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Yearly Traffic Safety Analysis

24,579 CRASHES IN
CONNECTICUT, CT
2023

All metrics benchmarked against2022

In Hartford County, overall crash volume remained stable year-over-year, with 24,579 total crashes in 2023 compared to 24,657 in 2022, a decrease of less than 1%. Despite this stability in crash frequency, the most notable shift was a significant 26.3% decrease in traffic fatalities, which fell from 95 in the prior year to 70 in the current year.

24,579

▼ -0.3%was 24,657

Total Crash Events

▼ -26.3%was 95

Persons Killed

9,124

▲ 0.2%was 9,105

Persons Injured

3,239

▲ 0.6%was 3,221

Hit-and-Run Crashes

Note: "Persons Killed" (70) counts individual fatalities across all crash events. "Fatal" in the severity table below (64) 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 in traffic crashes in Hartford County is one of stability in volume but decreasing severity. Total crashes declined by a marginal 0.3% from 2022 to 2023. However, fatal crashes decreased by 23.8% (from 84 to 64), and total fatalities dropped by 26.3% (from 95 to 70).

3,239

Hit-and-Run Crashes — 2023

▲ 0.6% vs prior (3,221)

Hit-and-run incidents remained a stable component of total crashes. The number of hit-and-run crashes increased slightly from 3,221 in 2022 to 3,239 in 2023. The hit-and-run rate showed minimal change, ticking up from 13.1% of all crashes in the prior year to 13.2% in the current year.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 25↓-44.0%

Cyclists Killed

Prior: 1↓-100.0%

Motorists Killed

Prior: 69↓-18.8%

274

Pedestrians Injured

Prior: 260↑5.4%

Cyclists Injured

Prior: 53↑81.1%

8,754

Motorists Injured

Prior: 8,792↓-0.4%

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

Temporal crash patterns were highly consistent year-over-year. Friday remained the peak day for crashes in both 2023 (4,136 crashes) and 2022 (4,175 crashes). Similarly, the 4 p.m. hour was the peak time for collisions in both periods, accounting for 2,122 crashes in 2023 and 2,135 in 2022.

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

While the percentage distribution of crash severity remained stable, the absolute number of severe crashes decreased. Fatal crashes dropped from 84 in 2022 to 64 in 2023, a 23.8% reduction. The proportion of crashes resulting in serious injury (1.3%), minor or possible injury (25.4% in 2023 vs 25.2% in 2022), and no injury (73.1% in 2023 vs 73.2% in 2022) was nearly unchanged.

Severity is per crash event (most severe injury). 64 fatal crash events resulted in 70 persons killed.

Outcome by Severity (Crash Events)

Fatal64fatal crashes0.3%

↓-23.8%prior 84

Serious Injury322serious injury crashes1.3%

↓-1.5%prior 327

Minor Injury3,290minor injury crashes13.4%

↑0.6%prior 3,272

Possible Injury2,938possible injury crashes12%

↑0.1%prior 2,934

No Injury17,965no injury crashes73.1%

↓-0.4%prior 18,040

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

Crash conditions saw a notable shift toward more incidents occurring in adverse weather. While crashes on dry roads decreased, collisions on wet road surfaces increased from 3,498 in 2022 to 4,202 in 2023, representing a rise from 14.2% to 17.1% of all crashes. Correspondingly, crashes during rain increased from 9.5% to 11.6% of the total. Lighting conditions remained proportionally consistent between the two periods.

Weather

Clear19,845 (81.2%)

↓-1.8%prior 20,199

Rain2,856 (11.7%)

↑21.8%prior 2,344

Cloudy1,100 (4.5%)

↑19.6%prior 920

Snow364 (1.5%)

↓-17.8%prior 443

Freezing Rain or Freezing Drizzle106 (0.4%)

↓-73.0%prior 392

Fog, Smog, Smoke90 (0.4%)

↑18.4%prior 76

Blowing Snow42 (0.2%)

↓-45.5%prior 77

Sleet or Hail21 (0.1%)

↑16.7%prior 18

Other11 (0.0%)

↓-45.0%prior 20

Severe Crosswinds6 (0.0%)

↓-14.3%prior 7

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

Lighting

Daylight16,896 (69.2%)

↓-0.6%prior 17,002

Dark-Lighted5,892 (24.1%)

↑2.6%prior 5,743

Dark-Not Lighted956 (3.9%)

↓-3.5%prior 991

Dusk301 (1.2%)

↓-17.5%prior 365

Dawn176 (0.7%)

↓-6.4%prior 188

Dark-Unknown Lighting162 (0.7%)

↓-4.1%prior 169

Other26 (0.1%)

↑30.0%prior 20

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

Road Surface

Dry19,715 (80.6%)

↓-1.1%prior 19,941

Wet4,202 (17.2%)

↑20.1%prior 3,498

Snow262 (1.1%)

↓-29.2%prior 370

Ice / Frost157 (0.6%)

↓-69.1%prior 508

Slush79 (0.3%)

↓-41.0%prior 134

Other12 (0.0%)

↓-25.0%prior 16

Standing Water9 (0.0%)

↑28.6%prior 7

Mud, Dirt, Gravel9 (0.0%)

↓-52.6%prior 19

Moving Water6 (0.0%)

↓-14.3%prior 7

Sand2 (0.0%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes, led by Honda, Toyota, and Ford, remained consistent in rank and volume year-over-year. A notable demographic shift occurred in the age of persons involved in crashes; the number of individuals aged 65 and older increased from 5,897 in 2022 to 6,406 in 2023, an 8.6% rise. Conversely, involvement for most younger age groups, such as 26-34, saw a slight decrease.

Top Vehicle Makes (46,691 vehicles)

HONDA4,804 (10.3%)

↓-0.6%prior 4,832

TOYOTA4,352 (9.3%)

↑1.3%prior 4,296

FORD3,855 (8.3%)

↓-1.1%prior 3,896

NISSAN3,131 (6.7%)

↓-0.3%prior 3,140

CHEVROLET2,289 (4.9%)

↑2.0%prior 2,245

SUBARU1,838 (3.9%)

↓-1.8%prior 1,872

HYUNDAI1,747 (3.7%)

↑1.6%prior 1,719

JEEP1,746 (3.7%)

↓-2.1%prior 1,783

HOND1,547 (3.3%)

↓-1.7%prior 1,574

TOYT1,273 (2.7%)

↑1.1%prior 1,259

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

3,972 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (56,739 persons with recorded sex)

Male31,450 (55.4%)

↓-0.6%prior 31,636

Female25,289 (44.6%)

↓-1.0%prior 25,549

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

Speed Limit Zones

There was a shift in where crashes occurred, with fewer incidents in higher speed zones. Crashes in 65 mph zones decreased from 2,219 to 2,140, and fatal crashes in this zone dropped from 15 to 13. Conversely, crashes in 25 mph zones increased from 3,955 to 4,029. The fatal crash rate in 30 mph zones also saw a decrease, falling from 0.69% in 2022 to 0.55% in 2023.

Fatal crashes by zone: 25 mph: 8 of 4,029 (0.199%) · 30 mph: 14 of 2,541 (0.551%) · 35 mph: 9 of 4,128 (0.218%) · 40 mph: 6 of 2,077 (0.289%) · 45 mph: 2 of 813 (0.246%) · 50 mph: 7 of 1,663 (0.421%) · 55 mph: 4 of 902 (0.443%) · 65 mph: 13 of 2,140 (0.607%)

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: 24,579
Total persons involved: 60,482
Total vehicles involved: 46,691

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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