Yearly Traffic Safety Analysis

4,015 CRASHES IN
CONNECTICUT, CT
2019

All metrics benchmarked against2018

In Litchfield County, total crashes decreased by 4.8% from 4,216 in 2018 to 4,015 in 2019. This overall reduction in collisions was accompanied by a significant year-over-year shift in outcomes. The most notable change was a 28% decrease in total traffic fatalities, which fell from 25 in 2018 to 18 in 2019.

4,015

▼ -4.8%was 4,216

Total Crash Events

18

▼ -28.0%was 25

Persons Killed

1,237

▼ -5.0%was 1,302

Persons Injured

337

▼ -8.7%was 369

Hit-and-Run Crashes

Note: "Persons Killed" (18) counts individual fatalities across all crash events. "Fatal" in the severity table below (17) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Key traffic safety metrics in Litchfield County showed a positive trend from 2018 to 2019. Total crashes fell by 4.8% (from 4,216 to 4,015), and the number of people injured in these incidents decreased by 5.0% (from 1,302 to 1,237). Most significantly, traffic fatalities dropped by 28%, from 25 deaths in 2018 to 18 in 2019.

337

Hit-and-Run Crashes — 2019

▼ -8.7% vs prior (369)

Hit-and-run incidents trended downward in Litchfield County from 2018 to 2019. The total number of hit-and-run crashes decreased from 369 to 337. This drop in volume also resulted in a lower hit-and-run rate, which fell from 8.8% of all crashes in 2018 to 8.4% in 2019.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 1↑100.0%

1

Cyclists Killed

Prior: 0→%

15

Motorists Killed

Prior: 24↓-37.5%

19

Pedestrians Injured

Prior: 24↓-20.8%

8

Cyclists Injured

Prior: 7↑14.3%

1,210

Motorists Injured

Prior: 1,270↓-4.7%

Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed some consistency and some change year-over-year. Friday remained the peak day for crashes in both 2019 (624 crashes) and 2018 (758 crashes). However, the peak hour for collisions shifted earlier, moving from the 5 p.m. hour in 2018 (354 crashes) to the 3 p.m. hour in 2019 (356 crashes).

Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The overall severity of crashes lessened from 2018 to 2019, with the fatal crash rate declining from 0.55% to 0.42%. While the proportion of fatal crashes decreased from 0.5% to 0.4%, the distribution of non-fatal injuries shifted. The number of serious injury crashes increased from 46 to 59, and minor injury crashes rose from 450 to 470, even as crashes involving only possible injuries fell from 466 to 412.

Severity is per crash event (most severe injury). 17 fatal crash events resulted in 18 persons killed.

Outcome by Severity (Crash Events)

Fatal17fatal crashes0.4%
↓-26.1%prior 23
Serious Injury59serious injury crashes1.5%
↑28.3%prior 46
Minor Injury470minor injury crashes11.7%
↑4.4%prior 450
Possible Injury412possible injury crashes10.3%
↓-11.6%prior 466
No Injury3,057no injury crashes76.1%
↓-5.4%prior 3,231

Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Road & Environmental Conditions

Crashes in 2019 were proportionally less likely to occur in adverse conditions compared to 2018. The share of crashes happening in rain decreased from 11.2% to 9.2%, and collisions on wet roads fell from 18.4% to 15.3% of the total. Correspondingly, crashes in daylight (70.1% vs. 67.9%) and on dry roads (74.1% vs. 69.7%) made up a larger percentage of incidents in 2019 than in the prior year.

Weather

Clear3,043 (76.1%)
↑1.3%prior 3,005
Rain371 (9.3%)
↓-21.4%prior 472
Cloudy286 (7.2%)
↓-13.3%prior 330
Snow159 (4.0%)
↓-33.5%prior 239
Freezing Rain or Freezing Drizzle68 (1.7%)
↑83.8%prior 37
Sleet or Hail22 (0.6%)
↑83.3%prior 12
Blowing Snow22 (0.6%)
↓-56.9%prior 51
Fog, Smog, Smoke18 (0.5%)
↓-43.8%prior 32
Severe Crosswinds6 (0.2%)
↑20.0%prior 5
Other2 (0.1%)
↓-75.0%prior 8

Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight2,814 (70.4%)
↓-1.8%prior 2,865
Dark-Not Lighted570 (14.3%)
↓-16.2%prior 680
Dark-Lighted481 (12.0%)
↓-6.6%prior 515
Dusk79 (2.0%)
↑27.4%prior 62
Dawn37 (0.9%)
↑12.1%prior 33
Dark-Unknown Lighting18 (0.5%)
↓-14.3%prior 21

Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry2,976 (74.4%)
↑1.3%prior 2,939
Wet614 (15.4%)
↓-20.9%prior 776
Snow183 (4.6%)
↓-33.5%prior 275
Ice / Frost108 (2.7%)
↑4.9%prior 103
Slush78 (2.0%)
↑44.4%prior 54
Mud, Dirt, Gravel24 (0.6%)
↑9.1%prior 22
Other8 (0.2%)
Sand7 (0.2%)
↓-41.7%prior 12
Standing Water1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

The top five vehicle makes involved in crashes were consistent across both periods, led by Ford, Toyota, Chevrolet, Subaru, and Honda. Ford was the most common make in both 2019 (791 vehicles) and 2018 (812 vehicles). An analysis of persons involved in crashes shows a slight demographic shift, with the proportion of individuals in the 16-20 age group decreasing from 11.6% of the total in 2018 to 10.9% in 2019.

Top Vehicle Makes (6,782 vehicles)

1
FORD791 (11.7%)
↓-2.6%prior 812
2
TOYOTA506 (7.5%)
↑15.5%prior 438
3
CHEVROLET453 (6.7%)
↓-11.5%prior 512
4
SUBARU442 (6.5%)
↓-0.2%prior 443
5
HONDA442 (6.5%)
↓-6.4%prior 472
6
JEEP393 (5.8%)
↓-2.2%prior 402
7
NISSAN328 (4.8%)
↑5.8%prior 310
8
HYUNDAI218 (3.2%)
↑13.0%prior 193
9
HOND207 (3.1%)
↓-11.2%prior 233
10
DODGE187 (2.8%)
↓-4.1%prior 195

Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

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

Sex Distribution (8,106 persons with recorded sex)

Male4,528 (55.9%)
↓-8.0%prior 4,924
Female3,577 (44.1%)
↓-6.1%prior 3,810
01 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across different speed zones remained largely unchanged, though there was a minor shift toward lower-speed roads. Crashes in zones of 35 mph or less accounted for 65.9% of incidents with a recorded speed limit in 2019, up from 63.4% in 2018. Notably, the number of fatal crashes in 25 mph zones increased from two to five, while fatalities in 40 mph zones decreased from six to five.

Fatal crashes by zone: 1 mph: 1 of 205 (0.488%) · 25 mph: 5 of 936 (0.534%) · 40 mph: 5 of 460 (1.087%) · 45 mph: 2 of 369 (0.542%) · 55 mph: 1 of 37 (2.703%) · 65 mph: 2 of 176 (1.136%) · 88 mph: 1 of 180 (0.556%)

Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 10, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: connecticut, CT
  • Total crash records analyzed: 4,015
  • Total persons involved: 8,907
  • Total vehicles involved: 6,782

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: 2019." Published September 10, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2019-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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