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Monthly Traffic Safety Analysis

7,808 CRASHES IN
CONNECTICUT, CT
APRIL 2024

All metrics benchmarked againstApril 2023

In April 2024, Connecticut recorded 7,808 vehicle crashes, a marginal 0.3% decrease from the 7,833 crashes in April 2023. While the total number of incidents remained stable, there was a significant year-over-year shift in crash outcomes. The most notable change was a 32% reduction in traffic fatalities, which fell from 25 to 17.

7,808

▼ -0.3%was 7,833

Total Crash Events

▼ -32.0%was 25

Persons Killed

2,524

▼ -7.1%was 2,716

Persons Injured

957

▼ -12.2%was 1,090

Hit-and-Run Crashes

Note: "Persons Killed" (17) 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 · 2024-04-01 to 2024-04-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends for April 2024 show a slight decline compared to the same month in the prior year. Total crashes decreased by 0.3% from 7,833 to 7,808. More significantly, the number of persons injured fell by 7.1% from 2,716 to 2,524, and fatalities saw a substantial 32% drop from 25 to 17.

957

Hit-and-Run Crashes — April 2024

▼ -12.2% vs prior (1,090)

Hit-and-run incidents decreased in April 2024 compared to the previous year. The total number of hit-and-run crashes fell by 12.2%, from 1,090 to 957. The hit-and-run rate, representing the proportion of all crashes that were hit-and-runs, also trended downward, declining from 13.9% in April 2023 to 12.3% in April 2024.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 3↑66.7%

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 22↓-45.5%

Pedestrians Injured

Prior: 75↑24.0%

Cyclists Injured

Prior: 22↑4.5%

2,408

Motorists Injured

Prior: 2,619↓-8.1%

Source: Connecticut Crash Data · Csv Open Data · 2024-04-01 to 2024-04-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted between the two periods. The peak day for crashes moved from Saturday (1,306 crashes) in April 2023 to Tuesday (1,363 crashes) in April 2024, indicating a shift from weekend to weekday collisions. The peak hour for collisions, however, remained consistent at 3 PM in both years, with 678 crashes in the current period compared to 664 in the prior.

Source: Connecticut Crash Data · Csv Open Data · 2024-04-01 to 2024-04-30 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2024-04-01 to 2024-04-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased in April 2024 compared to the previous year. The fatal crash rate dropped from 0.32% to 0.22%, with 17 fatal crashes recorded versus 25 in the prior period. The proportion of crashes resulting in no injuries increased from 74.6% to 75.9%, while crashes involving possible injuries decreased from 12.4% to 11.2% of the total.

Outcome by Severity (Crash Events)

Fatal17fatal crashes0.2%

↓-32.0%prior 25

Serious Injury90serious injury crashes1.2%

↓-4.3%prior 94

Minor Injury902minor injury crashes11.6%

↑0.1%prior 901

Possible Injury872possible injury crashes11.2%

↓-9.9%prior 968

No Injury5,927no injury crashes75.9%

↑1.4%prior 5,845

Source: Connecticut Crash Data · Csv Open Data · 2024-04-01 to 2024-04-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2024-04-01 to 2024-04-30 · Most severe injury per crash record

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with most incidents in both periods occurring in clear weather on dry roads during daylight hours. In April 2024, crashes on wet roads accounted for 17.9% of the total, a slight increase from 16.7% in April 2023. Correspondingly, crashes in rainy weather rose slightly to 14.2% from 12.9% in the prior year.

Weather

Clear6,050 (78.0%)

↓-3.3%prior 6,259

Rain1,110 (14.3%)

↑9.7%prior 1,012

Cloudy403 (5.2%)

↓-5.6%prior 427

Freezing Rain or Freezing Drizzle85 (1.1%)

↑240.0%prior 25

Snow56 (0.7%)

Sleet or Hail29 (0.4%)

Blowing Snow9 (0.1%)

Fog, Smog, Smoke7 (0.1%)

↓-83.7%prior 43

Severe Crosswinds3 (0.0%)

Other2 (0.0%)

↓-75.0%prior 8

Source: Connecticut Crash Data · Csv Open Data · 2024-04-01 to 2024-04-30 · Weather condition at time of crash

Lighting

Daylight6,036 (77.9%)

↑2.4%prior 5,893

Dark-Lighted1,147 (14.8%)

↓-13.3%prior 1,323

Dark-Not Lighted364 (4.7%)

↓-2.7%prior 374

Dusk80 (1.0%)

↓-7.0%prior 86

Dawn72 (0.9%)

↑67.4%prior 43

Dark-Unknown Lighting35 (0.5%)

↓-23.9%prior 46

Other11 (0.1%)

↑37.5%prior 8

Source: Connecticut Crash Data · Csv Open Data · 2024-04-01 to 2024-04-30 · Lighting condition field

Road Surface

Dry6,211 (80.1%)

↓-3.8%prior 6,454

Wet1,395 (18.0%)

↑6.7%prior 1,308

Slush60 (0.8%)

Ice / Frost50 (0.6%)

Snow24 (0.3%)

Mud, Dirt, Gravel8 (0.1%)

↑33.3%prior 6

Standing Water5 (0.1%)

Moving Water4 (0.1%)

Oil1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2024-04-01 to 2024-04-30 · Road surface condition field

Vehicles & Demographics

The top five vehicle makes involved in crashes—Honda, Toyota, Ford, Nissan, and Chevrolet—were identical in both April 2024 and April 2023, showing stability in vehicle make distribution. An analysis of persons involved shows a demographic shift, with decreased involvement from the 26-34 and 45-54 age groups. Conversely, the number of individuals aged 65 and older involved in crashes increased from 1,916 to 2,020.

Top Vehicle Makes (14,757 vehicles)

HONDA1,619 (11%)

↓-4.7%prior 1,699

TOYOTA1,522 (10.3%)

↓-1.2%prior 1,541

FORD1,350 (9.1%)

↑4.9%prior 1,287

NISSAN1,039 (7%)

↓-6.7%prior 1,114

CHEVROLET903 (6.1%)

↑1.0%prior 894

SUBARU734 (5%)

↑4.3%prior 704

JEEP637 (4.3%)

↓-1.5%prior 647

HYUNDAI611 (4.1%)

↓-2.9%prior 629

KIA381 (2.6%)

↑7.6%prior 354

BMW339 (2.3%)

↓-2.6%prior 348

Source: Connecticut Crash Data · Csv Open Data · 2024-04-01 to 2024-04-30 · Vehicle unit records

1,103 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (17,197 persons with recorded sex)

Male9,719 (56.5%)

↓-1.8%prior 9,902

Female7,478 (43.5%)

↓-3.6%prior 7,754

Source: Connecticut Crash Data · Csv Open Data · 2024-04-01 to 2024-04-30 · Person-level records linked to crash events

Speed Limit Zones

Crash distribution across speed zones showed some changes year-over-year. There was a notable decrease in crashes in 25 mph zones, from 2,250 to 2,091 incidents. Fatalities in 40 mph zones dropped from 5 to 0, while fatalities in 50 mph zones increased from 2 to 4. Crashes in 65 mph zones increased from 495 to 559.

Fatal crashes by zone: 1 mph: 1 of 1,063 (0.094%) · 25 mph: 2 of 2,091 (0.096%) · 30 mph: 2 of 628 (0.318%) · 35 mph: 1 of 816 (0.123%) · 45 mph: 3 of 303 (0.99%) · 50 mph: 4 of 221 (1.81%) · 65 mph: 3 of 559 (0.537%) · 88 mph: 1 of 356 (0.281%)

Source: Connecticut Crash Data · Csv Open Data · 2024-04-01 to 2024-04-30 · 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: 2024-04-01 through 2024-04-30
Report generated: August 20, 2026

Data Coverage

Reporting period: 2024-04-01 through 2024-04-30 (30 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 7,808
Total persons involved: 18,442
Total vehicles involved: 14,757

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: April 2024." Published August 20, 2026. Reporting period: 2024-04-01 to 2024-04-30. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/april-2024-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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