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Monthly Traffic Safety Analysis

8,686 CRASHES IN
CONNECTICUT, CT
JULY 2025

All metrics benchmarked againstJuly 2024

In July 2025, there were 8,686 total crashes, a 3.5% increase from the 8,396 crashes recorded in July 2024. While total fatalities remained unchanged at 25, the number of fatal crashes rose from 18 to 24 year-over-year, and crashes resulting in serious injuries increased from 129 to 213.

8,686

▲ 3.5%was 8,396

Total Crash Events

Persons Killed

2,923

▲ 0.8%was 2,901

Persons Injured

1,118

Hit-and-Run Crashes

Note: "Persons Killed" (25) counts individual fatalities across all crash events. "Fatal" in the severity table below (24) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2025-07-01 to 2025-07-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in July 2025 showed an upward trend compared to the same month in the prior year. The total number of crashes increased by 3.5%, from 8,396 to 8,686. While total fatalities held steady at 25, the number of reported injuries saw a marginal increase of 0.8% from 2,901 to 2,923.

1,118

Hit-and-Run Crashes — July 2025

▼ 0.0% vs prior (1,118)

The number of hit-and-run crashes was unchanged year-over-year, with exactly 1,118 incidents recorded in both July 2025 and July 2024. However, because the total number of crashes increased in the current period, the hit-and-run rate saw a slight decrease. The rate fell from 13.3% of all crashes in the prior year to 12.9% in the current year.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 0→%

Cyclists Killed

Prior: 0→%

Motorists Killed

Prior: 25↓-28.0%

Pedestrians Injured

Prior: 93↓-11.8%

Cyclists Injured

Prior: 37↑32.4%

2,792

Motorists Injured

Prior: 2,771↑0.8%

Source: Connecticut Crash Data · Csv Open Data · 2025-07-01 to 2025-07-31 · 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. The peak day for crashes in both July 2025 and July 2024 was Wednesday, with 1,497 and 1,421 crashes respectively. Similarly, the 4 p.m. hour was the peak time for collisions in both periods, accounting for 742 crashes in the current period and 702 in the prior.

Source: Connecticut Crash Data · Csv Open Data · 2025-07-01 to 2025-07-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2025-07-01 to 2025-07-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes increased in July 2025 compared to the previous year. The number of fatal crashes rose from 18 to 24, and the count of serious injury crashes increased significantly from 129 to 213. Consequently, the proportion of serious injury crashes grew from 1.5% to 2.5% of all collisions, while the percentage of crashes resulting in no injuries remained stable at 75.3% for both periods.

Severity is per crash event (most severe injury). 24 fatal crash events resulted in 25 persons killed.

Outcome by Severity (Crash Events)

Fatal24fatal crashes0.3%

↑33.3%prior 18

Serious Injury213serious injury crashes2.5%

↑65.1%prior 129

Minor Injury1,072minor injury crashes12.3%

↑3.1%prior 1,040

Possible Injury837possible injury crashes9.6%

↓-6.0%prior 890

No Injury6,540no injury crashes75.3%

↑3.5%prior 6,319

Source: Connecticut Crash Data · Csv Open Data · 2025-07-01 to 2025-07-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2025-07-01 to 2025-07-31 · Most severe injury per crash record

Road & Environmental Conditions

Crash conditions in July 2025 were very similar to those in July 2024, with the vast majority of incidents occurring in clear weather and daylight on dry roads. Crashes on dry road surfaces constituted 91.4% of the total in the current period, up slightly from 89.4% in the prior period. Correspondingly, crashes on wet roads decreased from 817 to 693. The proportion of crashes in daylight remained steady at approximately 80% for both years.

Weather

Clear7,826 (90.5%)

↑5.5%prior 7,416

Rain524 (6.1%)

↓-9.2%prior 577

Cloudy286 (3.3%)

↓-16.4%prior 342

Fog, Smog, Smoke9 (0.1%)

↓-43.8%prior 16

Other4 (0.0%)

Severe Crosswinds2 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2025-07-01 to 2025-07-31 · Weather condition at time of crash

Lighting

Daylight6,949 (80.5%)

↑4.2%prior 6,672

Dark-Lighted1,151 (13.3%)

↑3.3%prior 1,114

Dark-Not Lighted342 (4.0%)

↓-4.7%prior 359

Dusk87 (1.0%)

↑2.4%prior 85

Dark-Unknown Lighting49 (0.6%)

↓-5.8%prior 52

Dawn40 (0.5%)

↓-11.1%prior 45

Other12 (0.1%)

↑50.0%prior 8

Source: Connecticut Crash Data · Csv Open Data · 2025-07-01 to 2025-07-31 · Lighting condition field

Road Surface

Dry7,936 (91.7%)

↑5.8%prior 7,504

Wet693 (8.0%)

↓-15.2%prior 817

Mud, Dirt, Gravel10 (0.1%)

↑25.0%prior 8

Standing Water8 (0.1%)

Other4 (0.0%)

Moving Water3 (0.0%)

↓-40.0%prior 5

Sand2 (0.0%)

Oil1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2025-07-01 to 2025-07-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Honda, Toyota, and Ford being the top three in both July 2025 and July 2024. An analysis of persons involved shows a shift in age demographics; the 35-44 age group saw its involvement increase from 3,158 to 3,425 individuals, making it the most represented group in the current period. The number of persons aged 65 and older involved in crashes also rose from 2,171 to 2,391.

Top Vehicle Makes (16,560 vehicles)

HONDA1,855 (11.2%)

↑4.5%prior 1,775

TOYOTA1,783 (10.8%)

↑3.7%prior 1,720

FORD1,456 (8.8%)

↑4.4%prior 1,394

NISSAN1,090 (6.6%)

↑3.3%prior 1,055

CHEVROLET957 (5.8%)

↓-3.4%prior 991

SUBARU811 (4.9%)

↑7.3%prior 756

HYUNDAI696 (4.2%)

↑12.8%prior 617

JEEP692 (4.2%)

↑5.2%prior 658

KIA391 (2.4%)

↑0.8%prior 388

BMW370 (2.2%)

↑3.6%prior 357

Source: Connecticut Crash Data · Csv Open Data · 2025-07-01 to 2025-07-31 · Vehicle unit records

1,236 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (19,416 persons with recorded sex)

Male11,182 (57.6%)

↑3.2%prior 10,838

Female8,234 (42.4%)

↑2.0%prior 8,072

Source: Connecticut Crash Data · Csv Open Data · 2025-07-01 to 2025-07-31 · Person-level records linked to crash events

Speed Limit Zones

There was a noticeable shift in crashes toward higher speed zones in July 2025 compared to the prior year. Collisions in 55-65 mph zones increased from 1,457 to 1,621. The number of fatal crashes also increased in higher speed limit areas, with zones posted at 45 mph seeing fatalities rise from 2 to 5, and 65 mph zones seeing an increase from 1 to 4 fatal crashes. Conversely, crashes in 25 mph zones saw a slight decrease from 2,364 to 2,326.

Fatal crashes by zone: 1 mph: 1 of 1,166 (0.086%) · 25 mph: 4 of 2,326 (0.172%) · 30 mph: 4 of 652 (0.613%) · 35 mph: 3 of 947 (0.317%) · 40 mph: 2 of 459 (0.436%) · 45 mph: 5 of 278 (1.799%) · 50 mph: 1 of 246 (0.407%) · 65 mph: 4 of 692 (0.578%)

Source: Connecticut Crash Data · Csv Open Data · 2025-07-01 to 2025-07-31 · 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: 2025-07-01 through 2025-07-31
Report generated: August 20, 2026

Data Coverage

Reporting period: 2025-07-01 through 2025-07-31 (31 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 8,686
Total persons involved: 20,874
Total vehicles involved: 16,560

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: July 2025." Published August 20, 2026. Reporting period: 2025-07-01 to 2025-07-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/july-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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