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Yearly Traffic Safety Analysis

2,792 CRASHES IN
CONNECTICUT, CT
2019

All metrics benchmarked against2018

In 2019, Tolland County recorded 2,792 total crashes, representing a 3.3% increase from the 2,703 crashes in 2018. While total crashes and injuries rose, the most significant year-over-year change was a 37.5% reduction in traffic fatalities, which fell from 16 in 2018 to 10 in 2019.

2,792

▲ 3.3%was 2,703

Total Crash Events

▼ -37.5%was 16

Persons Killed

887

▲ 8.2%was 820

Persons Injured

194

▲ 4.9%was 185

Hit-and-Run Crashes

Note: "Persons Killed" (10) counts individual fatalities across all crash events. "Fatal" in the severity table below (10) 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

The overall trend shows a rise in traffic incidents, with total crashes increasing by 3.3% from 2,703 in 2018 to 2,792 in 2019. This was accompanied by an 8.2% increase in injuries, from 820 to 887. Conversely, traffic fatalities saw a substantial decline, dropping from 16 to 10 over the same period.

194

Hit-and-Run Crashes — 2019

▲ 4.9% vs prior (185)

Hit-and-run incidents trended slightly upward from 2018 to 2019. The total number of hit-and-run crashes increased from 185 to 194. As a percentage of all crashes, the hit-and-run rate also saw a marginal increase from 6.8% in 2018 to 6.9% in 2019.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 1↓-100.0%

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 15↓-33.3%

Pedestrians Injured

Prior: 19↓-47.4%

Cyclists Injured

Prior: 4→0.0%

873

Motorists Injured

Prior: 797↑9.5%

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 timing of crashes shifted year-over-year. In 2019, the peak day for crashes moved to Monday with 452 incidents, compared to Friday in 2018 which had 443 crashes. The peak hour also shifted later in the day, from 3 p.m. in 2018 (220 crashes) to 5 p.m. in 2019 (271 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

While total crashes increased, the severity profile showed a decrease in fatal outcomes. The number of fatal crashes fell from 15 in 2018 to 10 in 2019, and the fatal crash rate per 100 incidents dropped from 0.55 to 0.36. However, the count of serious injury crashes increased from 20 to 29, representing a shift from 0.7% to 1.0% of all crashes.

Outcome by Severity (Crash Events)

Fatal10fatal crashes0.4%

↓-33.3%prior 15

Serious Injury29serious injury crashes1%

↑45.0%prior 20

Minor Injury349minor injury crashes12.5%

↑7.7%prior 324

Possible Injury276possible injury crashes9.9%

↓-0.7%prior 278

No Injury2,128no injury crashes76.2%

↑3.0%prior 2,066

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

The distribution of crashes across different conditions remained largely stable between 2018 and 2019. Crashes in clear weather and on dry roads constituted the majority in both years, with proportions changing by less than one percentage point. One notable shift was an increase in crashes on roads with ice or frost, which rose from 74 incidents in 2018 to 118 in 2019.

Weather

Clear2,084 (75.1%)

↑4.3%prior 1,998

Rain306 (11.0%)

↓-5.0%prior 322

Snow155 (5.6%)

↑23.0%prior 126

Cloudy110 (4.0%)

↓-6.8%prior 118

Freezing Rain or Freezing Drizzle52 (1.9%)

↑67.7%prior 31

Sleet or Hail29 (1.0%)

↑163.6%prior 11

Blowing Snow20 (0.7%)

↓-60.0%prior 50

Fog, Smog, Smoke15 (0.5%)

↓-6.3%prior 16

Other2 (0.1%)

Blowing Sand, Soil, Dirt1 (0.0%)

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

Lighting

Daylight1,870 (67.5%)

↑3.8%prior 1,801

Dark-Lighted411 (14.8%)

↑5.1%prior 391

Dark-Not Lighted405 (14.6%)

↑1.8%prior 398

Dusk51 (1.8%)

↓-3.8%prior 53

Dawn25 (0.9%)

↑8.7%prior 23

Dark-Unknown Lighting7 (0.3%)

↓-30.0%prior 10

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

Road Surface

Dry1,922 (69.2%)

↑2.8%prior 1,870

Wet480 (17.3%)

↓-8.4%prior 524

Snow144 (5.2%)

↑0.7%prior 143

Ice / Frost118 (4.3%)

↑59.5%prior 74

Slush95 (3.4%)

↑90.0%prior 50

Mud, Dirt, Gravel11 (0.4%)

↓-8.3%prior 12

Other4 (0.1%)

↓-42.9%prior 7

Sand1 (0.0%)

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 three vehicle makes involved in crashes—Ford, Toyota, and Honda—remained consistent in both 2018 and 2019. Analysis of persons involved shows a demographic shift, with the 26-34 age group increasing its involvement from 966 individuals in 2018 to 1,084 in 2019. Meanwhile, the number of individuals in the 16-20 and 21-25 age groups saw slight decreases.

Top Vehicle Makes (4,861 vehicles)

FORD579 (11.9%)

↑0.7%prior 575

TOYOTA488 (10%)

↑11.7%prior 437

HONDA434 (8.9%)

→0.0%prior 434

NISSAN326 (6.7%)

↑11.6%prior 292

SUBARU275 (5.7%)

→0.0%prior 275

CHEVROLET270 (5.6%)

↑4.7%prior 258

JEEP217 (4.5%)

↑7.4%prior 202

HYUNDAI189 (3.9%)

↓-9.1%prior 208

DODGE141 (2.9%)

↑4.4%prior 135

VOLKSWAGEN129 (2.7%)

↑29.0%prior 100

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

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

Sex Distribution (6,138 persons with recorded sex)

Male3,425 (55.8%)

↑3.7%prior 3,302

Female2,713 (44.2%)

↓-4.6%prior 2,843

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 was consistent, with the 35 mph, 25 mph, and 30 mph zones accounting for the most crashes in both years. However, the location of fatal crashes changed significantly; in 2018, the 65 mph zone had the most fatalities (4), but this dropped to zero in 2019. In 2019, the 45 mph zone became the most frequent location for fatal crashes, with 4 fatalities recorded, up from 2 in the prior year.

Fatal crashes by zone: 25 mph: 2 of 439 (0.456%) · 35 mph: 2 of 627 (0.319%) · 40 mph: 2 of 394 (0.508%) · 45 mph: 4 of 353 (1.133%)

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: August 21, 2026

Data Coverage

Reporting period: 2019-01-01 through 2019-12-31 (365 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 2,792
Total persons involved: 6,437
Total vehicles involved: 4,861

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 August 21, 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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