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Monthly Traffic Safety Analysis

2,181 CRASHES IN
CONNECTICUT, CT
MAY 2026

All metrics benchmarked againstMay 2025

In May 2026, Connecticut recorded 2,181 total vehicle crashes, a significant 75.2% decrease from the 8,778 crashes reported in May 2025. Despite the sharp decline in overall collisions, the number of fatalities increased slightly from 17 to 19 year-over-year. The most notable shift was in crash severity, with the proportion of crashes involving an injury rising from 23% to 96.1%, suggesting a change in which types of crashes were reported.

2,181

▼ -75.2%was 8,778

Total Crash Events

▲ 11.8%was 17

Persons Killed

2,835

▲ 3.6%was 2,737

Persons Injured

217

▼ -81.0%was 1,141

Hit-and-Run Crashes

Note: "Persons Killed" (19) 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 · 2026-05-01 to 2026-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend shows a dramatic year-over-year decrease in crash volume, with total crashes falling by 75.2% from May 2025 to May 2026. However, this decline in total incidents did not correspond with a decrease in harm. Total injuries rose by 3.6% (from 2,737 to 2,835), and fatalities increased from 17 to 19 over the same period.

217

Hit-and-Run Crashes — May 2026

▼ -81.0% vs prior (1,141)

The incidence of hit-and-run crashes decreased significantly year-over-year. In May 2026, there were 217 hit-and-run incidents, down from 1,141 in May 2025. The hit-and-run rate, representing the proportion of all crashes that were hit-and-runs, also declined from 13.0% to 9.9% over the same period.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 2↓-50.0%

Cyclists Killed

Prior: 1↓-100.0%

Motorists Killed

Prior: 14↑28.6%

Pedestrians Injured

Prior: 73↑32.9%

Cyclists Injured

Prior: 28↑50.0%

2,696

Motorists Injured

Prior: 2,636↑2.3%

Source: Connecticut Crash Data · Csv Open Data · 2026-05-01 to 2026-05-31 · 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 consistent between May 2025 and May 2026. Friday was the peak day for crashes in both periods, with 376 crashes in May 2026 compared to 1,596 in the prior year. Similarly, the 3 PM hour remained the peak time for collisions, accounting for 191 crashes in the current period versus 791 in the previous year.

Source: Connecticut Crash Data · Csv Open Data · 2026-05-01 to 2026-05-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2026-05-01 to 2026-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the absolute number of fatal crashes remained unchanged at 17 year-over-year, the fatal crash rate quadrupled from 0.2% to 0.8% due to the steep drop in total reported crashes. A significant shift occurred in the overall severity distribution, as the proportion of crashes involving any level of injury rose from 23% in May 2025 to 96.1% in May 2026. Consequently, crashes with no reported injuries fell from representing 77% of all incidents to just 3.9%.

Severity is per crash event (most severe injury). 17 fatal crash events resulted in 19 persons killed.

Outcome by Severity (Crash Events)

Fatal17fatal crashes0.8%

→0.0%prior 17

Serious Injury97serious injury crashes4.4%

↓-7.6%prior 105

Minor Injury1,076minor injury crashes49.3%

↑6.9%prior 1,007

Possible Injury907possible injury crashes41.6%

↑2.3%prior 887

No Injury84no injury crashes3.9%

↓-98.8%prior 6,762

Source: Connecticut Crash Data · Csv Open Data · 2026-05-01 to 2026-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2026-05-01 to 2026-05-31 · Most severe injury per crash record

Road & Environmental Conditions

Crashes in May 2026 predominantly occurred in favorable conditions, consistent with the prior year. The proportion of crashes on dry roads increased from 74.2% in May 2025 to 84.4% in May 2026, while crashes in clear weather rose from 72.5% to 84.3% of the total. The percentage of crashes occurring during daylight remained relatively stable, shifting from 79.5% to 75.7%.

Weather

Clear1,838 (84.5%)

↓-71.1%prior 6,366

Rain252 (11.6%)

↓-85.3%prior 1,719

Cloudy81 (3.7%)

↓-86.6%prior 603

Other2 (0.1%)

Fog, Smog, Smoke2 (0.1%)

↓-93.8%prior 32

Source: Connecticut Crash Data · Csv Open Data · 2026-05-01 to 2026-05-31 · Weather condition at time of crash

Lighting

Daylight1,650 (76.0%)

↓-76.4%prior 6,977

Dark-Lighted361 (16.6%)

↓-70.3%prior 1,214

Dark-Not Lighted113 (5.2%)

↓-66.5%prior 337

Dusk18 (0.8%)

↓-81.1%prior 95

Dawn15 (0.7%)

↓-69.4%prior 49

Dark-Unknown Lighting12 (0.6%)

↓-75.0%prior 48

Other3 (0.1%)

↓-80.0%prior 15

Source: Connecticut Crash Data · Csv Open Data · 2026-05-01 to 2026-05-31 · Lighting condition field

Road Surface

Dry1,841 (84.7%)

↓-71.7%prior 6,512

Wet324 (14.9%)

↓-85.3%prior 2,199

Moving Water3 (0.1%)

Mud, Dirt, Gravel2 (0.1%)

↓-71.4%prior 7

Other2 (0.1%)

Sand1 (0.0%)

Standing Water1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2026-05-01 to 2026-05-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Honda (491 vehicles), Toyota (417), and Ford (316) being the most common in May 2026, similar to the prior year. The age demographics of individuals involved in crashes also showed stability. The 26-34 age group was the most represented cohort in both periods, accounting for 17.3% of all persons in May 2026 compared to 16.8% in May 2025.

Top Vehicle Makes (4,174 vehicles)

HONDA491 (11.8%)

↓-72.7%prior 1,797

TOYOTA417 (10%)

↓-77.2%prior 1,826

FORD316 (7.6%)

↓-77.9%prior 1,432

NISSAN302 (7.2%)

↓-72.8%prior 1,112

SUBARU208 (5%)

↓-73.7%prior 791

CHEVROLET198 (4.7%)

↓-79.8%prior 982

JEEP190 (4.6%)

↓-74.2%prior 736

HYUNDAI172 (4.1%)

↓-76.1%prior 720

BMW99 (2.4%)

↓-74.9%prior 395

ACURA97 (2.3%)

↓-70.4%prior 328

Source: Connecticut Crash Data · Csv Open Data · 2026-05-01 to 2026-05-31 · Vehicle unit records

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

Sex Distribution (5,576 persons with recorded sex)

Male3,129 (56.1%)

↓-71.7%prior 11,075

Female2,447 (43.9%)

↓-71.0%prior 8,439

Source: Connecticut Crash Data · Csv Open Data · 2026-05-01 to 2026-05-31 · Person-level records linked to crash events

Speed Limit Zones

In both May 2026 and May 2025, the 25 mph speed zone recorded the highest number of total crashes. However, a notable shift occurred in the location of fatal crashes, with the 65 mph zone showing the highest fatal crash rate (3.85%) in May 2026, accounting for 6 fatalities in 156 crashes. This is a substantial increase from May 2025, when the highest fatal rate was 1.23% in the 50 mph zone.

Fatal crashes by zone: 25 mph: 1 of 608 (0.164%) · 30 mph: 2 of 175 (1.143%) · 35 mph: 3 of 274 (1.095%) · 45 mph: 2 of 108 (1.852%) · 50 mph: 1 of 62 (1.613%) · 55 mph: 1 of 192 (0.521%) · 65 mph: 6 of 156 (3.846%) · 88 mph: 1 of 90 (1.111%)

Source: Connecticut Crash Data · Csv Open Data · 2026-05-01 to 2026-05-31 · 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: 2026-05-01 through 2026-05-31
Report generated: August 20, 2026

Data Coverage

Reporting period: 2026-05-01 through 2026-05-31 (31 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 2,181
Total persons involved: 5,875
Total vehicles involved: 4,174

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: May 2026." Published August 20, 2026. Reporting period: 2026-05-01 to 2026-05-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/may-2026-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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