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Monthly Traffic Safety Analysis

8,120 CRASHES IN
CONNECTICUT, CT
AUGUST 2023

All metrics benchmarked againstAugust 2022

In August 2023, Connecticut recorded 8,120 total vehicle crashes, a 1.6% decrease from the 8,251 crashes reported in August 2022. While overall crashes and fatalities (22, down from 26) declined, total injuries rose by 2.0% to 2,961. The most significant year-over-year shift was an 85.3% increase in bicycle-involved crashes, rising from 34 to 63 incidents.

8,120

▼ -1.6%was 8,251

Total Crash Events

▼ -15.4%was 26

Persons Killed

2,961

▲ 2.0%was 2,904

Persons Injured

981

▼ -8.2%was 1,069

Hit-and-Run Crashes

Note: "Persons Killed" (22) counts individual fatalities across all crash events. "Fatal" in the severity table below (22) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2023-08-01 to 2023-08-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crash trends showed a slight improvement year-over-year, with total collisions falling by 1.6% from 8,251 to 8,120. Fatalities also saw a notable decrease of 15.4%, dropping from 26 to 22. However, the number of people injured in crashes saw a slight 2.0% increase, rising from 2,904 to 2,961.

981

Hit-and-Run Crashes — August 2023

▼ -8.2% vs prior (1,069)

Hit-and-run incidents decreased in August 2023 compared to the previous year. The total count of hit-and-run crashes fell from 1,069 to 981. The corresponding hit-and-run rate also trended downward, dropping from 13.0% to 12.1% of all crashes.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 6↓-16.7%

Cyclists Killed

Prior: 1↓-100.0%

Motorists Killed

Prior: 19↓-10.5%

Pedestrians Injured

Prior: 91↑3.3%

Cyclists Injured

Prior: 29↑82.8%

2,814

Motorists Injured

Prior: 2,784↑1.1%

Source: Connecticut Crash Data · Csv Open Data · 2023-08-01 to 2023-08-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes remained broadly consistent year-over-year, though the peak day for incidents shifted from Wednesday (1,384 crashes) in 2022 to Tuesday (1,337 crashes) in 2023. The peak hour for collisions was unchanged, remaining the 4 p.m. hour in both periods, with 725 crashes in August 2023 compared to 753 in the prior year.

Source: Connecticut Crash Data · Csv Open Data · 2023-08-01 to 2023-08-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2023-08-01 to 2023-08-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity outcomes showed a mixed trend compared to the prior year. The fatal crash rate decreased slightly from 0.32% to 0.27% of all crashes. The proportion of crashes resulting in serious injuries also declined from 1.7% to 1.3%. Conversely, crashes classified with 'possible injury' increased from 11.7% to 12.7% of the total.

Outcome by Severity (Crash Events)

Fatal22fatal crashes0.3%

↓-15.4%prior 26

Serious Injury106serious injury crashes1.3%

↓-23.7%prior 139

Minor Injury1,005minor injury crashes12.4%

↓-0.8%prior 1,013

Possible Injury1,032possible injury crashes12.7%

↑7.2%prior 963

No Injury5,955no injury crashes73.3%

↓-2.5%prior 6,110

Source: Connecticut Crash Data · Csv Open Data · 2023-08-01 to 2023-08-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2023-08-01 to 2023-08-31 · Most severe injury per crash record

Road & Environmental Conditions

While lighting conditions at the time of crashes remained stable year-over-year, there was a significant shift in weather and road surface conditions. In August 2023, 10.6% of crashes occurred during rain, more than double the 4.0% reported in August 2022. Consequently, crashes on wet roads increased from 5.9% to 14.5% of all incidents.

Weather

Clear6,855 (84.9%)

↓-10.1%prior 7,629

Rain863 (10.7%)

↑161.5%prior 330

Cloudy344 (4.3%)

↑58.5%prior 217

Fog, Smog, Smoke9 (0.1%)

↓-30.8%prior 13

Other3 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2023-08-01 to 2023-08-31 · Weather condition at time of crash

Lighting

Daylight6,326 (78.5%)

↓-0.9%prior 6,383

Dark-Lighted1,208 (15.0%)

↓-2.4%prior 1,238

Dark-Not Lighted328 (4.1%)

↓-15.2%prior 387

Dusk97 (1.2%)

↑18.3%prior 82

Dawn48 (0.6%)

↑11.6%prior 43

Dark-Unknown Lighting46 (0.6%)

↑9.5%prior 42

Other10 (0.1%)

Source: Connecticut Crash Data · Csv Open Data · 2023-08-01 to 2023-08-31 · Lighting condition field

Road Surface

Dry6,880 (85.2%)

↓-10.4%prior 7,677

Wet1,174 (14.5%)

↑139.1%prior 491

Mud, Dirt, Gravel12 (0.1%)

↓-20.0%prior 15

Other3 (0.0%)

↓-40.0%prior 5

Oil2 (0.0%)

Sand2 (0.0%)

Moving Water1 (0.0%)

Standing Water1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2023-08-01 to 2023-08-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes were nearly identical across both periods, with Honda, Toyota, and Ford consistently ranking as the top three. The demographic profile of persons involved in crashes also remained stable. The only slight change was a minor proportional increase in individuals aged 65 and older, who represented 11.2% of persons involved in August 2023, up from 10.4% in the prior year.

Top Vehicle Makes (15,442 vehicles)

HONDA1,705 (11%)

↓-1.7%prior 1,735

TOYOTA1,612 (10.4%)

↓-0.2%prior 1,615

FORD1,382 (8.9%)

↑5.3%prior 1,312

NISSAN1,101 (7.1%)

↓-0.5%prior 1,107

CHEVROLET908 (5.9%)

↓-6.0%prior 966

SUBARU722 (4.7%)

↑6.6%prior 677

JEEP693 (4.5%)

↑1.5%prior 683

HYUNDAI641 (4.2%)

↑1.7%prior 630

KIA368 (2.4%)

↑9.9%prior 335

BMW335 (2.2%)

↓-11.4%prior 378

Source: Connecticut Crash Data · Csv Open Data · 2023-08-01 to 2023-08-31 · Vehicle unit records

1,229 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (18,300 persons with recorded sex)

Male10,439 (57.0%)

↑0.2%prior 10,418

Female7,861 (43.0%)

↓-1.6%prior 7,986

Source: Connecticut Crash Data · Csv Open Data · 2023-08-01 to 2023-08-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across different speed zones was similar year-over-year, with a slight decrease in crashes in zones 35 mph or less and a slight increase in zones 40 mph or more. A more notable shift occurred in fatal crash locations; fatalities in lower-speed zones (35 mph or less) increased from 15 to 17, while fatalities in higher-speed zones (40 mph or more) fell from 9 to 4.

Fatal crashes by zone: 1 mph: 1 of 1,018 (0.098%) · 5 mph: 1 of 11 (9.091%) · 25 mph: 5 of 2,217 (0.226%) · 30 mph: 3 of 615 (0.488%) · 35 mph: 6 of 933 (0.643%) · 40 mph: 2 of 484 (0.413%) · 50 mph: 1 of 243 (0.412%) · 55 mph: 2 of 818 (0.244%) · 65 mph: 1 of 526 (0.19%)

Source: Connecticut Crash Data · Csv Open Data · 2023-08-01 to 2023-08-31 · 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: 2023-08-01 through 2023-08-31
Report generated: August 20, 2026

Data Coverage

Reporting period: 2023-08-01 through 2023-08-31 (31 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 8,120
Total persons involved: 19,751
Total vehicles involved: 15,442

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: August 2023." Published August 20, 2026. Reporting period: 2023-08-01 to 2023-08-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/august-2023-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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