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Monthly Traffic Safety Analysis

10,004 CRASHES IN
CONNECTICUT, CT
DECEMBER 2025

All metrics benchmarked againstDecember 2024

In December 2025, Connecticut recorded 10,004 vehicle crashes, a 2.3% increase from the 9,783 crashes in December 2024. While the overall crash volume saw a modest rise, the most significant year-over-year change was a 92.3% increase in traffic fatalities, which climbed from 13 to 25.

10,004

▲ 2.3%was 9,783

Total Crash Events

▲ 92.3%was 13

Persons Killed

2,735

▼ -7.2%was 2,946

Persons Injured

1,239

▲ 7.3%was 1,155

Hit-and-Run Crashes

Note: "Persons Killed" (25) counts individual fatalities across all crash events. "Fatal" in the severity table below (22) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

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

Trend Summary

Overall crash totals in Connecticut rose by 2.3% in December 2025 compared to the same month in the prior year, increasing from 9,783 to 10,004 incidents. This increase was accompanied by a concerning trend in severity, as total fatalities grew from 13 to 25. Conversely, the total number of people injured in crashes decreased by 7.2%, from 2,946 to 2,735.

1,239

Hit-and-Run Crashes — December 2025

▲ 7.3% vs prior (1,155)

Hit-and-run incidents increased from 1,155 in December 2024 to 1,239 in December 2025. This change represents an upward trend in both absolute numbers and as a proportion of all collisions. The hit-and-run rate rose from 11.8% to 12.4% year-over-year.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 4↑50.0%

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 9↑111.1%

141

Pedestrians Injured

Prior: 135↑4.4%

Cyclists Injured

Prior: 16↓-18.8%

2,581

Motorists Injured

Prior: 2,795↓-7.7%

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

When Crashes Happen

The evening commute hour remained the most frequent time for crashes, with the 5 PM hour being the peak in both December 2025 (957 crashes) and December 2024 (1,004 crashes). However, the peak day for collisions shifted from Monday in the prior year (1,849 crashes) to Tuesday in the current period (1,674 crashes). Crash distribution during the week also changed, with a notable year-over-year increase on Wednesdays from 1,211 to 1,646 incidents.

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

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

Crash Severity Breakdown

The number of fatal crashes increased from 15 to 22 year-over-year, with total fatalities rising sharply from 13 to 25. Despite this increase in the most severe outcomes, the overall proportion of crashes resulting in any level of injury decreased from 22.2% to 20.7%. This was primarily due to a significant drop in serious injury crashes, which fell from 157 in December 2024 to 81 in December 2025.

Severity is per crash event (most severe injury). 22 fatal crash events resulted in 25 persons killed.

Outcome by Severity (Crash Events)

Fatal22fatal crashes0.2%

↑46.7%prior 15

Serious Injury81serious injury crashes0.8%

↓-48.4%prior 157

Minor Injury984minor injury crashes9.8%

↓-1.0%prior 994

Possible Injury1,005possible injury crashes10%

↓-1.2%prior 1,017

No Injury7,912no injury crashes79.1%

↑4.1%prior 7,600

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

Severity Distribution (Crash Events)

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

Road & Environmental Conditions

Crash conditions shifted year-over-year, reflecting different weather patterns between the two periods. The proportion of crashes occurring in snow increased from 6.8% to 10.1%, while those in rain decreased from 13.8% to 8.1%. This was mirrored in road surface data, where crashes on snowy or icy roads rose from 8.9% to 16.7% of the total, and collisions on wet surfaces fell from 22.4% to 15.1%.

Weather

Clear7,465 (75.0%)

↑5.6%prior 7,072

Snow1,010 (10.1%)

↑52.8%prior 661

Rain809 (8.1%)

↓-40.1%prior 1,350

Cloudy291 (2.9%)

↓-14.7%prior 341

Blowing Snow157 (1.6%)

↑61.9%prior 97

Freezing Rain or Freezing Drizzle150 (1.5%)

↑17.2%prior 128

Sleet or Hail36 (0.4%)

↑44.0%prior 25

Other18 (0.2%)

↑125.0%prior 8

Fog, Smog, Smoke16 (0.2%)

↓-66.0%prior 47

Severe Crosswinds6 (0.1%)

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

Lighting

Daylight5,341 (53.8%)

↑4.0%prior 5,135

Dark-Lighted3,291 (33.2%)

↑1.2%prior 3,253

Dark-Not Lighted943 (9.5%)

↓-4.7%prior 989

Dusk138 (1.4%)

↑4.5%prior 132

Dark-Unknown Lighting120 (1.2%)

↑50.0%prior 80

Dawn72 (0.7%)

↓-30.1%prior 103

Other20 (0.2%)

↓-35.5%prior 31

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

Road Surface

Dry6,751 (67.8%)

↑1.7%prior 6,640

Wet1,510 (15.2%)

↓-31.1%prior 2,191

Snow897 (9.0%)

↑113.6%prior 420

Ice / Frost540 (5.4%)

↑84.3%prior 293

Slush232 (2.3%)

↑46.8%prior 158

Other16 (0.2%)

↑77.8%prior 9

Moving Water5 (0.1%)

↓-28.6%prior 7

Mud, Dirt, Gravel5 (0.1%)

↓-44.4%prior 9

Sand4 (0.0%)

↓-20.0%prior 5

Standing Water2 (0.0%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained stable year-over-year, with Toyota (2,109) and Honda (2,077) leading in December 2025, similar to the prior year. The demographic profile of persons involved in crashes also showed little change; the 26-34 age group was the largest cohort in both periods, accounting for approximately 17% of all individuals involved.

Top Vehicle Makes (18,468 vehicles)

TOYOTA2,109 (11.4%)

↑1.8%prior 2,071

HONDA2,077 (11.2%)

↓-1.3%prior 2,104

FORD1,529 (8.3%)

↓-2.2%prior 1,563

NISSAN1,282 (6.9%)

↑0.2%prior 1,280

CHEVROLET1,098 (5.9%)

↓-4.0%prior 1,144

SUBARU989 (5.4%)

↑0.3%prior 986

JEEP877 (4.7%)

↑9.1%prior 804

HYUNDAI783 (4.2%)

↓-1.4%prior 794

KIA440 (2.4%)

↓-4.6%prior 461

BMW439 (2.4%)

↑4.5%prior 420

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

1,268 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (20,875 persons with recorded sex)

Male11,963 (57.3%)

↓-1.1%prior 12,095

Female8,912 (42.7%)

↓-3.1%prior 9,198

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

Speed Limit Zones

While the overall distribution of crashes across speed zones was similar, fatal crashes shifted toward higher-speed roadways. Fatalities in 25 mph zones decreased from 9 to 3 year-over-year, but they increased in 40 mph zones (from 0 to 6) and 55 mph zones (from 0 to 3). In December 2025, crashes in zones of 40 mph or higher accounted for 12 of the 22 fatal incidents, compared to 6 of 15 fatal crashes in the prior year.

Fatal crashes by zone: 25 mph: 3 of 2,753 (0.109%) · 30 mph: 3 of 827 (0.363%) · 40 mph: 6 of 610 (0.984%) · 45 mph: 4 of 344 (1.163%) · 50 mph: 2 of 236 (0.847%) · 55 mph: 3 of 914 (0.328%) · 65 mph: 1 of 696 (0.144%)

Source: Connecticut Crash Data · Csv Open Data · 2025-12-01 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: 2025-12-01 through 2025-12-31
Report generated: August 20, 2026

Data Coverage

Reporting period: 2025-12-01 through 2025-12-31 (31 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 10,004
Total persons involved: 22,508
Total vehicles involved: 18,468

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: December 2025." Published August 20, 2026. Reporting period: 2025-12-01 to 2025-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/december-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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