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Yearly Traffic Safety Analysis

1,986 CRASHES IN
CONNECTICUT, CT
2020

All metrics benchmarked against2019

In Tolland County, total traffic crashes decreased from 2,792 in 2019 to 1,986 in 2020, a reduction of approximately 28.9%. Despite this significant drop in overall collisions, the number of fatalities more than doubled, increasing from 10 in the prior period to 22 in the current period. This stark contrast highlights a rise in crash severity even as crash frequency declined.

1,986

▼ -28.9%was 2,792

Total Crash Events

▲ 120.0%was 10

Persons Killed

715

▼ -19.4%was 887

Persons Injured

179

▼ -7.7%was 194

Hit-and-Run Crashes

Note: "Persons Killed" (22) counts individual fatalities across all crash events. "Fatal" in the severity table below (21) 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

Overall crash volume in Tolland County saw a significant year-over-year decline, with total incidents falling by 28.9% from 2,792 to 1,986. Similarly, the number of injuries decreased by 19.4%. However, this downward trend in crash frequency was accompanied by a sharp 120% increase in fatalities, which rose from 10 to 22.

179

Hit-and-Run Crashes — 2020

▼ -7.7% vs prior (194)

While the absolute number of hit-and-run incidents decreased slightly from 194 in 2019 to 179 in 2020, the hit-and-run rate increased. These incidents accounted for 9.0% of all crashes in 2020, up from 6.9% in the prior year. This indicates that hit-and-run crashes became a larger proportion of the total crash landscape.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 0→%

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 10↑100.0%

Pedestrians Injured

Prior: 10↑60.0%

Cyclists Injured

Prior: 4↑75.0%

692

Motorists Injured

Prior: 873↓-20.7%

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 shifted between the two periods. While the peak hour for collisions remained 5 p.m. in both years, the peak day moved from Monday (452 crashes) in 2019 to Friday (325 crashes) in 2020. Overall crash counts were lower across all days and hours in 2020 compared to the prior year.

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, their severity increased year-over-year. The proportion of fatal crashes rose from 0.4% of all crashes in 2019 to 1.1% in 2020, with the absolute count of fatal crashes more than doubling from 10 to 21. The share of crashes resulting in any type of injury (Fatal, Serious, Minor, or Possible) also increased from 23.8% in 2019 to 27.7% in 2020.

Severity is per crash event (most severe injury). 21 fatal crash events resulted in 22 persons killed.

Outcome by Severity (Crash Events)

Fatal21fatal crashes1.1%

↑110.0%prior 10

Serious Injury26serious injury crashes1.3%

↓-10.3%prior 29

Minor Injury281minor injury crashes14.1%

↓-19.5%prior 349

Possible Injury222possible injury crashes11.2%

↓-19.6%prior 276

No Injury1,436no injury crashes72.3%

↓-32.5%prior 2,128

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 likely to occur on dry roads (77.0% vs. 68.8% in 2019) and in clear weather (78.7% vs. 74.6%). There was a slight proportional shift towards crashes occurring in darkness, which accounted for 32.2% of incidents in 2020 compared to 29.5% in 2019. Conversely, the share of crashes happening during daylight hours decreased from 67.0% to 65.0%.

Weather

Clear1,563 (79.2%)

↓-25.0%prior 2,084

Rain198 (10.0%)

↓-35.3%prior 306

Snow79 (4.0%)

↓-49.0%prior 155

Cloudy77 (3.9%)

↓-30.0%prior 110

Fog, Smog, Smoke18 (0.9%)

↑20.0%prior 15

Blowing Snow15 (0.8%)

↓-25.0%prior 20

Freezing Rain or Freezing Drizzle14 (0.7%)

↓-73.1%prior 52

Severe Crosswinds5 (0.3%)

Other3 (0.2%)

Sleet or Hail1 (0.1%)

↓-96.6%prior 29

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

Lighting

Daylight1,290 (65.4%)

↓-31.0%prior 1,870

Dark-Lighted323 (16.4%)

↓-21.4%prior 411

Dark-Not Lighted311 (15.8%)

↓-23.2%prior 405

Dusk28 (1.4%)

↓-45.1%prior 51

Dawn10 (0.5%)

↓-60.0%prior 25

Dark-Unknown Lighting5 (0.3%)

↓-28.6%prior 7

Other4 (0.2%)

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

Road Surface

Dry1,529 (77.5%)

↓-20.4%prior 1,922

Wet329 (16.7%)

↓-31.5%prior 480

Snow69 (3.5%)

↓-52.1%prior 144

Ice / Frost21 (1.1%)

↓-82.2%prior 118

Slush14 (0.7%)

↓-85.3%prior 95

Mud, Dirt, Gravel7 (0.4%)

↓-36.4%prior 11

Sand2 (0.1%)

Oil1 (0.1%)

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

Vehicles & Demographics

The composition of vehicles involved in crashes remained consistent year-over-year, with Ford, Honda, and Toyota being the top three makes in both 2019 and 2020. Similarly, the age distribution of persons involved in collisions showed no significant shifts. The proportional representation of major age groups, such as 16-20 year-olds (13.2% in 2020 vs. 13.4% in 2019) and those 65 and older (9.6% vs. 9.9%), was stable across both periods.

Top Vehicle Makes (3,387 vehicles)

FORD391 (11.5%)

↓-32.5%prior 579

HONDA322 (9.5%)

↓-25.8%prior 434

TOYOTA301 (8.9%)

↓-38.3%prior 488

NISSAN231 (6.8%)

↓-29.1%prior 326

CHEVROLET228 (6.7%)

↓-15.6%prior 270

SUBARU168 (5%)

↓-38.9%prior 275

JEEP154 (4.5%)

↓-29.0%prior 217

HYUNDAI134 (4%)

↓-29.1%prior 189

DODGE90 (2.7%)

↓-36.2%prior 141

GMC88 (2.6%)

↓-11.1%prior 99

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

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

Sex Distribution (4,343 persons with recorded sex)

Male2,462 (56.7%)

↓-28.1%prior 3,425

Female1,881 (43.3%)

↓-30.7%prior 2,713

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 the majority occurring in zones of 45 mph or less in both years. However, the lethality of crashes in these zones increased notably in 2020. For instance, the fatal crash rate in 45 mph zones nearly doubled from 1.13% to 2.22%, and the rate in 40 mph zones increased from 0.51% to 1.75%.

Fatal crashes by zone: 1 mph: 2 of 126 (1.587%) · 30 mph: 3 of 295 (1.017%) · 35 mph: 4 of 462 (0.866%) · 40 mph: 5 of 285 (1.754%) · 45 mph: 5 of 225 (2.222%) · 65 mph: 2 of 193 (1.036%)

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 21, 2026

Data Coverage

Reporting period: 2020-01-01 through 2020-12-31 (366 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 1,986
Total persons involved: 4,583
Total vehicles involved: 3,387

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 21, 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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