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Yearly Traffic Safety Analysis

6,052 CRASHES IN
CONNECTICUT, CT
2021

All metrics benchmarked against2020

In New London County, total vehicle crashes increased from 5,228 in 2020 to 6,052 in 2021, a 15.8% rise. This increase was accompanied by a 27.3% rise in fatalities, from 22 to 28 deaths. The most notable shift was a 50% year-over-year increase in the number of crashes resulting in serious injuries, which grew from 58 to 87.

6,052

15.8%was 5,228

Total Crash Events

28

27.3%was 22

Persons Killed

1,806

10.2%was 1,639

Persons Injured

819

5.0%was 780

Hit-and-Run Crashes

Note: "Persons Killed" (28) counts individual fatalities across all crash events. "Fatal" in the severity table below (26) 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 New London County moved upward from 2020 to 2021. Total crashes increased by 15.8% (from 5,228 to 6,052), while total injuries rose by 10.2% (from 1,639 to 1,806). The number of fatalities saw the most significant percentage increase, rising 27.3% from 22 to 28 deaths year-over-year.

819

Hit-and-Run Crashes — 2021

5.0% vs prior (780)

The total number of hit-and-run incidents increased from 780 in 2020 to 819 in 2021. However, due to the larger overall increase in total crashes, the hit-and-run rate trended downward. Hit-and-runs constituted 13.5% of all crashes in 2021, a decrease from the 14.9% rate recorded in the prior year.

Vulnerable Road User Casualties

3

Pedestrians Killed

Prior: 30.0%

0

Cyclists Killed

Prior: 00.0%

25

Motorists Killed

Prior: 1931.6%

49

Pedestrians Injured

Prior: 482.1%

12

Cyclists Injured

Prior: 28-57.1%

1,745

Motorists Injured

Prior: 1,56311.6%

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

Temporal crash patterns showed some consistency and some change between the two periods. Friday remained the day with the highest number of crashes in both 2020 (853 crashes) and 2021 (1,044 crashes). However, the peak hour for collisions shifted slightly earlier, from the 4 PM hour in 2020 (459 crashes) to the 3 PM hour in 2021 (559 crashes).

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 the overall percentage of crashes that were fatal remained stable at 0.4% in both 2020 and 2021, the severity of injury crashes intensified. The proportion of crashes involving a serious injury increased from 1.1% (58 incidents) in 2020 to 1.4% (87 incidents) in 2021. Conversely, the share of crashes resulting in a 'possible injury' decreased from 10.2% to 8.2% over the same period.

Severity is per crash event (most severe injury). 26 fatal crash events resulted in 28 persons killed.

Outcome by Severity (Crash Events)

Fatal26fatal crashes0.4%
23.8%prior 21
Serious Injury87serious injury crashes1.4%
50.0%prior 58
Minor Injury747minor injury crashes12.3%
19.1%prior 627
Possible Injury494possible injury crashes8.2%
-7.7%prior 535
No Injury4,698no injury crashes77.6%
17.8%prior 3,987

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

In both 2020 and 2021, the vast majority of crashes occurred in daylight and on dry roads. The proportion of crashes in clear weather was stable at approximately 80% for both years. There was a notable increase in the number of crashes occurring in snow, which rose from 106 incidents in 2020 to 250 in 2021. Correspondingly, the proportion of crashes on wet road surfaces decreased from 15.3% in 2020 to 12.9% in 2021.

Weather

Clear4,851 (80.4%)
16.8%prior 4,152
Rain505 (8.4%)
-5.1%prior 532
Cloudy331 (5.5%)
0.6%prior 329
Snow250 (4.1%)
135.8%prior 106
Blowing Snow37 (0.6%)
131.3%prior 16
Freezing Rain or Freezing Drizzle35 (0.6%)
59.1%prior 22
Fog, Smog, Smoke14 (0.2%)
-53.3%prior 30
Other4 (0.1%)
-20.0%prior 5
Sleet or Hail3 (0.0%)
Severe Crosswinds1 (0.0%)
-83.3%prior 6

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

Lighting

Daylight4,179 (69.5%)
19.7%prior 3,492
Dark-Lighted1,059 (17.6%)
7.4%prior 986
Dark-Not Lighted588 (9.8%)
13.5%prior 518
Dusk87 (1.4%)
-3.3%prior 90
Dawn39 (0.6%)
-32.8%prior 58
Dark-Unknown Lighting35 (0.6%)
150.0%prior 14
Other25 (0.4%)
-16.7%prior 30

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

Road Surface

Dry4,879 (80.8%)
16.8%prior 4,177
Wet783 (13.0%)
-2.0%prior 799
Snow246 (4.1%)
125.7%prior 109
Ice / Frost68 (1.1%)
15.3%prior 59
Slush40 (0.7%)
42.9%prior 28
Moving Water6 (0.1%)
20.0%prior 5
Mud, Dirt, Gravel6 (0.1%)
-45.5%prior 11
Sand4 (0.1%)
Standing Water3 (0.0%)
-66.7%prior 9
Other1 (0.0%)
-88.9%prior 9

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

Vehicles & Demographics

The top three vehicle makes involved in crashes—Ford, Toyota, and Honda—remained the same in both 2020 and 2021, with all three seeing an increase in total crash involvement. An analysis of persons involved in crashes shows a slight shift in age demographics. The proportion of individuals in the 16-20 age group increased from 9.3% of all persons in 2020 to 10.7% in 2021.

Top Vehicle Makes (10,771 vehicles)

1
FORD1,330 (12.3%)
20.3%prior 1,106
2
TOYOTA753 (7%)
23.4%prior 610
3
HONDA732 (6.8%)
27.3%prior 575
4
NISSAN510 (4.7%)
14.9%prior 444
5
JEEP498 (4.6%)
21.5%prior 410
6
CHEVROLET470 (4.4%)
20.8%prior 389
7
TOYT447 (4.2%)
17.9%prior 379
8
HYUNDAI356 (3.3%)
23.2%prior 289
9
SUBARU346 (3.2%)
31.1%prior 264
10
HOND343 (3.2%)
2.4%prior 335

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

1,064 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (13,127 persons with recorded sex)

Male7,272 (55.4%)
11.6%prior 6,519
Female5,855 (44.6%)
24.4%prior 4,706

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

Speed Limit Zones

Crashes increased across most speed zones from 2020 to 2021, including a 28.1% rise in 65 mph zones (from 569 to 729 crashes). While the number of fatal crashes in 25 mph zones decreased from 6 to 1, the fatal crash rate increased in higher speed zones. Specifically, the fatality rate in 40 mph zones rose from 0.5% to 1.6%, and the rate in 45 mph zones increased from 0.8% to 1.0%.

Fatal crashes by zone: 1 mph: 1 of 183 (0.546%) · 20 mph: 2 of 41 (4.878%) · 25 mph: 1 of 2,221 (0.045%) · 30 mph: 2 of 408 (0.49%) · 35 mph: 3 of 1,037 (0.289%) · 40 mph: 4 of 244 (1.639%) · 45 mph: 6 of 601 (0.998%) · 50 mph: 2 of 99 (2.02%) · 55 mph: 1 of 142 (0.704%) · 65 mph: 4 of 729 (0.549%)

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 22, 2026

Data Coverage

  • Reporting period: 2021-01-01 through 2021-12-31 (365 days)
  • Geographic scope: connecticut, CT
  • Total crash records analyzed: 6,052
  • Total persons involved: 14,160
  • Total vehicles involved: 10,771

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 22, 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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