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Yearly Traffic Safety Analysis

1,634 CRASHES IN
CONNECTICUT, CT
2020

All metrics benchmarked against2019

In 2020, Windham County recorded 1,634 total traffic crashes, a 23.3% decrease from the 2,131 crashes reported in 2019. This overall reduction was accompanied by a decrease in both fatalities, which fell from 17 to 12, and injuries, which declined from 676 to 646. The most notable shift despite the overall downturn was a significant increase in the number of serious injury crashes, which more than doubled from 18 in 2019 to 37 in 2020.

1,634

▼ -23.3%was 2,131

Total Crash Events

▼ -29.4%was 17

Persons Killed

646

▼ -4.4%was 676

Persons Injured

157

▼ -4.8%was 165

Hit-and-Run Crashes

Note: "Persons Killed" (12) counts individual fatalities across all crash events. "Fatal" in the severity table below (12) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety metrics in Windham County showed a downward trend from 2019 to 2020. Total crashes fell by 497 incidents, representing a 23.3% decrease. This trend extended to crash outcomes, with total fatalities decreasing by 29.4% (from 17 to 12) and total injuries seeing a smaller decline of 4.4% (from 676 to 646).

157

Hit-and-Run Crashes — 2020

▼ -4.8% vs prior (165)

While the absolute number of hit-and-run incidents decreased slightly from 165 in 2019 to 157 in 2020, the hit-and-run rate as a percentage of total crashes showed an upward trend. In 2020, hit-and-runs accounted for 9.6% of all crashes, a notable increase from the 7.7% rate observed in the prior year.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 1↓-100.0%

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 16↓-25.0%

Pedestrians Injured

Prior: 10↑10.0%

Cyclists Injured

Prior: 8↓-62.5%

632

Motorists Injured

Prior: 658↓-4.0%

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed some changes between the two periods. The peak day for crashes shifted from Thursday (340 crashes) in 2019 to Friday (275 crashes) in 2020. However, the afternoon rush hour remained the most frequent time for collisions, with the 3 p.m. hour being the peak in both 2019 (187 crashes) and 2020 (142 crashes), though the volume of crashes during this peak hour decreased.

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-12-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes decreased, the severity distribution shifted year-over-year. The fatal crash rate remained relatively stable, moving from 0.75% in 2019 to 0.73% in 2020. However, the number of crashes resulting in serious injuries more than doubled, increasing from 18 in 2019 to 37 in 2020. Consequently, the proportion of crashes classified as 'Serious Injury' rose from 0.8% to 2.3% of all incidents.

Outcome by Severity (Crash Events)

Fatal12fatal crashes0.7%

↓-25.0%prior 16

Serious Injury37serious injury crashes2.3%

↑105.6%prior 18

Minor Injury257minor injury crashes15.7%

↓-7.9%prior 279

Possible Injury186possible injury crashes11.4%

↓-9.7%prior 206

No Injury1,142no injury crashes69.9%

↓-29.2%prior 1,612

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-12-31 · Most severe injury per crash record

Road & Environmental Conditions

Crashes in 2020 were more concentrated in clear weather and on dry roads compared to 2019. In 2020, 79.4% of crashes occurred in clear weather, up from 75.7% in the prior year. Similarly, 77.2% of crashes happened on dry road surfaces, compared to 71.7% in 2019. The proportion of crashes occurring on roads with snow or ice saw a significant decrease, accounting for 4.7% of incidents in 2020 versus 9.0% in 2019.

Weather

Clear1,297 (79.5%)

↓-19.6%prior 1,613

Rain186 (11.4%)

↓-19.5%prior 231

Cloudy51 (3.1%)

↓-34.6%prior 78

Snow44 (2.7%)

↓-56.0%prior 100

Freezing Rain or Freezing Drizzle33 (2.0%)

↓-42.1%prior 57

Fog, Smog, Smoke12 (0.7%)

↑50.0%prior 8

Blowing Snow7 (0.4%)

↓-74.1%prior 27

Severe Crosswinds2 (0.1%)

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-12-31 · Weather condition at time of crash

Lighting

Daylight1,067 (65.5%)

↓-24.9%prior 1,421

Dark-Not Lighted287 (17.6%)

↓-23.7%prior 376

Dark-Lighted223 (13.7%)

↓-13.6%prior 258

Dusk22 (1.4%)

↓-46.3%prior 41

Dawn16 (1.0%)

↓-20.0%prior 20

Dark-Unknown Lighting11 (0.7%)

↑57.1%prior 7

Other3 (0.2%)

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-12-31 · Lighting condition field

Road Surface

Dry1,261 (77.3%)

↓-17.4%prior 1,527

Wet263 (16.1%)

↓-18.1%prior 321

Ice / Frost44 (2.7%)

↓-48.2%prior 85

Snow33 (2.0%)

↓-69.2%prior 107

Slush21 (1.3%)

↓-66.7%prior 63

Standing Water4 (0.2%)

↓-20.0%prior 5

Mud, Dirt, Gravel3 (0.2%)

↓-62.5%prior 8

Other2 (0.1%)

Moving Water1 (0.1%)

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes showed stability in rankings between the two years. Ford was the most common make involved in both 2020 (355 vehicles) and 2019 (492 vehicles), followed by Toyota, Chevrolet, and Honda. The age distribution of persons involved in crashes also remained largely consistent, with the 26-34 age group being the largest cohort in both periods. There was a slight proportional decrease in the involvement of individuals aged 65 and older, who represented 9.0% of persons in 2020 compared to 10.2% in 2019.

Top Vehicle Makes (2,651 vehicles)

FORD355 (13.4%)

↓-27.8%prior 492

TOYOTA201 (7.6%)

↓-21.2%prior 255

NISSAN170 (6.4%)

↓-12.4%prior 194

CHEVROLET165 (6.2%)

↓-34.3%prior 251

HONDA157 (5.9%)

↓-21.5%prior 200

JEEP122 (4.6%)

↓-10.9%prior 137

CHEV98 (3.7%)

↓-17.6%prior 119

HYUNDAI93 (3.5%)

↓-19.8%prior 116

SUBARU74 (2.8%)

↓-37.3%prior 118

DODGE71 (2.7%)

↓-37.7%prior 114

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-12-31 · Vehicle unit records

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

Sex Distribution (3,345 persons with recorded sex)

Male1,943 (58.1%)

↓-21.7%prior 2,482

Female1,402 (41.9%)

↓-30.7%prior 2,022

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-12-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across different speed zones remained relatively stable, with a minor proportional shift toward higher speed zones in 2020. Crashes in zones of 40 mph or higher accounted for 37.2% of the total in 2020, compared to 35.7% in 2019. While the number of crashes in the 65 mph zone decreased from 241 to 182, the fatal crash rate within that zone increased, with two fatal crashes recorded in 2020 compared to one in 2019.

Fatal crashes by zone: 1 mph: 1 of 89 (1.124%) · 25 mph: 2 of 459 (0.436%) · 35 mph: 2 of 239 (0.837%) · 45 mph: 4 of 207 (1.932%) · 50 mph: 1 of 44 (2.273%) · 65 mph: 2 of 182 (1.099%)

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-12-31 · 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: 2020-01-01 through 2020-12-31
Report generated: August 20, 2026

Data Coverage

Reporting period: 2020-01-01 through 2020-12-31 (366 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 1,634
Total persons involved: 3,513
Total vehicles involved: 2,651

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: 2020." Published August 20, 2026. Reporting period: 2020-01-01 to 2020-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2020-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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