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Yearly Traffic Safety Analysis

6,479 CRASHES IN
CONNECTICUT, CT
2025

All metrics benchmarked against2024

In 2025, New London County recorded 6,479 total crashes, a 3.5% increase from the 6,258 crashes reported in 2024. Despite the rise in total collisions, the most significant year-over-year change was a 52.6% decrease in traffic fatalities, which fell from 38 in 2024 to 18 in 2025. Total injuries also saw a slight decline of 3.0% during the same period.

6,479

▲ 3.5%was 6,258

Total Crash Events

▼ -52.6%was 38

Persons Killed

1,786

▼ -3.0%was 1,842

Persons Injured

840

▼ -1.3%was 851

Hit-and-Run Crashes

Note: "Persons Killed" (18) counts individual fatalities across all crash events. "Fatal" in the severity table below (17) 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 crash trends in New London County showed a mixed picture year-over-year. While the total number of crashes increased by 3.5% from 6,258 in 2024 to 6,479 in 2025, both total injuries and fatalities declined. Total injuries saw a modest decrease of 3.0%, while fatalities dropped significantly by 52.6%.

840

Hit-and-Run Crashes — 2025

▼ -1.3% vs prior (851)

Hit-and-run incidents showed a slight downward trend in New London County. The total number of hit-and-run crashes decreased from 851 in 2024 to 840 in 2025. Correspondingly, the hit-and-run rate, representing the percentage of all crashes that were hit-and-runs, also declined slightly from 13.6% to 13.0%.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 3↓-66.7%

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 35↓-51.4%

Pedestrians Injured

Prior: 58↓-10.3%

Cyclists Injured

Prior: 29↓-41.4%

1,717

Motorists Injured

Prior: 1,755↓-2.2%

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 was the peak day for crashes in both 2025 (1,028 crashes) and 2024 (967 crashes). The peak hour for collisions shifted slightly earlier, moving from 3 PM in 2024 (598 crashes) to 2 PM in 2025 (586 crashes), with the afternoon period from 2 PM to 5 PM remaining the time with the highest crash frequency in both periods.

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

Crash severity decreased notably from 2024 to 2025. The number of fatal crashes fell from 36 to 17, and their proportion of all crashes dropped from 0.6% to 0.3%. Similarly, serious injury crashes decreased from 85 to 80. The proportion of crashes resulting in no injuries increased from 77.3% in 2024 to 78.9% in 2025, reflecting the overall drop in injury and fatal outcomes.

Severity is per crash event (most severe injury). 17 fatal crash events resulted in 18 persons killed.

Outcome by Severity (Crash Events)

Fatal17fatal crashes0.3%

↓-52.8%prior 36

Serious Injury80serious injury crashes1.2%

↓-5.9%prior 85

Minor Injury781minor injury crashes12.1%

↓-3.5%prior 809

Possible Injury491possible injury crashes7.6%

↑0.4%prior 489

No Injury5,110no injury crashes78.9%

↑5.6%prior 4,839

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

Crash conditions remained remarkably stable between 2024 and 2025. In both years, the vast majority of incidents occurred in clear weather (81.6% in 2025 vs. 81.0% in 2024) and during daylight hours (71.0% in 2025 vs. 70.3% in 2024). Similarly, crashes on dry road surfaces accounted for over 81% of the total in both periods, indicating no significant shift in the role of adverse environmental conditions.

Weather

Clear5,286 (81.9%)

↑4.2%prior 5,071

Rain553 (8.6%)

↓-8.6%prior 605

Cloudy260 (4.0%)

↓-5.5%prior 275

Snow242 (3.8%)

↑40.7%prior 172

Fog, Smog, Smoke37 (0.6%)

↑60.9%prior 23

Blowing Snow33 (0.5%)

↑57.1%prior 21

Freezing Rain or Freezing Drizzle32 (0.5%)

↓-25.6%prior 43

Severe Crosswinds3 (0.0%)

Sleet or Hail2 (0.0%)

Other2 (0.0%)

↓-75.0%prior 8

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

Lighting

Daylight4,600 (71.5%)

↑4.5%prior 4,402

Dark-Lighted1,115 (17.3%)

↑1.4%prior 1,100

Dark-Not Lighted552 (8.6%)

↑3.0%prior 536

Dusk83 (1.3%)

↓-11.7%prior 94

Dawn54 (0.8%)

↑3.8%prior 52

Dark-Unknown Lighting23 (0.4%)

↓-17.9%prior 28

Other6 (0.1%)

↓-14.3%prior 7

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

Road Surface

Dry5,256 (81.4%)

↑3.2%prior 5,091

Wet810 (12.6%)

↓-3.3%prior 838

Snow225 (3.5%)

↑78.6%prior 126

Ice / Frost118 (1.8%)

↑25.5%prior 94

Slush34 (0.5%)

↓-29.2%prior 48

Mud, Dirt, Gravel4 (0.1%)

↓-63.6%prior 11

Other3 (0.0%)

Standing Water2 (0.0%)

↓-86.7%prior 15

Moving Water2 (0.0%)

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

Vehicles & Demographics

The demographics of vehicles and persons involved in crashes saw little change between the two periods. The top five vehicle makes involved in collisions were identical in both 2024 and 2025: Ford, Toyota, Honda, Chevrolet, and Nissan. The age distribution of persons involved also remained consistent, with the 26-34 age group being the most represented in both years, accounting for 2,340 individuals in 2025 and 2,341 in 2024.

Top Vehicle Makes (11,714 vehicles)

FORD1,306 (11.1%)

↑9.7%prior 1,190

TOYOTA1,054 (9%)

↑1.4%prior 1,039

HONDA890 (7.6%)

↑2.3%prior 870

CHEVROLET628 (5.4%)

↑3.0%prior 610

NISSAN599 (5.1%)

↑10.9%prior 540

SUBARU556 (4.7%)

↑5.3%prior 528

JEEP548 (4.7%)

↑21.0%prior 453

HYUNDAI548 (4.7%)

↑5.8%prior 518

TOYT350 (3%)

↑3.6%prior 338

KIA345 (2.9%)

↑25.5%prior 275

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

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

Sex Distribution (13,680 persons with recorded sex)

Male7,852 (57.4%)

↑1.3%prior 7,750

Female5,828 (42.6%)

↓-2.8%prior 5,994

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 showed a significant shift in where fatal incidents occurred. While the total number of crashes in lower speed zones (35 mph or less) remained high, the number of fatal crashes in the 25 mph zone dropped dramatically from 14 in 2024 to just 3 in 2025. In 2025, the highest number of fatal crashes occurred in the 45 mph zone (5 fatalities) and the 65 mph zone (4 fatalities), a notable change from the prior year's concentration of fatalities in lower-speed zones.

Fatal crashes by zone: 1 mph: 1 of 304 (0.329%) · 25 mph: 3 of 2,285 (0.131%) · 30 mph: 1 of 489 (0.204%) · 35 mph: 1 of 966 (0.104%) · 40 mph: 1 of 285 (0.351%) · 45 mph: 5 of 585 (0.855%) · 50 mph: 1 of 176 (0.568%) · 65 mph: 4 of 911 (0.439%)

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 22, 2026

Data Coverage

Reporting period: 2025-01-01 through 2025-12-31 (365 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 6,479
Total persons involved: 14,680
Total vehicles involved: 11,714

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 22, 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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