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Monthly Traffic Safety Analysis

8,921 CRASHES IN
CONNECTICUT, CT
JANUARY 2020

All metrics benchmarked againstJanuary 2019

In January 2020, Connecticut recorded 8,921 total traffic crashes, a 2.7% decrease from the 9,166 crashes reported in January 2019. While overall crashes and injuries saw a minor decline, the most notable year-over-year shift was a 15% increase in fatalities, which rose from 20 to 23.

8,921

-2.7%was 9,166

Total Crash Events

23

15.0%was 20

Persons Killed

2,702

-1.0%was 2,730

Persons Injured

1,120

7.2%was 1,045

Hit-and-Run Crashes

Note: "Persons Killed" (23) counts individual fatalities across all crash events. "Fatal" in the severity table below (23) 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 2020-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends showed a slight decrease in volume year-over-year. Total collisions fell by 2.7% from 9,166 in January 2019 to 8,921 in January 2020, and total injuries declined by 1.0% from 2,730 to 2,702. In contrast to this trend, traffic fatalities increased by 15%, rising from 20 to 23.

1,120

Hit-and-Run Crashes — January 2020

7.2% vs prior (1,045)

Hit-and-run crashes increased both in total count and as a percentage of all collisions. The number of hit-and-run incidents rose by 7.2%, from 1,045 in January 2019 to 1,120 in January 2020. This pushed the hit-and-run rate up from 11.4% to 12.6% of all crashes year-over-year.

Vulnerable Road User Casualties

8

Pedestrians Killed

Prior: 714.3%

0

Cyclists Killed

Prior: 00.0%

15

Motorists Killed

Prior: 1315.4%

0

Other Killed

Prior: 00.0%

114

Pedestrians Injured

Prior: 10112.9%

15

Cyclists Injured

Prior: 150.0%

2,570

Motorists Injured

Prior: 2,613-1.6%

3

Other Injured

Prior: 1200.0%

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-01-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted slightly year-over-year. The peak day for collisions moved from Tuesday (1,601 crashes) in January 2019 to Friday (1,644 crashes) in January 2020. The 5 p.m. hour remained the peak time for crashes in both periods, though the number of incidents during this hour increased from 885 to 954.

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-01-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-01-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes declined, the fatal crash rate increased from 0.21% in January 2019 to 0.26% in January 2020. The absolute number of fatal crashes rose from 19 to 23. The proportion of all injury-related crashes (serious, minor, and possible) remained stable, accounting for 22.0% of crashes in the current period compared to 21.9% in the prior year.

Outcome by Severity (Crash Events)

Fatal23fatal crashes0.3%
21.1%prior 19
Serious Injury70serious injury crashes0.8%
-4.1%prior 73
Minor Injury757minor injury crashes8.5%
-0.7%prior 762
Possible Injury1,135possible injury crashes12.7%
-3.4%prior 1,175
No Injury6,936no injury crashes77.7%
-2.8%prior 7,137

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-01-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-01-31 · Most severe injury per crash record

Road & Environmental Conditions

The vast majority of crashes in both periods occurred in clear weather and during daylight hours. In January 2020, 78.0% of crashes occurred on dry road surfaces, a notable increase from 70.5% in January 2019. Correspondingly, crashes on roads with ice or frost saw a significant decrease, falling from 663 incidents in the prior year to 148 in the current period.

Weather

Clear7,122 (80.3%)
-1.0%prior 7,195
Snow645 (7.3%)
90.3%prior 339
Cloudy482 (5.4%)
0.6%prior 479
Rain470 (5.3%)
-36.6%prior 741
Blowing Snow67 (0.8%)
9.8%prior 61
Freezing Rain or Freezing Drizzle42 (0.5%)
-74.5%prior 165
Fog, Smog, Smoke28 (0.3%)
-54.8%prior 62
Other8 (0.1%)
-70.4%prior 27
Sleet or Hail3 (0.0%)
-88.5%prior 26
Blowing Sand, Soil, Dirt2 (0.0%)

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

Lighting

Daylight5,143 (58.1%)
-4.2%prior 5,367
Dark-Lighted2,624 (29.6%)
2.5%prior 2,560
Dark-Not Lighted766 (8.7%)
-2.3%prior 784
Dusk171 (1.9%)
-7.6%prior 185
Dawn71 (0.8%)
-29.0%prior 100
Dark-Unknown Lighting58 (0.7%)
-10.8%prior 65
Other17 (0.2%)
-19.0%prior 21

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

Road Surface

Dry6,955 (78.3%)
7.7%prior 6,460
Wet1,055 (11.9%)
-21.7%prior 1,347
Snow646 (7.3%)
59.1%prior 406
Ice / Frost148 (1.7%)
-77.7%prior 663
Slush51 (0.6%)
-72.4%prior 185
Mud, Dirt, Gravel7 (0.1%)
-22.2%prior 9
Other6 (0.1%)
-53.8%prior 13
Sand5 (0.1%)
-75.0%prior 20
Standing Water2 (0.0%)
-71.4%prior 7
Moving Water2 (0.0%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Honda (1,731 vehicles), Toyota (1,679), and Ford (1,451) leading in January 2020, a slight reordering from the previous year. The age distribution of persons involved in crashes was also stable, with the 26-34 age group representing the largest cohort in both periods, followed by the 35-44 and 45-54 age groups.

Top Vehicle Makes (16,860 vehicles)

1
HONDA1,731 (10.3%)
-0.1%prior 1,733
2
TOYOTA1,679 (10%)
-3.9%prior 1,747
3
FORD1,451 (8.6%)
-6.0%prior 1,544
4
NISSAN1,440 (8.5%)
7.1%prior 1,345
5
CHEVROLET965 (5.7%)
-0.8%prior 973
6
JEEP724 (4.3%)
2.8%prior 704
7
SUBARU702 (4.2%)
0.4%prior 699
8
HYUNDAI633 (3.8%)
-1.2%prior 641
9
KIA388 (2.3%)
26.4%prior 307
10
BMW366 (2.2%)
12.3%prior 326

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

1,616 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (20,100 persons with recorded sex)

Male11,097 (55.2%)
-2.3%prior 11,356
Female9,003 (44.8%)
-0.4%prior 9,040

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

Speed Limit Zones

Crashes were most frequent in 25 mph zones in both periods, with counts increasing slightly from 2,818 to 2,891 year-over-year. Fatal crashes were also most common in this zone, rising from 6 to 7. Notably, the fatal crash rate in the 45 mph zone increased from 0.8% to 1.4%, with 4 fatal crashes recorded out of 281 total incidents in January 2020.

Fatal crashes by zone: 25 mph: 7 of 2,891 (0.242%) · 30 mph: 1 of 801 (0.125%) · 35 mph: 3 of 1,021 (0.294%) · 40 mph: 2 of 514 (0.389%) · 45 mph: 4 of 281 (1.423%) · 50 mph: 1 of 230 (0.435%) · 55 mph: 2 of 698 (0.287%) · 65 mph: 2 of 440 (0.455%) · 88 mph: 1 of 603 (0.166%)

Source: Connecticut Crash Data · Csv Open Data · 2020-01-01 to 2020-01-31 · 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 2020-01-31
  • Report generated: August 20, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-01-31 (31 days)
  • Geographic scope: connecticut, CT
  • Total crash records analyzed: 8,921
  • Total persons involved: 21,557
  • Total vehicles involved: 16,860

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: January 2020." Published August 20, 2026. Reporting period: 2020-01-01 to 2020-01-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/january-2020-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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