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Monthly Traffic Safety Analysis

6,815 CRASHES IN
CONNECTICUT, CT
MARCH 2021

All metrics benchmarked againstMarch 2020

In March 2021, Connecticut recorded 6,815 total vehicle crashes, a 12.3% increase from the 6,068 crashes documented in March 2020. This rise was accompanied by a 21.6% increase in injuries, from 1,925 to 2,341. However, total fatalities saw a notable year-over-year decrease of 31.8%, falling from 22 to 15.

6,815

12.3%was 6,068

Total Crash Events

15

-31.8%was 22

Persons Killed

2,341

21.6%was 1,925

Persons Injured

950

12.6%was 844

Hit-and-Run Crashes

Note: "Persons Killed" (15) counts individual fatalities across all crash events. "Fatal" in the severity table below (15) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2021-03-01 to 2021-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crash trends for March 2021 show a significant increase in volume compared to the same month in the prior year. Total crashes rose by 12.3% (from 6,068 to 6,815) and total injuries increased by 21.6% (from 1,925 to 2,341). In contrast, the number of fatalities decreased from 22 to 15.

950

Hit-and-Run Crashes — March 2021

12.6% vs prior (844)

The total number of hit-and-run crashes increased by 12.6%, from 844 in March 2020 to 950 in March 2021. Despite this rise in absolute numbers, the hit-and-run rate remained unchanged at 13.9% for both periods. This indicates that the growth in hit-and-run incidents was directly proportional to the overall increase in total crashes.

Vulnerable Road User Casualties

3

Pedestrians Killed

Prior: 4-25.0%

0

Cyclists Killed

Prior: 00.0%

12

Motorists Killed

Prior: 18-33.3%

77

Pedestrians Injured

Prior: 6518.5%

10

Cyclists Injured

Prior: 12-16.7%

2,254

Motorists Injured

Prior: 1,84722.0%

Source: Connecticut Crash Data · Csv Open Data · 2021-03-01 to 2021-03-31 · 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 slightly year-over-year. The peak day for crashes moved from Monday (1,133 crashes) in the prior period to Tuesday (1,173 crashes) in the current period. The peak hour for collisions remained consistent at 3 p.m. in both periods, though the crash volume during this hour increased from 531 to 592.

Source: Connecticut Crash Data · Csv Open Data · 2021-03-01 to 2021-03-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2021-03-01 to 2021-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes increased, the fatal crash rate decreased from 0.33% to 0.22% year-over-year. In March 2021, there were 15 fatal crashes, down from 20 in March 2020. Conversely, the proportion of crashes resulting in injury saw an uptick, with serious injury crashes rising from 1.0% to 1.3% of all collisions and minor injury crashes increasing from 10.0% to 11.1%.

Outcome by Severity (Crash Events)

Fatal15fatal crashes0.2%
-25.0%prior 20
Serious Injury87serious injury crashes1.3%
40.3%prior 62
Minor Injury757minor injury crashes11.1%
24.7%prior 607
Possible Injury842possible injury crashes12.4%
11.1%prior 758
No Injury5,114no injury crashes75%
10.7%prior 4,621

Source: Connecticut Crash Data · Csv Open Data · 2021-03-01 to 2021-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2021-03-01 to 2021-03-31 · Most severe injury per crash record

Road & Environmental Conditions

Crashes in March 2021 were more likely to occur in clear conditions compared to the prior year. The proportion of crashes on dry road surfaces increased from 82.0% to 89.9% year-over-year, while crashes on wet roads decreased from 14.3% to 8.8% of the total. Similarly, collisions in clear weather accounted for 88.2% of all crashes, up from 80.7% in the previous year.

Weather

Clear6,009 (88.8%)
22.8%prior 4,894
Rain457 (6.8%)
-25.1%prior 610
Cloudy217 (3.2%)
-35.2%prior 335
Severe Crosswinds21 (0.3%)
Fog, Smog, Smoke18 (0.3%)
Snow17 (0.3%)
-85.6%prior 118
Freezing Rain or Freezing Drizzle10 (0.1%)
-78.7%prior 47
Other9 (0.1%)
Blowing Snow7 (0.1%)
-66.7%prior 21
Sleet or Hail2 (0.0%)
-71.4%prior 7

Source: Connecticut Crash Data · Csv Open Data · 2021-03-01 to 2021-03-31 · Weather condition at time of crash

Lighting

Daylight4,863 (72.0%)
14.5%prior 4,246
Dark-Lighted1,346 (19.9%)
17.2%prior 1,148
Dark-Not Lighted375 (5.6%)
-7.9%prior 407
Dusk94 (1.4%)
-13.8%prior 109
Dark-Unknown Lighting43 (0.6%)
2.4%prior 42
Dawn26 (0.4%)
-29.7%prior 37
Other6 (0.1%)
-60.0%prior 15

Source: Connecticut Crash Data · Csv Open Data · 2021-03-01 to 2021-03-31 · Lighting condition field

Road Surface

Dry6,124 (90.4%)
23.0%prior 4,978
Wet601 (8.9%)
-30.8%prior 868
Ice / Frost17 (0.3%)
-75.0%prior 68
Sand8 (0.1%)
Snow7 (0.1%)
-88.1%prior 59
Mud, Dirt, Gravel6 (0.1%)
0.0%prior 6
Other4 (0.1%)
Standing Water2 (0.0%)
Moving Water1 (0.0%)
Slush1 (0.0%)
-98.0%prior 51

Source: Connecticut Crash Data · Csv Open Data · 2021-03-01 to 2021-03-31 · Road surface condition field

Vehicles & Demographics

The top three vehicle makes involved in crashes—Honda, Toyota, and Ford—remained the same across both periods, with each make seeing an increase in total incidents. The age distribution of persons involved in crashes also remained largely consistent, with all age groups seeing an increase in raw numbers that was proportional to the overall rise in crash participants. No significant shifts were observed in the demographic makeup of individuals involved in collisions.

Top Vehicle Makes (12,834 vehicles)

1
HONDA1,512 (11.8%)
27.2%prior 1,189
2
TOYOTA1,273 (9.9%)
16.7%prior 1,091
3
FORD1,083 (8.4%)
6.3%prior 1,019
4
NISSAN1,033 (8%)
11.6%prior 926
5
CHEVROLET809 (6.3%)
29.9%prior 623
6
JEEP571 (4.4%)
28.6%prior 444
7
HYUNDAI552 (4.3%)
35.6%prior 407
8
SUBARU552 (4.3%)
22.1%prior 452
9
ACURA284 (2.2%)
17.4%prior 242
10
DODGE284 (2.2%)
15.0%prior 247

Source: Connecticut Crash Data · Csv Open Data · 2021-03-01 to 2021-03-31 · Vehicle unit records

1,285 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (14,994 persons with recorded sex)

Male8,496 (56.7%)
11.9%prior 7,591
Female6,498 (43.3%)
19.5%prior 5,438

Source: Connecticut Crash Data · Csv Open Data · 2021-03-01 to 2021-03-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across different speed zones remained stable between the two periods. However, the profile of fatal crashes shifted toward higher speed roads. In March 2021, 66.7% of all fatalities (10 of 15) occurred in zones with speed limits of 40 mph or higher. This is a notable increase from March 2020, when 45.5% of fatalities (10 of 22) occurred in these same higher-speed zones.

Fatal crashes by zone: 1 mph: 1 of 888 (0.113%) · 20 mph: 1 of 39 (2.564%) · 25 mph: 1 of 2,180 (0.046%) · 35 mph: 2 of 803 (0.249%) · 40 mph: 3 of 376 (0.798%) · 45 mph: 4 of 263 (1.521%) · 55 mph: 3 of 536 (0.56%)

Source: Connecticut Crash Data · Csv Open Data · 2021-03-01 to 2021-03-31 · 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: 2021-03-01 through 2021-03-31
  • Report generated: August 20, 2026

Data Coverage

  • Reporting period: 2021-03-01 through 2021-03-31 (31 days)
  • Geographic scope: connecticut, CT
  • Total crash records analyzed: 6,815
  • Total persons involved: 16,235
  • Total vehicles involved: 12,834

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: March 2021." Published August 20, 2026. Reporting period: 2021-03-01 to 2021-03-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/march-2021-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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