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Yearly Traffic Safety Analysis

2,930 CRASHES IN
CONNECTICUT, CT
2020

All metrics benchmarked against2019

In Middlesex County, total vehicle crashes decreased by 21% from 3,710 in 2019 to 2,930 in 2020. Despite this significant drop in overall collisions, the number of fatalities increased from 13 to 18, and the number of fatal crashes rose from 11 to 16 over the same period.

2,930

▼ -21.0%was 3,710

Total Crash Events

▲ 38.5%was 13

Persons Killed

879

▼ -25.0%was 1,172

Persons Injured

265

▼ -4.3%was 277

Hit-and-Run Crashes

Note: "Persons Killed" (18) 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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

While the overall trend shows a substantial year-over-year decrease in total crashes and injuries, the severity of crashes worsened. The number of fatalities increased by 38.5% from 13 to 18, and fatal crashes increased by 45.5% from 11 to 16, running counter to the general downward trend in collision volume.

265

Hit-and-Run Crashes — 2020

▼ -4.3% vs prior (277)

The total number of hit-and-run crashes saw a slight decrease from 277 in 2019 to 265 in 2020. However, due to the overall reduction in total crashes, the hit-and-run rate increased. Hit-and-runs constituted 9.0% of all crashes in 2020, up from 7.5% in the prior year.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 1→0.0%

Cyclists Killed

Prior: 1→0.0%

Motorists Killed

Prior: 11↑45.5%

Other Killed

Prior: 0→0.0%

Pedestrians Injured

Prior: 31↓-38.7%

Cyclists Injured

Prior: 17↓-64.7%

853

Motorists Injured

Prior: 1,122↓-24.0%

Other Injured

Prior: 2↓-50.0%

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 remained consistent between the two years, though at a lower volume in 2020. Friday was the peak day for crashes in both 2019 (676 crashes) and 2020 (506 crashes). Similarly, the 3 p.m. hour was the peak time for collisions in both periods, accounting for 347 crashes in 2019 and 269 in 2020.

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

The severity of crashes increased from 2019 to 2020. The proportion of fatal crashes rose from 0.3% of all incidents in 2019 to 0.5% in 2020. While total injuries fell from 1,172 to 879, the share of crashes resulting in serious injuries also saw a slight increase from 1.0% to 1.3%. Conversely, the proportion of crashes with possible injuries decreased from 11.2% to 9.9%.

Severity is per crash event (most severe injury). 16 fatal crash events resulted in 18 persons killed.

Outcome by Severity (Crash Events)

Fatal16fatal crashes0.5%

↑45.5%prior 11

Serious Injury37serious injury crashes1.3%

↑2.8%prior 36

Minor Injury335minor injury crashes11.4%

↓-19.7%prior 417

Possible Injury290possible injury crashes9.9%

↓-30.1%prior 415

No Injury2,252no injury crashes76.9%

↓-20.5%prior 2,831

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

Crash conditions remained broadly similar year-over-year, with no significant shifts toward more adverse conditions. In both 2020 and 2019, the majority of crashes occurred in clear weather (80.3% and 78.0%, respectively) and on dry road surfaces (79.4% and 76.7%, respectively). Daylight hours continued to account for the largest share of incidents, representing 71.3% of crashes in 2020 and 72.8% in 2019.

Weather

Clear2,352 (81.0%)

↓-18.7%prior 2,894

Rain308 (10.6%)

↓-24.9%prior 410

Cloudy156 (5.4%)

↓-17.5%prior 189

Snow53 (1.8%)

↓-17.2%prior 64

Fog, Smog, Smoke12 (0.4%)

↑20.0%prior 10

Blowing Snow9 (0.3%)

↓-55.0%prior 20

Freezing Rain or Freezing Drizzle9 (0.3%)

↓-86.4%prior 66

Other3 (0.1%)

↓-57.1%prior 7

Severe Crosswinds2 (0.1%)

↓-60.0%prior 5

Sleet or Hail1 (0.0%)

↓-91.7%prior 12

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

Lighting

Daylight2,090 (72.0%)

↓-22.6%prior 2,701

Dark-Lighted453 (15.6%)

↓-15.2%prior 534

Dark-Not Lighted253 (8.7%)

↓-21.9%prior 324

Dusk63 (2.2%)

↑1.6%prior 62

Dawn32 (1.1%)

↓-5.9%prior 34

Dark-Unknown Lighting6 (0.2%)

↓-50.0%prior 12

Other4 (0.1%)

↓-42.9%prior 7

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

Road Surface

Dry2,327 (79.9%)

↓-18.3%prior 2,847

Wet471 (16.2%)

↓-22.9%prior 611

Snow52 (1.8%)

↓-21.2%prior 66

Ice / Frost34 (1.2%)

↓-62.2%prior 90

Mud, Dirt, Gravel13 (0.4%)

↑85.7%prior 7

Slush9 (0.3%)

↓-80.4%prior 46

Standing Water2 (0.1%)

Other2 (0.1%)

Moving Water1 (0.0%)

Sand1 (0.0%)

↓-80.0%prior 5

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

Vehicles & Demographics

The makes of vehicles involved in crashes showed a consistent pattern, with Toyota, Ford, Honda, Nissan, and Chevrolet ranking as the top five in both 2019 and 2020. The age demographics of persons involved in crashes also remained stable. The 26-34 age group was the largest single cohort in both years, accounting for 16.0% of persons in 2020 and 16.2% in 2019.

Top Vehicle Makes (5,227 vehicles)

TOYOTA535 (10.2%)

↓-23.1%prior 696

FORD526 (10.1%)

↓-22.8%prior 681

HONDA480 (9.2%)

↓-18.6%prior 590

NISSAN415 (7.9%)

↓-19.6%prior 516

CHEVROLET361 (6.9%)

↓-11.5%prior 408

SUBARU282 (5.4%)

↓-20.6%prior 355

JEEP254 (4.9%)

↓-2.7%prior 261

HYUNDAI161 (3.1%)

↓-25.1%prior 215

DODGE152 (2.9%)

↓-23.6%prior 199

KIA111 (2.1%)

↓-20.1%prior 139

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

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

Sex Distribution (6,294 persons with recorded sex)

Male3,575 (56.8%)

↓-21.0%prior 4,523

Female2,719 (43.2%)

↓-23.9%prior 3,571

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 in 2020 shifted slightly toward lower speed zones, with 52.6% of incidents occurring in zones of 35 mph or less, compared to 50.6% in 2019. However, the fatal crash rate increased in some higher speed zones. In 65 mph zones, the fatal crash rate grew from 0.67% in 2019 to 1.09% in 2020, and in 45 mph zones, the rate increased from 0.66% to 1.08%.

Fatal crashes by zone: 1 mph: 1 of 199 (0.503%) · 25 mph: 1 of 507 (0.197%) · 30 mph: 1 of 219 (0.457%) · 35 mph: 3 of 540 (0.556%) · 40 mph: 2 of 312 (0.641%) · 45 mph: 3 of 277 (1.083%) · 65 mph: 4 of 368 (1.087%) · 88 mph: 1 of 362 (0.276%)

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 20, 2026

Data Coverage

Reporting period: 2020-01-01 through 2020-12-31 (366 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 2,930
Total persons involved: 6,658
Total vehicles involved: 5,227

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 20, 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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