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Yearly Traffic Safety Analysis

2,432 CRASHES IN
CONNECTICUT, CT
2021

All metrics benchmarked against2020

In 2021, Tolland County recorded 2,432 total crashes, a 22.5% increase from the 1,986 crashes documented in 2020. This rise in collisions was accompanied by a similar 22.5% increase in persons injured. The most notable year-over-year shift was a 27.3% decrease in total fatalities, which fell from 22 to 16 despite the overall increase in crashes.

2,432

22.5%was 1,986

Total Crash Events

16

-27.3%was 22

Persons Killed

876

22.5%was 715

Persons Injured

237

32.4%was 179

Hit-and-Run Crashes

Note: "Persons Killed" (16) counts individual fatalities across all crash events. "Fatal" in the severity table below (16) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2021-01-01 to 2021-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crash trends in Tolland County show a significant increase year-over-year. Total collisions rose by 22.5%, from 1,986 in 2020 to 2,432 in 2021. This increase was mirrored by a 22.5% rise in total injuries, which grew from 715 to 876.

237

Hit-and-Run Crashes — 2021

32.4% vs prior (179)

Hit-and-run crashes increased both in absolute numbers and as a proportion of total collisions. The count of hit-and-run incidents rose by 32.4% from 179 in 2020 to 237 in 2021. Consequently, the hit-and-run rate trended upward, increasing from 9.0% to 9.7% of all crashes.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 20.0%

0

Cyclists Killed

Prior: 00.0%

14

Motorists Killed

Prior: 20-30.0%

19

Pedestrians Injured

Prior: 1618.8%

7

Cyclists Injured

Prior: 70.0%

850

Motorists Injured

Prior: 69222.8%

Source: Connecticut Crash Data · Csv Open Data · 2021-01-01 to 2021-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Peak crash times remained consistent year-over-year, with Friday serving as the peak day and 5 PM as the peak hour in both periods. However, the distribution of the increase was uneven; crashes on weekdays (Monday-Friday) grew by 29.4%, while weekend (Saturday-Sunday) crashes increased by a much smaller margin of 4.7%.

Source: Connecticut Crash Data · Csv Open Data · 2021-01-01 to 2021-12-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2021-01-01 to 2021-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes increased, the rate of the most severe outcomes decreased. The fatal crash rate fell from 1.06 per 100 crashes in 2020 to 0.66 in 2021. The overall proportion of crashes resulting in any injury held steady at 26.6% for both years, though 2021 saw a slight proportional increase in crashes involving serious injuries (1.6% vs. 1.3%) and minor injuries (15.6% vs. 14.1%).

Outcome by Severity (Crash Events)

Fatal16fatal crashes0.7%
-23.8%prior 21
Serious Injury38serious injury crashes1.6%
46.2%prior 26
Minor Injury380minor injury crashes15.6%
35.2%prior 281
Possible Injury229possible injury crashes9.4%
3.2%prior 222
No Injury1,769no injury crashes72.7%
23.2%prior 1,436

Source: Connecticut Crash Data · Csv Open Data · 2021-01-01 to 2021-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2021-01-01 to 2021-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The distribution of crashes across different environmental conditions showed little change between 2020 and 2021. In both years, approximately 80% of crashes occurred in clear weather, and about 65-66% happened during daylight hours. Similarly, crashes on dry road surfaces accounted for a consistent 77-78% of the total, indicating that the year-over-year increase in crashes was not driven by a shift in adverse conditions.

Weather

Clear1,947 (80.3%)
24.6%prior 1,563
Rain229 (9.4%)
15.7%prior 198
Snow101 (4.2%)
27.8%prior 79
Cloudy100 (4.1%)
29.9%prior 77
Freezing Rain or Freezing Drizzle19 (0.8%)
35.7%prior 14
Fog, Smog, Smoke13 (0.5%)
-27.8%prior 18
Blowing Snow10 (0.4%)
-33.3%prior 15
Other3 (0.1%)
Severe Crosswinds2 (0.1%)
-60.0%prior 5

Source: Connecticut Crash Data · Csv Open Data · 2021-01-01 to 2021-12-31 · Weather condition at time of crash

Lighting

Daylight1,605 (66.3%)
24.4%prior 1,290
Dark-Lighted396 (16.4%)
22.6%prior 323
Dark-Not Lighted336 (13.9%)
8.0%prior 311
Dusk49 (2.0%)
75.0%prior 28
Dawn23 (0.9%)
130.0%prior 10
Dark-Unknown Lighting12 (0.5%)
140.0%prior 5
Other1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2021-01-01 to 2021-12-31 · Lighting condition field

Road Surface

Dry1,904 (78.5%)
24.5%prior 1,529
Wet367 (15.1%)
11.6%prior 329
Snow89 (3.7%)
29.0%prior 69
Ice / Frost29 (1.2%)
38.1%prior 21
Slush21 (0.9%)
50.0%prior 14
Mud, Dirt, Gravel10 (0.4%)
42.9%prior 7
Standing Water3 (0.1%)
Other1 (0.0%)
Moving Water1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2021-01-01 to 2021-12-31 · Road surface condition field

Vehicles & Demographics

The top five vehicle makes involved in crashes were Ford, Toyota, Honda, Nissan, and Chevrolet in both years, with their rankings remaining largely stable. An analysis of the persons involved in collisions reveals a demographic shift in age distribution. The 16-20 age group's representation increased from 13.2% of all persons involved in 2020 to 15.1% in 2021.

Top Vehicle Makes (4,285 vehicles)

1
FORD446 (10.4%)
14.1%prior 391
2
TOYOTA426 (9.9%)
41.5%prior 301
3
HONDA421 (9.8%)
30.7%prior 322
4
NISSAN291 (6.8%)
26.0%prior 231
5
CHEVROLET270 (6.3%)
18.4%prior 228
6
SUBARU251 (5.9%)
49.4%prior 168
7
HYUNDAI184 (4.3%)
37.3%prior 134
8
JEEP179 (4.2%)
16.2%prior 154
9
VOLKSWAGEN119 (2.8%)
72.5%prior 69
10
DODGE112 (2.6%)
24.4%prior 90

Source: Connecticut Crash Data · Csv Open Data · 2021-01-01 to 2021-12-31 · Vehicle unit records

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

Sex Distribution (5,444 persons with recorded sex)

Male3,108 (57.1%)
26.2%prior 2,462
Female2,336 (42.9%)
24.2%prior 1,881

Source: Connecticut Crash Data · Csv Open Data · 2021-01-01 to 2021-12-31 · Person-level records linked to crash events

Speed Limit Zones

The year-over-year increase in crashes was most pronounced in zones with posted speed limits of 35 mph or less, which saw a 25.4% rise in collisions. A significant change occurred in the 50 mph speed zone, where crashes more than doubled from 21 to 50, and fatalities jumped from zero to 5, giving it a 10% fatal crash rate in 2021. In the prior year, fatal crashes were more concentrated in the 40 and 45 mph zones.

Fatal crashes by zone: 1 mph: 1 of 149 (0.671%) · 30 mph: 3 of 375 (0.8%) · 35 mph: 1 of 605 (0.165%) · 40 mph: 1 of 322 (0.311%) · 45 mph: 3 of 289 (1.038%) · 50 mph: 5 of 50 (10%) · 65 mph: 2 of 216 (0.926%)

Source: Connecticut Crash Data · Csv Open Data · 2021-01-01 to 2021-12-31 · 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: 2021-01-01 through 2021-12-31
  • Report generated: August 21, 2026

Data Coverage

  • Reporting period: 2021-01-01 through 2021-12-31 (365 days)
  • Geographic scope: connecticut, CT
  • Total crash records analyzed: 2,432
  • Total persons involved: 5,722
  • Total vehicles involved: 4,285

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: 2021." Published August 21, 2026. Reporting period: 2021-01-01 to 2021-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2021-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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