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Monthly Traffic Safety Analysis

8,396 CRASHES IN
CONNECTICUT, CT
JULY 2024

All metrics benchmarked againstJuly 2023

In July 2024, Connecticut recorded 8,396 traffic crashes, a 1.9% decrease from the 8,555 crashes in July 2023. While overall crash volume remained relatively stable, the most significant year-over-year change was a 37.5% reduction in traffic fatalities, which fell from 40 to 25.

8,396

▼ -1.9%was 8,555

Total Crash Events

▼ -37.5%was 40

Persons Killed

2,901

▼ -4.7%was 3,043

Persons Injured

1,118

▲ 6.0%was 1,055

Hit-and-Run Crashes

Note: "Persons Killed" (25) counts individual fatalities across all crash events. "Fatal" in the severity table below (18) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2024-07-01 to 2024-07-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Year-over-year data indicates a slight downward trend in overall traffic collisions. Total crashes fell by 1.9% from 8,555 to 8,396, and total injuries decreased by 4.7% from 3,043 to 2,901. Most notably, traffic fatalities saw a substantial 37.5% decline from 40 in July 2023 to 25 in July 2024.

1,118

Hit-and-Run Crashes — July 2024

▲ 6.0% vs prior (1,055)

Hit-and-run incidents increased in both absolute numbers and as a percentage of total crashes. The count of hit-and-run crashes rose by 6.0%, from 1,055 in July 2023 to 1,118 in July 2024. Consequently, the hit-and-run rate increased from 12.3% to 13.3% of all collisions during the same period.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 4↓-100.0%

Cyclists Killed

Prior: 1↓-100.0%

Motorists Killed

Prior: 35↓-28.6%

Pedestrians Injured

Prior: 59↑57.6%

Cyclists Injured

Prior: 50↓-26.0%

2,771

Motorists Injured

Prior: 2,934↓-5.6%

Source: Connecticut Crash Data · Csv Open Data · 2024-07-01 to 2024-07-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted slightly year-over-year. The day with the highest number of crashes moved from Saturday (1,403 crashes) in the prior period to Wednesday (1,421 crashes) in the current period. The peak hour for collisions also shifted one hour later, from 3 p.m. (726 crashes) in July 2023 to 4 p.m. (702 crashes) in July 2024.

Source: Connecticut Crash Data · Csv Open Data · 2024-07-01 to 2024-07-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2024-07-01 to 2024-07-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes decreased compared to the previous year. The proportion of fatal crashes dropped from 0.5% to 0.2% of all incidents, with the total number of fatal crashes falling from 39 to 18. Correspondingly, the share of crashes resulting in no injuries increased from 73.8% to 75.3%.

Severity is per crash event (most severe injury). 18 fatal crash events resulted in 25 persons killed.

Outcome by Severity (Crash Events)

Fatal18fatal crashes0.2%

↓-53.8%prior 39

Serious Injury129serious injury crashes1.5%

↑5.7%prior 122

Minor Injury1,040minor injury crashes12.4%

↓-4.1%prior 1,084

Possible Injury890possible injury crashes10.6%

↓-10.6%prior 995

No Injury6,319no injury crashes75.3%

↑0.1%prior 6,315

Source: Connecticut Crash Data · Csv Open Data · 2024-07-01 to 2024-07-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2024-07-01 to 2024-07-31 · Most severe injury per crash record

Road & Environmental Conditions

Crashes in July 2024 occurred under slightly better environmental conditions than in the same month last year. The proportion of crashes on wet roads decreased from 13.4% to 9.7% of the total. Similarly, collisions during rainy weather dropped from 9.5% to 6.9% of all incidents, with the vast majority of crashes in both periods occurring on dry roads in clear weather.

Weather

Clear7,416 (88.8%)

↑0.9%prior 7,347

Rain577 (6.9%)

↓-28.7%prior 809

Cloudy342 (4.1%)

↑8.9%prior 314

Fog, Smog, Smoke16 (0.2%)

↓-42.9%prior 28

Other3 (0.0%)

↓-40.0%prior 5

Source: Connecticut Crash Data · Csv Open Data · 2024-07-01 to 2024-07-31 · Weather condition at time of crash

Lighting

Daylight6,672 (80.0%)

↑0.2%prior 6,657

Dark-Lighted1,114 (13.4%)

↓-8.5%prior 1,217

Dark-Not Lighted359 (4.3%)

↓-11.8%prior 407

Dusk85 (1.0%)

↓-9.6%prior 94

Dark-Unknown Lighting52 (0.6%)

↑15.6%prior 45

Dawn45 (0.5%)

↓-13.5%prior 52

Other8 (0.1%)

↓-38.5%prior 13

Source: Connecticut Crash Data · Csv Open Data · 2024-07-01 to 2024-07-31 · Lighting condition field

Road Surface

Dry7,504 (89.9%)

↑2.6%prior 7,315

Wet817 (9.8%)

↓-28.6%prior 1,145

Mud, Dirt, Gravel8 (0.1%)

↓-42.9%prior 14

Moving Water5 (0.1%)

↓-37.5%prior 8

Other4 (0.0%)

↓-50.0%prior 8

Oil3 (0.0%)

Standing Water2 (0.0%)

↓-81.8%prior 11

Sand1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2024-07-01 to 2024-07-31 · Road surface condition field

Vehicles & Demographics

The top five vehicle makes involved in crashes—Honda, Toyota, Ford, Nissan, and Chevrolet—were identical in both periods, indicating stable vehicle make demographics. An analysis of persons involved shows a consistent age distribution, with the 26-34 age group being the largest in both years. There was a minor proportional shift, with individuals aged 16-20 representing 10.4% of persons involved, up from 9.7% the previous year.

Top Vehicle Makes (15,903 vehicles)

HONDA1,775 (11.2%)

↓-3.6%prior 1,841

TOYOTA1,720 (10.8%)

↑3.3%prior 1,665

FORD1,394 (8.8%)

↓-3.1%prior 1,438

NISSAN1,055 (6.6%)

↓-8.9%prior 1,158

CHEVROLET991 (6.2%)

↓-0.7%prior 998

SUBARU756 (4.8%)

↑9.1%prior 693

JEEP658 (4.1%)

↓-3.9%prior 685

HYUNDAI617 (3.9%)

↓-9.1%prior 679

KIA388 (2.4%)

↑4.9%prior 370

BMW357 (2.2%)

↓-1.1%prior 361

Source: Connecticut Crash Data · Csv Open Data · 2024-07-01 to 2024-07-31 · Vehicle unit records

1,272 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (18,910 persons with recorded sex)

Male10,838 (57.3%)

↓-0.8%prior 10,920

Female8,072 (42.7%)

↓-0.9%prior 8,145

Source: Connecticut Crash Data · Csv Open Data · 2024-07-01 to 2024-07-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across different speed zones remained largely unchanged year-over-year, with 25 mph zones continuing to see the highest volume at 2,364 crashes. However, there was a notable reduction in the number of fatalities within these zones. Fatalities in 30 mph zones dropped from 7 to 3, and fatalities in 65 mph zones fell from 6 to 1, contributing to the overall decrease in fatal outcomes.

Fatal crashes by zone: 25 mph: 5 of 2,364 (0.212%) · 30 mph: 3 of 591 (0.508%) · 35 mph: 4 of 936 (0.427%) · 40 mph: 1 of 445 (0.225%) · 45 mph: 2 of 329 (0.608%) · 50 mph: 1 of 213 (0.469%) · 55 mph: 1 of 873 (0.115%) · 65 mph: 1 of 584 (0.171%)

Source: Connecticut Crash Data · Csv Open Data · 2024-07-01 to 2024-07-31 · 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: 2024-07-01 through 2024-07-31
Report generated: August 20, 2026

Data Coverage

Reporting period: 2024-07-01 through 2024-07-31 (31 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 8,396
Total persons involved: 20,431
Total vehicles involved: 15,903

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 2024." Published August 20, 2026. Reporting period: 2024-07-01 to 2024-07-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/july-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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