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Yearly Traffic Safety Analysis

2,533 CRASHES IN
CONNECTICUT, CT
2023

All metrics benchmarked against2022

In Tolland County, total traffic crashes remained nearly stable, decreasing by just 0.35% from 2,542 in 2022 to 2,533 in 2023. Despite this stability in crash volume, the number of fatalities saw a significant year-over-year increase. The most notable shift was a 60% rise in total fatalities, which grew from 10 in 2022 to 16 in 2023.

2,533

▼ -0.4%was 2,542

Total Crash Events

▲ 60.0%was 10

Persons Killed

826

▲ 2.4%was 807

Persons Injured

243

▲ 2.5%was 237

Hit-and-Run Crashes

Note: "Persons Killed" (16) counts individual fatalities across all crash events. "Fatal" in the severity table below (15) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend for crash volume in Tolland County was stable year-over-year, with total incidents decreasing by a marginal 0.35% from 2,542 to 2,533. However, the severity of these crashes worsened, as total injuries increased by 2.4% from 807 to 826. Most significantly, total fatalities rose by 60%, from 10 deaths in 2022 to 16 in 2023.

243

Hit-and-Run Crashes — 2023

▲ 2.5% vs prior (237)

The frequency of hit-and-run incidents saw a slight increase in 2023 compared to the prior year. The total number of hit-and-run crashes rose from 237 to 243. This corresponds to a minor increase in the hit-and-run rate, which edged up from 9.3% of all crashes in 2022 to 9.6% in 2023.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 1↑100.0%

Cyclists Killed

Prior: 1→0.0%

Motorists Killed

Prior: 8↑62.5%

Pedestrians Injured

Prior: 18↓-16.7%

Cyclists Injured

Prior: 4↓-25.0%

808

Motorists Injured

Prior: 785↑2.9%

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The daily and hourly patterns of crashes showed some year-over-year changes. The peak day for crashes shifted from Wednesday in 2022 (436 crashes) to Friday in 2023 (421 crashes). The peak hour for collisions remained 4 PM in both periods, though the number of crashes occurring at that hour increased from 224 to 251.

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity outcomes worsened in 2023 compared to the prior year. The number of fatal crashes increased from 9 to 15, and the fatal crash rate rose from 0.35 to 0.59. While the number of crashes resulting in serious injuries decreased from 34 to 28, incidents involving minor or possible injuries increased.

Severity is per crash event (most severe injury). 15 fatal crash events resulted in 16 persons killed.

Outcome by Severity (Crash Events)

Fatal15fatal crashes0.6%

↑66.7%prior 9

Serious Injury28serious injury crashes1.1%

↓-17.6%prior 34

Minor Injury385minor injury crashes15.2%

↑1.9%prior 378

Possible Injury212possible injury crashes8.4%

↑5.5%prior 201

No Injury1,893no injury crashes74.7%

↓-1.4%prior 1,920

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The distribution of crashes across environmental conditions remained largely consistent, with a majority occurring in clear weather on dry roads in both periods. However, there was a noticeable increase in crashes during adverse weather in 2023. Incidents during rain increased from 244 to 309, and crashes on wet road surfaces rose from 385 to 448 year-over-year.

Weather

Clear1,979 (78.3%)

↓-0.5%prior 1,989

Rain309 (12.2%)

↑26.6%prior 244

Cloudy97 (3.8%)

↑11.5%prior 87

Snow69 (2.7%)

↓-28.9%prior 97

Freezing Rain or Freezing Drizzle31 (1.2%)

↓-50.0%prior 62

Fog, Smog, Smoke23 (0.9%)

↑64.3%prior 14

Blowing Snow10 (0.4%)

↓-54.5%prior 22

Sleet or Hail6 (0.2%)

↓-25.0%prior 8

Other4 (0.2%)

Blowing Sand, Soil, Dirt1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight1,724 (68.3%)

↑2.6%prior 1,680

Dark-Lighted381 (15.1%)

↓-3.5%prior 395

Dark-Not Lighted334 (13.2%)

↓-7.5%prior 361

Dusk39 (1.5%)

↓-23.5%prior 51

Dawn33 (1.3%)

↑26.9%prior 26

Dark-Unknown Lighting11 (0.4%)

↓-21.4%prior 14

Other2 (0.1%)

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry1,935 (76.5%)

↑0.8%prior 1,920

Wet448 (17.7%)

↑16.4%prior 385

Snow56 (2.2%)

↓-31.7%prior 82

Ice / Frost51 (2.0%)

↓-47.4%prior 97

Slush23 (0.9%)

↓-32.4%prior 34

Mud, Dirt, Gravel13 (0.5%)

↑30.0%prior 10

Standing Water2 (0.1%)

Sand1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The profile of vehicles and persons involved in crashes showed high stability between 2022 and 2023. The top three vehicle makes involved in collisions remained Toyota, Ford, and Honda, with only minor changes in their total counts. Similarly, the age distribution of all persons involved was nearly identical, with the 26-34 age group consistently representing the largest cohort in both years.

Top Vehicle Makes (4,471 vehicles)

TOYOTA484 (10.8%)

↑4.8%prior 462

FORD475 (10.6%)

↑4.6%prior 454

HONDA459 (10.3%)

↑2.0%prior 450

NISSAN310 (6.9%)

↓-4.6%prior 325

SUBARU271 (6.1%)

↑2.7%prior 264

CHEVROLET254 (5.7%)

↑1.6%prior 250

HYUNDAI187 (4.2%)

↓-3.1%prior 193

JEEP174 (3.9%)

↓-9.8%prior 193

VOLKSWAGEN112 (2.5%)

↑9.8%prior 102

MAZDA101 (2.3%)

↑17.4%prior 86

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (5,506 persons with recorded sex)

Male3,109 (56.5%)

↑0.9%prior 3,080

Female2,397 (43.5%)

↑0.6%prior 2,382

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events

Speed Limit Zones

A notable shift occurred in where crashes happened, with a higher concentration on high-speed roadways in 2023. Crashes in 65 mph zones increased from 270 in 2022 to 318 in 2023. Concurrently, fatal crashes became more frequent in higher speed zones, with fatalities increasing in 40 mph, 50 mph, and 65 mph zones compared to the previous year.

Fatal crashes by zone: 1 mph: 1 of 158 (0.633%) · 35 mph: 4 of 633 (0.632%) · 40 mph: 3 of 319 (0.94%) · 45 mph: 2 of 287 (0.697%) · 50 mph: 2 of 36 (5.556%) · 65 mph: 3 of 318 (0.943%)

Source: Connecticut Crash Data · Csv Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
Report generated: August 21, 2026

Data Coverage

Reporting period: 2023-01-01 through 2023-12-31 (365 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 2,533
Total persons involved: 5,899
Total vehicles involved: 4,471

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: 2023." Published August 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2023-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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