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Yearly Traffic Safety Analysis

3,682 CRASHES IN
CONNECTICUT, CT
2024

All metrics benchmarked against2023

In Middlesex County, total traffic crashes remained relatively stable, increasing 1.3% from 3,634 in 2023 to 3,682 in 2024. Despite the slight rise in total incidents, the most notable year-over-year shift was a 22.2% decrease in traffic fatalities, which fell from 18 to 14.

3,682

▲ 1.3%was 3,634

Total Crash Events

▼ -22.2%was 18

Persons Killed

1,107

▼ -1.3%was 1,122

Persons Injured

301

▼ -3.5%was 312

Hit-and-Run Crashes

Note: "Persons Killed" (14) counts individual fatalities across all crash events. "Fatal" in the severity table below (14) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash volume in Middlesex County saw a minor increase of 1.3% between 2023 and 2024. However, key severity indicators showed improvement, as total fatalities decreased by 22.2% from 18 to 14, and the number of injuries declined by 1.3% from 1,122 to 1,107.

301

Hit-and-Run Crashes — 2024

▼ -3.5% vs prior (312)

Hit-and-run incidents showed a slight downward trend. The total count of hit-and-run crashes decreased from 312 in 2023 to 301 in 2024. Consequently, the hit-and-run rate as a percentage of all crashes also declined, moving from 8.6% in the prior year to 8.2% in the current year.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 1↑100.0%

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 17↓-29.4%

Pedestrians Injured

Prior: 23→0.0%

Cyclists Injured

Prior: 8↑25.0%

1,074

Motorists Injured

Prior: 1,091↓-1.6%

Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal distribution of crashes remained consistent year-over-year. Friday was the peak day for crashes in both 2024 (607 crashes) and 2023 (617 crashes). The peak hour shifted one hour earlier to 3 p.m. in 2024 (327 crashes) from 4 p.m. in 2023 (337 crashes), with afternoon commute times remaining the most frequent period for incidents.

Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes decreased from the prior year, with the fatal crash rate falling from 0.47 to 0.38 per 100 crashes. Total fatalities dropped from 18 to 14. While the proportion of crashes resulting in possible injuries decreased, the count of serious injury (Severity A) crashes rose from 46 to 53. The share of crashes with no injuries increased from 76.7% to 77.5%.

Outcome by Severity (Crash Events)

Fatal14fatal crashes0.4%

↓-17.6%prior 17

Serious Injury53serious injury crashes1.4%

↑15.2%prior 46

Minor Injury442minor injury crashes12%

↑1.1%prior 437

Possible Injury320possible injury crashes8.7%

↓-7.8%prior 347

No Injury2,853no injury crashes77.5%

↑2.4%prior 2,787

Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Road & Environmental Conditions

Crash conditions were largely similar year-over-year, with approximately 82% of incidents in both periods occurring in clear weather and on dry roads. However, there was a notable increase in crashes under adverse winter conditions in 2024. Crashes in snow more than doubled from 38 to 98, and collisions on snowy road surfaces increased from 25 to 67.

Weather

Clear3,025 (82.4%)

↑1.2%prior 2,990

Rain355 (9.7%)

↓-10.4%prior 396

Cloudy139 (3.8%)

↓-10.3%prior 155

Snow98 (2.7%)

↑157.9%prior 38

Freezing Rain or Freezing Drizzle20 (0.5%)

↑150.0%prior 8

Blowing Snow10 (0.3%)

Fog, Smog, Smoke10 (0.3%)

↓-60.0%prior 25

Sleet or Hail7 (0.2%)

Severe Crosswinds4 (0.1%)

Other2 (0.1%)

↓-75.0%prior 8

Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight2,745 (75.0%)

↑2.3%prior 2,683

Dark-Lighted574 (15.7%)

↓-6.1%prior 611

Dark-Not Lighted250 (6.8%)

↑3.3%prior 242

Dusk51 (1.4%)

↑24.4%prior 41

Dawn25 (0.7%)

↓-16.7%prior 30

Dark-Unknown Lighting11 (0.3%)

↓-26.7%prior 15

Other5 (0.1%)

→0.0%prior 5

Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry3,005 (81.9%)

↑0.5%prior 2,990

Wet500 (13.6%)

↓-10.6%prior 559

Snow67 (1.8%)

↑168.0%prior 25

Ice / Frost52 (1.4%)

↑44.4%prior 36

Slush35 (1.0%)

↑483.3%prior 6

Mud, Dirt, Gravel7 (0.2%)

Sand1 (0.0%)

Moving Water1 (0.0%)

Other1 (0.0%)

Standing Water1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Toyota, Honda, and Ford being the most frequent in both periods. Analysis of persons involved shows the 26-34 age group was the most represented cohort in both 2023 (1,397 people) and 2024 (1,377 people). Notably, the number of individuals aged 65 and older involved in crashes grew by 8.4%, from 1,196 to 1,296.

Top Vehicle Makes (6,690 vehicles)

TOYOTA773 (11.6%)

↑0.8%prior 767

HONDA728 (10.9%)

↑11.0%prior 656

FORD674 (10.1%)

↓-5.1%prior 710

CHEVROLET512 (7.7%)

↑3.6%prior 494

NISSAN492 (7.4%)

↓-6.3%prior 525

SUBARU404 (6%)

↑8.9%prior 371

JEEP290 (4.3%)

↓-6.5%prior 310

HYUNDAI245 (3.7%)

↓-0.8%prior 247

KIA180 (2.7%)

↑5.3%prior 171

GMC165 (2.5%)

↑26.9%prior 130

Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (8,095 persons with recorded sex)

Male4,482 (55.4%)

↑0.6%prior 4,455

Female3,613 (44.6%)

↑1.2%prior 3,569

Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across speed zones was similar year-over-year, with the 25 mph, 35 mph, and 65 mph zones accounting for the highest volumes. A significant improvement was observed in fatal outcomes within specific zones. Fatalities in 25 mph zones decreased from 5 to 2, and fatalities in 65 mph zones fell from 6 to 4, even as total crashes in that zone increased.

Fatal crashes by zone: 25 mph: 2 of 735 (0.272%) · 30 mph: 2 of 294 (0.68%) · 35 mph: 3 of 551 (0.544%) · 40 mph: 1 of 283 (0.353%) · 45 mph: 1 of 289 (0.346%) · 65 mph: 4 of 709 (0.564%) · 88 mph: 1 of 188 (0.532%)

Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
Report generated: August 20, 2026

Data Coverage

Reporting period: 2024-01-01 through 2024-12-31 (366 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 3,682
Total persons involved: 8,532
Total vehicles involved: 6,690

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: 2024." Published August 20, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2024-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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