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Yearly Traffic Safety Analysis

2,738 CRASHES IN
CONNECTICUT, CT
2025

All metrics benchmarked against2024

In Tolland County, crash data for 2025 shows a total of 2,738 crashes, a slight decrease of 0.2% from the 2,744 crashes recorded in 2024. While overall crash volume remained stable, there was a notable 21.8% decrease in crashes where speeding was a contributing factor, which fell from 431 in the prior year to 337 in the current year. Fatalities also decreased from 13 to 10 year-over-year.

2,738

▼ -0.2%was 2,744

Total Crash Events

▼ -23.1%was 13

Persons Killed

879

▼ -0.9%was 887

Persons Injured

284

▲ 5.6%was 269

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 · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in traffic crashes in Tolland County was relatively stable year-over-year, with a marginal 0.2% decrease from 2,744 incidents in 2024 to 2,738 in 2025. This stability was also reflected in injury and fatality counts, which saw minor decreases. Total injuries fell by 0.9% from 887 to 879, and fatalities decreased from 13 to 10.

284

Hit-and-Run Crashes — 2025

▲ 5.6% vs prior (269)

Hit-and-run incidents increased in both absolute numbers and as a percentage of total crashes. The count of hit-and-run crashes rose from 269 in 2024 to 284 in 2025. Consequently, the hit-and-run rate trended upward, increasing from 9.8% of all crashes in the prior period to 10.4% in the current period.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 1↓-100.0%

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 12↓-16.7%

Pedestrians Injured

Prior: 23↑8.7%

Cyclists Injured

Prior: 3↑66.7%

849

Motorists Injured

Prior: 861↓-1.4%

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal crash patterns saw a slight shift between the two periods. The most frequent day for crashes moved from Thursday (436 crashes) in 2024 to Friday (455 crashes) in 2025. The peak hour for collisions shifted one hour earlier, from 5 p.m. in the prior year to 4 p.m. in the current year, though the peak crash count at 4 p.m. in 2025 (243 crashes) was the same as the peak count at 5 p.m. in 2024.

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity outcomes showed a mixed but generally stable pattern. The fatal crash rate decreased from 0.44% in 2024 to 0.37% in 2025, corresponding to a drop from 12 to 10 fatal crashes. While the proportion of crashes resulting in minor injuries fell from 14.9% to 13.9%, the share of serious injury crashes increased from 0.9% (25 crashes) to 1.1% (31 crashes). Crashes with no injuries accounted for a slightly larger share of the total, rising from 75.1% to 76.0%.

Outcome by Severity (Crash Events)

Fatal10fatal crashes0.4%

↓-16.7%prior 12

Serious Injury31serious injury crashes1.1%

↑24.0%prior 25

Minor Injury380minor injury crashes13.9%

↓-6.9%prior 408

Possible Injury237possible injury crashes8.7%

↓-0.8%prior 239

No Injury2,080no injury crashes76%

↑1.0%prior 2,060

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The distribution of crashes by environmental conditions remained largely consistent year-over-year. However, there was a marked decrease in crashes occurring in snow, which fell from 161 in 2024 to 88 in 2025, with a corresponding drop in crashes on snowy road surfaces from 140 to 93. Conversely, crashes in dark, unlighted conditions increased from 369 to 408.

Weather

Clear2,191 (80.6%)

↑4.8%prior 2,090

Rain250 (9.2%)

↓-3.8%prior 260

Cloudy101 (3.7%)

↓-7.3%prior 109

Snow88 (3.2%)

↓-45.3%prior 161

Freezing Rain or Freezing Drizzle46 (1.7%)

↑2.2%prior 45

Blowing Snow20 (0.7%)

↓-33.3%prior 30

Fog, Smog, Smoke8 (0.3%)

↓-57.9%prior 19

Sleet or Hail7 (0.3%)

↓-36.4%prior 11

Severe Crosswinds5 (0.2%)

Other4 (0.1%)

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight1,831 (67.4%)

↓-1.5%prior 1,858

Dark-Not Lighted408 (15.0%)

↑10.6%prior 369

Dark-Lighted398 (14.6%)

↓-2.2%prior 407

Dusk41 (1.5%)

↓-6.8%prior 44

Dawn26 (1.0%)

↓-3.7%prior 27

Dark-Unknown Lighting12 (0.4%)

↓-7.7%prior 13

Other2 (0.1%)

↓-66.7%prior 6

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry2,131 (78.3%)

↑3.3%prior 2,063

Wet360 (13.2%)

↓-5.8%prior 382

Snow93 (3.4%)

↓-33.6%prior 140

Ice / Frost86 (3.2%)

↑17.8%prior 73

Slush40 (1.5%)

↓-34.4%prior 61

Mud, Dirt, Gravel4 (0.1%)

↓-50.0%prior 8

Other3 (0.1%)

Standing Water3 (0.1%)

Sand2 (0.1%)

Moving Water1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

The top three vehicle makes involved in crashes shifted year-over-year. In 2025, Honda was the most common make with 515 vehicles, up from 479 in 2024 when it ranked third. Toyota and Ford, which were the top two makes in the prior year, saw their counts decrease to 485 and 472, respectively. Analysis of persons involved in crashes shows a decrease in the 16-20 age group (from 986 to 881) and an increase in the 65+ age group (from 714 to 732).

Top Vehicle Makes (4,747 vehicles)

HONDA515 (10.8%)

↑7.5%prior 479

TOYOTA485 (10.2%)

↓-10.4%prior 541

FORD472 (9.9%)

↓-11.3%prior 532

NISSAN354 (7.5%)

↑26.9%prior 279

SUBARU341 (7.2%)

↑7.2%prior 318

CHEVROLET266 (5.6%)

↑6.8%prior 249

JEEP236 (5%)

↑4.0%prior 227

HYUNDAI198 (4.2%)

↓-12.4%prior 226

VOLKSWAGEN123 (2.6%)

↓-6.1%prior 131

KIA110 (2.3%)

↓-0.9%prior 111

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

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

Sex Distribution (5,781 persons with recorded sex)

Male3,343 (57.8%)

↑2.5%prior 3,263

Female2,438 (42.2%)

↓-5.6%prior 2,582

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across speed zones showed some changes year-over-year. Crashes in 40 mph and 45 mph zones decreased, while those in 65 mph zones increased from 312 to 334. Fatality patterns within these zones also shifted; in 2025, there were no fatal crashes in 65 mph zones, compared to two in the previous year. The highest number of fatal crashes in 2025 occurred in 35 mph zones, with 3 fatalities from 613 crashes.

Fatal crashes by zone: 25 mph: 1 of 384 (0.26%) · 30 mph: 2 of 387 (0.517%) · 35 mph: 3 of 613 (0.489%) · 40 mph: 2 of 346 (0.578%) · 45 mph: 2 of 326 (0.613%)

Source: Connecticut Crash Data · Csv Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
Report generated: August 21, 2026

Data Coverage

Reporting period: 2025-01-01 through 2025-12-31 (365 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 2,738
Total persons involved: 6,197
Total vehicles involved: 4,747

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: 2025." Published August 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2025-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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