Yearly Traffic Safety Analysis

3,056 CRASHES IN
CONNECTICUT, CT
2020

All metrics benchmarked against2019

In Litchfield County, total traffic crashes decreased by 23.9% from 4,015 in 2019 to 3,056 in 2020. Despite this significant drop in overall collisions, the number of fatalities increased slightly from 18 to 20, and the fatal crash rate rose from 0.42% to 0.62%.

3,056

▼ -23.9%was 4,015

Total Crash Events

20

▲ 11.1%was 18

Persons Killed

999

▼ -19.2%was 1,237

Persons Injured

240

▼ -28.8%was 337

Hit-and-Run Crashes

Note: "Persons Killed" (20) counts individual fatalities across all crash events. "Fatal" in the severity table below (19) 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

Crash data for Litchfield County indicates a significant downward trend in collisions year-over-year. The total number of crashes fell from 4,015 to 3,056, a 23.9% reduction. Similarly, the number of people injured in these incidents decreased by 19.2%, from 1,237 in the prior year to 999 in the current year.

240

Hit-and-Run Crashes — 2020

▼ -28.8% vs prior (337)

The total number of hit-and-run crashes decreased from 337 in 2019 to 240 in 2020. The hit-and-run rate, which measures the percentage of all crashes that are hit-and-runs, also trended downward slightly. The rate fell from 8.4% in the prior period to 7.9% in the current period.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 2↓-50.0%

0

Cyclists Killed

Prior: 1↓-100.0%

19

Motorists Killed

Prior: 15↑26.7%

21

Pedestrians Injured

Prior: 19↑10.5%

16

Cyclists Injured

Prior: 8↑100.0%

962

Motorists Injured

Prior: 1,210↓-20.5%

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 peak day for crashes shifted from Friday (624 crashes) in the prior period to Saturday (515 crashes) in the current period. The busiest hour for collisions also moved slightly earlier, from 3 p.m. in 2019 (356 crashes) to 2 p.m. in 2020 (269 crashes). Overall crash volumes were lower on every day of the week compared to the previous 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

Although total crashes declined, the severity of crashes increased year-over-year. The fatal crash rate rose from 0.42% to 0.62%, with fatal crashes increasing from 17 to 19. The proportion of crashes resulting in serious injuries also grew from 1.5% to 1.9%. Concurrently, the share of crashes with no reported injuries fell from 76.1% to 74.7%.

Severity is per crash event (most severe injury). 19 fatal crash events resulted in 20 persons killed.

Outcome by Severity (Crash Events)

Fatal19fatal crashes0.6%
↑11.8%prior 17
Serious Injury57serious injury crashes1.9%
↓-3.4%prior 59
Minor Injury354minor injury crashes11.6%
↓-24.7%prior 470
Possible Injury344possible injury crashes11.3%
↓-16.5%prior 412
No Injury2,282no injury crashes74.7%
↓-25.4%prior 3,057

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

The distribution of crashes by environmental conditions remained largely consistent between the two periods. The majority of incidents in both years occurred in clear weather and on dry road surfaces. Crashes in daylight accounted for 68.2% of incidents in the current period, a slight decrease from 70.1% in the prior period. There were no major shifts in the proportion of crashes occurring in adverse weather or lighting conditions.

Weather

Clear2,381 (78.4%)
↓-21.8%prior 3,043
Rain283 (9.3%)
↓-23.7%prior 371
Cloudy205 (6.8%)
↓-28.3%prior 286
Snow105 (3.5%)
↓-34.0%prior 159
Blowing Snow20 (0.7%)
↓-9.1%prior 22
Fog, Smog, Smoke17 (0.6%)
↓-5.6%prior 18
Freezing Rain or Freezing Drizzle16 (0.5%)
↓-76.5%prior 68
Other4 (0.1%)
Severe Crosswinds4 (0.1%)
↓-33.3%prior 6
Sleet or Hail1 (0.0%)
↓-95.5%prior 22

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

Lighting

Daylight2,083 (68.7%)
↓-26.0%prior 2,814
Dark-Not Lighted461 (15.2%)
↓-19.1%prior 570
Dark-Lighted394 (13.0%)
↓-18.1%prior 481
Dusk43 (1.4%)
↓-45.6%prior 79
Dawn30 (1.0%)
↓-18.9%prior 37
Dark-Unknown Lighting19 (0.6%)
↑5.6%prior 18
Other1 (0.0%)

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

Road Surface

Dry2,363 (77.6%)
↓-20.6%prior 2,976
Wet483 (15.9%)
↓-21.3%prior 614
Snow92 (3.0%)
↓-49.7%prior 183
Ice / Frost45 (1.5%)
↓-58.3%prior 108
Slush32 (1.1%)
↓-59.0%prior 78
Mud, Dirt, Gravel21 (0.7%)
↓-12.5%prior 24
Other3 (0.1%)
↓-62.5%prior 8
Sand2 (0.1%)
↓-71.4%prior 7
Oil1 (0.0%)
Moving Water1 (0.0%)

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

Vehicles & Demographics

Ford, Chevrolet, and Honda were the top three vehicle makes involved in crashes in 2020, with Ford leading at 551 vehicles. This represents a change from 2019, when the top three were Ford (791), Toyota (506), and Chevrolet (453). The age distribution of persons involved in collisions remained stable, with the 26-34 age group being the largest cohort in both years and no significant proportional shifts observed among other age groups.

Top Vehicle Makes (5,113 vehicles)

1
FORD551 (10.8%)
↓-30.3%prior 791
2
CHEVROLET414 (8.1%)
↓-8.6%prior 453
3
HONDA411 (8%)
↓-7.0%prior 442
4
TOYOTA355 (6.9%)
↓-29.8%prior 506
5
SUBARU321 (6.3%)
↓-27.4%prior 442
6
JEEP272 (5.3%)
↓-30.8%prior 393
7
NISSAN264 (5.2%)
↓-19.5%prior 328
8
HYUNDAI171 (3.3%)
↓-21.6%prior 218
9
HOND131 (2.6%)
↓-36.7%prior 207
10
GMC126 (2.5%)
↓-19.2%prior 156

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

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

Sex Distribution (6,031 persons with recorded sex)

Male3,533 (58.6%)
↓-22.0%prior 4,528
Female2,497 (41.4%)
↓-30.2%prior 3,577
01 (0.0%)
→0.0%prior 1

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

Speed Limit Zones

Crashes decreased across all speed zones year-over-year, with the majority occurring in zones posted at 35 mph or less in both periods. While the number of crashes on roads with a 55 mph speed limit was low, the fatal crash rate in this zone increased notably from 2.7% (1 of 37 crashes) in 2019 to 8.0% (2 of 25 crashes) in 2020. Fatal crashes also occurred at a high rate (3.18%) in 50 mph zones in the current period, where none were recorded in the prior year.

Fatal crashes by zone: 25 mph: 4 of 715 (0.559%) · 30 mph: 2 of 403 (0.496%) · 35 mph: 2 of 451 (0.443%) · 40 mph: 2 of 361 (0.554%) · 45 mph: 3 of 266 (1.128%) · 50 mph: 2 of 63 (3.175%) · 55 mph: 2 of 25 (8%) · 65 mph: 1 of 162 (0.617%) · 99 mph: 1 of 32 (3.125%)

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: September 10, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: connecticut, CT
  • Total crash records analyzed: 3,056
  • Total persons involved: 6,629
  • Total vehicles involved: 5,113

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 September 10, 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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