-Lighted3,011 (34.4%)

↑7.0%prior 2,814

Dark-Not Lighted798 (9.1%)

↑1.1%prior 789

Dark-Unknown Lighting114 (1.3%)

↑81.0%prior 63

Dusk110 (1.3%)

↓-21.4%prior 140

Dawn33 (0.4%)

↓-15.4%prior 39

Other9 (0.1%)

↓-70.0%prior 30

Source: Connecticut Crash Data · Csv Open Data · 2025-11-01 to 2025-11-30 · Lighting condition field

Road Surface

Dry7,425 (84.7%)

↓-4.2%prior 7,752

Wet1,304 (14.9%)

↑2.2%prior 1,276

Ice / Frost23 (0.3%)

↓-59.6%prior 57

Snow4 (0.0%)

↓-20.0%prior 5

Mud, Dirt, Gravel4 (0.0%)

Moving Water3 (0.0%)

Other2 (0.0%)

↓-71.4%prior 7

Oil1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2025-11-01 to 2025-11-30 · Road surface condition field

Vehicles & Demographics

The ranking of the most frequently involved vehicle makes shifted, with Toyota (1,883 vehicles) surpassing Honda (1,870) for the top spot in November 2025; Honda had led with 2,016 vehicles in the prior year. The number of crash-involved vehicles from other top makes, such as Ford (1,354 vs. 1,467), also decreased. The age distribution of persons involved in crashes remained consistent, with all major age groups showing a decrease in line with the overall drop in collisions.

Top Vehicle Makes (16,674 vehicles)

TOYOTA1,883 (11.3%)

↑2.6%prior 1,836

HONDA1,870 (11.2%)

↓-7.2%prior 2,016

FORD1,354 (8.1%)

↓-7.7%prior 1,467

NISSAN1,146 (6.9%)

↓-6.4%prior 1,224

CHEVROLET1,001 (6%)

↓-4.0%prior 1,043

SUBARU856 (5.1%)

↓-2.2%prior 875

JEEP775 (4.6%)

↑3.2%prior 751

HYUNDAI687 (4.1%)

↓-1.3%prior 696

KIA462 (2.8%)

↑5.5%prior 438

BMW383 (2.3%)

↓-3.5%prior 397

Source: Connecticut Crash Data · Csv Open Data · 2025-11-01 to 2025-11-30 · Vehicle unit records

1,309 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (19,249 persons with recorded sex)

Male11,014 (57.2%)

↓-3.7%prior 11,433

Female8,235 (42.8%)

↓-7.3%prior 8,885

Source: Connecticut Crash Data · Csv Open Data · 2025-11-01 to 2025-11-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 25 mph zones, the most frequent location for collisions, decreased from 2,504 to 2,400 year-over-year, but the number of fatal crashes in these zones doubled from 4 to 8. Collisions in higher speed zones of 55 mph and 65 mph saw slight increases in volume. The location of the most fatal crashes shifted from 65 mph zones in the prior year (5 fatal crashes) to 25 mph zones in the current period (8 fatal crashes).

Fatal crashes by zone: 1 mph: 2 of 1,081 (0.185%) · 25 mph: 8 of 2,400 (0.333%) · 30 mph: 3 of 737 (0.407%) · 35 mph: 4 of 916 (0.437%) · 40 mph: 4 of 519 (0.771%) · 45 mph: 2 of 342 (0.585%) · 65 mph: 2 of 634 (0.315%)

Source: Connecticut Crash Data · Csv Open Data · 2025-11-01 to 2025-11-30 · 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: 2025-11-01 through 2025-11-30
Report generated: August 20, 2026

Data Coverage

Reporting period: 2025-11-01 through 2025-11-30 (30 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 8,806
Total persons involved: 20,831
Total vehicles involved: 16,674

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: November 2025." Published August 20, 2026. Reporting period: 2025-11-01 to 2025-11-30. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/november-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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