SponsoredThatCarHitMe.com

If you're a data point in this report, call us.

We'll evaluate whether you have a case. Free, and no pressure.

(888) 988-8341Free for accident victims

Yearly Traffic Safety Analysis

23,323 CRASHES IN
CONNECTICUT, CT
2020

All metrics benchmarked against2019

In Fairfield County, total traffic crashes decreased from 33,043 in 2019 to 23,323 in 2020, a reduction of 29.4%. Despite this significant drop in overall collisions, the number of fatalities recorded rose sharply from 33 to 58, representing a 75.8% year-over-year increase. The total number of injuries also declined by 26.4% from 9,786 to 7,199.

23,323

▼ -29.4%was 33,043

Total Crash Events

▲ 75.8%was 33

Persons Killed

7,199

▼ -26.4%was 9,786

Persons Injured

2,881

▼ -11.9%was 3,269

Hit-and-Run Crashes

Note: "Persons Killed" (58) counts individual fatalities across all crash events. "Fatal" in the severity table below (56) 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

Overall crash trends in Fairfield County show a substantial year-over-year decline in volume. Total crashes dropped by 29.4%, from 33,043 in 2019 to 23,323 in 2020, and total injuries fell by 26.4%. In contrast to this downward trend, fatalities increased by 75.8% during the same period, rising from 33 to 58.

2,881

Hit-and-Run Crashes — 2020

▼ -11.9% vs prior (3,269)

While the absolute number of hit-and-run crashes in Fairfield County decreased from 3,269 in 2019 to 2,881 in 2020, the hit-and-run rate increased. Hit-and-runs constituted 12.4% of all crashes in 2020, up from 9.9% in the prior year. This indicates that a larger proportion of the collisions that occurred in 2020 involved a driver leaving the scene.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 14↓-14.3%

Cyclists Killed

Prior: 0→%

Motorists Killed

Prior: 19↑136.8%

305

Pedestrians Injured

Prior: 456↓-33.1%

Cyclists Injured

Prior: 96↓-19.8%

6,817

Motorists Injured

Prior: 9,234↓-26.2%

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 year-over-year, though with a lower volume of incidents in 2020. Friday was the peak day for crashes in both 2020 (3,966 crashes) and 2019 (5,587 crashes). Similarly, the 5 p.m. hour was the peak time for collisions in both periods, with 1,930 crashes in 2020 compared to 2,886 in 2019. While the peak times did not shift, there was a notable reduction in crashes during morning and evening commute hours 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

While total crashes decreased, the severity of those crashes increased from 2019 to 2020. The number of fatal crashes rose from 30 to 56, and the fatal crash rate per 100 crashes more than doubled from 0.09 to 0.24. The proportion of crashes resulting in serious injuries increased from 0.9% to 1.1% of all collisions. Correspondingly, the share of crashes with no reported injuries decreased from 78.1% in 2019 to 77.3% in 2020.

Severity is per crash event (most severe injury). 56 fatal crash events resulted in 58 persons killed.

Outcome by Severity (Crash Events)

Fatal56fatal crashes0.2%

↑86.7%prior 30

Serious Injury259serious injury crashes1.1%

↓-15.4%prior 306

Minor Injury2,258minor injury crashes9.7%

↓-16.6%prior 2,709

Possible Injury2,724possible injury crashes11.7%

↓-35.2%prior 4,205

No Injury18,026no injury crashes77.3%

↓-30.1%prior 25,793

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 largely stable between 2019 and 2020, with no significant shifts in the driving environment. In 2020, 68.5% of crashes occurred in daylight, compared to 71.2% in 2019. Crashes in clear weather accounted for 81.6% of the total in 2020, a slight increase from 80.1% in the prior year. Similarly, the proportion of collisions on dry road surfaces was consistent, making up 81.7% of crashes in 2020 versus 80.5% in 2019.

Weather

Clear19,042 (82.3%)

↓-28.1%prior 26,471

Rain2,172 (9.4%)

↓-39.0%prior 3,559

Cloudy1,313 (5.7%)

↓-25.4%prior 1,761

Snow348 (1.5%)

↓-37.5%prior 557

Freezing Rain or Freezing Drizzle106 (0.5%)

↓-53.7%prior 229

Fog, Smog, Smoke63 (0.3%)

↑10.5%prior 57

Blowing Snow63 (0.3%)

↓-4.5%prior 66

Severe Crosswinds18 (0.1%)

↑5.9%prior 17

Other10 (0.0%)

↓-56.5%prior 23

Sleet or Hail9 (0.0%)

↓-85.7%prior 63

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

Lighting

Daylight15,970 (69.2%)

↓-32.1%prior 23,535

Dark-Lighted5,019 (21.8%)

↓-22.2%prior 6,450

Dark-Not Lighted1,391 (6.0%)

↓-24.5%prior 1,843

Dusk376 (1.6%)

↓-18.4%prior 461

Dark-Unknown Lighting139 (0.6%)

↓-17.3%prior 168

Dawn137 (0.6%)

↓-37.4%prior 219

Other39 (0.2%)

↑5.4%prior 37

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

Road Surface

Dry19,063 (82.3%)

↓-28.4%prior 26,615

Wet3,450 (14.9%)

↓-32.4%prior 5,102

Snow380 (1.6%)

↓-24.3%prior 502

Ice / Frost139 (0.6%)

↓-58.4%prior 334

Slush60 (0.3%)

↓-67.6%prior 185

Moving Water16 (0.1%)

→0.0%prior 16

Other14 (0.1%)

↓-6.7%prior 15

Standing Water14 (0.1%)

↑55.6%prior 9

Mud, Dirt, Gravel14 (0.1%)

↓-17.6%prior 17

Sand5 (0.0%)

↓-50.0%prior 10

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

Vehicles & Demographics

The composition of vehicles and persons involved in crashes showed little change year-over-year. The top five vehicle makes involved in collisions were identical in both periods: Honda, Toyota, Ford, Nissan, and Chevrolet, with their rank order remaining unchanged. The age distribution of persons involved in crashes was also stable; for instance, the 26-34 age group represented 17.4% of individuals in 2020 compared to 16.6% in 2019, showing no significant demographic shift.

Top Vehicle Makes (44,763 vehicles)

HONDA5,331 (11.9%)

↓-28.3%prior 7,437

TOYOTA4,757 (10.6%)

↓-30.9%prior 6,886

FORD3,888 (8.7%)

↓-32.0%prior 5,714

NISSAN3,821 (8.5%)

↓-27.8%prior 5,290

CHEVROLET2,479 (5.5%)

↓-30.5%prior 3,567

JEEP2,097 (4.7%)

↓-29.8%prior 2,986

SUBARU1,851 (4.1%)

↓-30.5%prior 2,663

HYUNDAI1,552 (3.5%)

↓-24.0%prior 2,043

BMW1,340 (3%)

↓-29.5%prior 1,901

DODGE962 (2.1%)

↓-24.2%prior 1,269

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

3,689 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (50,557 persons with recorded sex)

Male29,650 (58.6%)

↓-29.9%prior 42,277

Female20,907 (41.4%)

↓-37.7%prior 33,578

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

Speed Limit Zones

While the overall distribution of crashes across different speed zones was similar year-over-year, there was a notable increase in the number and rate of fatal crashes in higher speed zones. In 55 mph zones, fatal crashes more than doubled from 9 in 2019 to 20 in 2020, with the fatality rate for that zone increasing from 0.171 to 0.614 per 100 crashes. Fatal crashes in 25 mph zones also nearly doubled from 8 to 15. The proportion of total crashes occurring in 55 mph zones decreased slightly from 16.0% to 13.9%.

Fatal crashes by zone: 1 mph: 3 of 4,170 (0.072%) · 25 mph: 15 of 8,702 (0.172%) · 30 mph: 5 of 1,589 (0.315%) · 35 mph: 6 of 1,829 (0.328%) · 40 mph: 2 of 804 (0.249%) · 45 mph: 1 of 175 (0.571%) · 55 mph: 20 of 3,255 (0.614%) · 65 mph: 3 of 228 (1.316%)

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: 23,323
Total persons involved: 54,181
Total vehicles involved: 44,763

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

ThatCarHitMe.com · An Injuria.ai Company