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Yearly Traffic Safety Analysis

2,046 CRASHES IN
CONNECTICUT, CT
2023

All metrics benchmarked against2022

In 2023, Windham County recorded 2,046 total vehicle crashes, a 2.5% increase from the 1,997 crashes reported in 2022. Despite the rise in total incidents, the number of fatalities saw a significant year-over-year decrease. Fatalities dropped by 58.1%, from 31 in 2022 to 13 in 2023.

2,046

▲ 2.5%was 1,997

Total Crash Events

▼ -58.1%was 31

Persons Killed

701

▼ -4.1%was 731

Persons Injured

215

▲ 3.4%was 208

Hit-and-Run Crashes

Note: "Persons Killed" (13) counts individual fatalities across all crash events. "Fatal" in the severity table below (13) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in Windham County shows a slight increase in total crashes, rising by 2.5% from 1,997 in 2022 to 2,046 in 2023. However, the severity of these crashes decreased notably. Total injuries fell by 4.1% from 731 to 701, and total fatalities decreased by 58.1% from 31 to 13.

215

Hit-and-Run Crashes — 2023

▲ 3.4% vs prior (208)

The number of hit-and-run incidents in Windham County saw a minor increase, rising from 208 in 2022 to 215 in 2023. This represents a 3.4% increase in the raw count of hit-and-run crashes. The hit-and-run rate as a percentage of total crashes remained stable, moving from 10.4% in 2022 to 10.5% in 2023, indicating no significant change in the trend.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 2→0.0%

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 29↓-62.1%

Pedestrians Injured

Prior: 22↓-31.8%

Cyclists Injured

Prior: 5→0.0%

681

Motorists Injured

Prior: 704↓-3.3%

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes in Windham County remained consistent year-over-year. Friday was the peak day for crashes in both 2023 (343 crashes) and 2022 (329 crashes). Similarly, the 3 p.m. hour was the most frequent time for incidents in both periods, with 202 crashes in 2023 compared to 184 in 2022.

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased in 2023 compared to the prior year. The proportion of fatal crashes was halved, dropping from 1.3% of all crashes in 2022 to 0.6% in 2023. While crashes resulting in serious injury saw a slight proportional increase from 1.5% to 1.8%, the share of minor and possible injury crashes remained relatively stable. Consequently, the percentage of crashes with no reported injuries rose from 72.1% to 73.5%.

Outcome by Severity (Crash Events)

Fatal13fatal crashes0.6%

↓-50.0%prior 26

Serious Injury37serious injury crashes1.8%

↑23.3%prior 30

Minor Injury328minor injury crashes16%

↓-1.8%prior 334

Possible Injury165possible injury crashes8.1%

↓-1.2%prior 167

No Injury1,503no injury crashes73.5%

↑4.4%prior 1,440

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The distribution of crashes across environmental conditions remained largely stable year-over-year. Crashes in clear weather and daylight conditions continued to be the most common scenarios in both periods, with proportions changing by less than 3 percentage points. There was a modest increase in the share of crashes occurring on wet road surfaces, which rose from 14.1% of all incidents in 2022 to 17.1% in 2023. Similarly, crashes in dark, unlit conditions increased proportionally from 14.7% to 16.0%.

Weather

Clear1,577 (77.2%)

↓-1.5%prior 1,601

Rain245 (12.0%)

↑22.5%prior 200

Cloudy101 (4.9%)

↑50.7%prior 67

Snow39 (1.9%)

↓-37.1%prior 62

Freezing Rain or Freezing Drizzle29 (1.4%)

↑45.0%prior 20

Fog, Smog, Smoke26 (1.3%)

↑136.4%prior 11

Blowing Snow20 (1.0%)

↓-4.8%prior 21

Other3 (0.1%)

Severe Crosswinds2 (0.1%)

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight1,367 (67.0%)

↑2.2%prior 1,338

Dark-Not Lighted327 (16.0%)

↑11.6%prior 293

Dark-Lighted259 (12.7%)

↑1.2%prior 256

Dusk39 (1.9%)

↓-4.9%prior 41

Dawn30 (1.5%)

↑30.4%prior 23

Dark-Unknown Lighting15 (0.7%)

↓-59.5%prior 37

Other3 (0.1%)

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry1,555 (76.2%)

↑0.6%prior 1,545

Wet349 (17.1%)

↑24.2%prior 281

Ice / Frost61 (3.0%)

↑17.3%prior 52

Snow42 (2.1%)

↓-48.8%prior 82

Slush11 (0.5%)

↓-26.7%prior 15

Mud, Dirt, Gravel8 (0.4%)

↑14.3%prior 7

Moving Water6 (0.3%)

Standing Water6 (0.3%)

Other2 (0.1%)

↓-60.0%prior 5

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The top five vehicle makes involved in crashes remained consistent, with Ford, Toyota, Honda, Chevrolet, and Nissan leading in both 2022 and 2023. Ford vehicles saw the largest increase in crash involvement, rising from 426 to 452. Analysis of persons involved in crashes shows the 26-34 age group was the most represented in both periods, increasing from 708 individuals in 2022 to 733 in 2023. Notably, the number of individuals in the 0-15 age group involved in crashes increased from 277 to 400.

Top Vehicle Makes (3,414 vehicles)

FORD452 (13.2%)

↑6.1%prior 426

TOYOTA298 (8.7%)

↓-6.0%prior 317

HONDA255 (7.5%)

↑9.9%prior 232

CHEVROLET242 (7.1%)

↑6.6%prior 227

NISSAN220 (6.4%)

↑4.8%prior 210

SUBARU186 (5.4%)

↑14.1%prior 163

JEEP174 (5.1%)

↑6.1%prior 164

HYUNDAI147 (4.3%)

↓-3.9%prior 153

KIA93 (2.7%)

↑25.7%prior 74

VOLKSWAGEN77 (2.3%)

↓-6.1%prior 82

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (4,088 persons with recorded sex)

Male2,320 (56.8%)

↑2.0%prior 2,274

Female1,768 (43.2%)

↑0.3%prior 1,763

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across different speed zones showed minimal change, with 25 mph zones accounting for the highest number of incidents in both 2023 (579 crashes) and 2022 (572 crashes). A significant shift occurred in the fatality rate within specific speed zones. In 2022, the 50 mph zone had a high fatality rate of 12.1% with 7 fatal crashes, which dropped dramatically to 2.0% with only 1 fatal crash in 2023.

Fatal crashes by zone: 20 mph: 1 of 12 (8.333%) · 25 mph: 2 of 579 (0.345%) · 30 mph: 2 of 247 (0.81%) · 35 mph: 2 of 316 (0.633%) · 40 mph: 3 of 185 (1.622%) · 45 mph: 1 of 264 (0.379%) · 50 mph: 1 of 49 (2.041%) · 65 mph: 1 of 243 (0.412%)

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
Report generated: August 20, 2026

Data Coverage

Reporting period: 2023-01-01 through 2023-12-31 (365 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 2,046
Total persons involved: 4,495
Total vehicles involved: 3,414

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: 2023." Published August 20, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2023-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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