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Monthly Traffic Safety Analysis

9,109 CRASHES IN
CONNECTICUT, CT
DECEMBER 2023

All metrics benchmarked againstDecember 2022

In December 2023, Connecticut recorded 9,109 traffic crashes, a 1.9% decrease from the 9,282 crashes reported in December 2022. While overall crash and injury totals remained relatively stable, fatalities saw a notable decline from 32 to 26. The most significant year-over-year change was a reduction in pedestrian fatalities, which fell from 12 in December 2022 to 4 in December 2023.

9,109

▼ -1.9%was 9,282

Total Crash Events

▼ -18.8%was 32

Persons Killed

2,940

▼ -0.7%was 2,962

Persons Injured

1,066

▼ -3.4%was 1,104

Hit-and-Run Crashes

Note: "Persons Killed" (26) counts individual fatalities across all crash events. "Fatal" in the severity table below (26) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

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

Trend Summary

Overall traffic safety metrics in Connecticut showed a slight improvement in December 2023 compared to the same month in the prior year. Total crashes decreased by 1.9% from 9,282 to 9,109. Similarly, total injuries saw a marginal decline of 0.7% from 2,962 to 2,940, while total fatalities decreased by 18.8% from 32 to 26.

1,066

Hit-and-Run Crashes — December 2023

▼ -3.4% vs prior (1,104)

Hit-and-run incidents saw a slight decrease in both count and rate compared to the previous year. In December 2023, there were 1,066 hit-and-run crashes, down from 1,104 in December 2022. This corresponds to a hit-and-run rate of 11.7% of all crashes, a small reduction from the 11.9% rate observed in the prior year.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 12↓-66.7%

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 20↑10.0%

120

Pedestrians Injured

Prior: 134↓-10.4%

Cyclists Injured

Prior: 10↑40.0%

2,806

Motorists Injured

Prior: 2,818↓-0.4%

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

When Crashes Happen

The temporal patterns of crashes remained largely consistent year-over-year. The peak day for crashes in both December 2023 and December 2022 was Friday, with 1,776 and 1,704 incidents respectively. The evening commute hour of 5 PM was the peak hour in both periods, accounting for 954 crashes in 2023 and 1,006 in 2022, marking a slight decrease in peak-hour volume.

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

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

Crash Severity Breakdown

The distribution of crash severity saw minor shifts between December 2022 and December 2023. The proportion of fatal crashes remained stable at 0.3% of all incidents, though the absolute count of fatal crashes fell from 29 to 26. Crashes resulting in serious injuries (A) increased slightly from 84 to 88, while crashes with possible injuries (C) decreased from 1,123 to 1,008.

Outcome by Severity (Crash Events)

Fatal26fatal crashes0.3%

↓-10.3%prior 29

Serious Injury88serious injury crashes1%

↑4.8%prior 84

Minor Injury1,041minor injury crashes11.4%

↑9.6%prior 950

Possible Injury1,008possible injury crashes11.1%

↓-10.2%prior 1,123

No Injury6,946no injury crashes76.3%

↓-2.1%prior 7,096

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

Severity Distribution (Crash Events)

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

Road & Environmental Conditions

Crash conditions were broadly similar year-over-year, with the majority of incidents occurring in clear weather and on dry roads in both periods. In December 2023, 76.6% of crashes occurred in clear weather, compared to 74.3% in the prior year. A significant difference was the near absence of snow-related road conditions, which were a factor in only 4 crashes in December 2023 compared to 324 in December 2022, indicating different weather patterns for the two months.

Weather

Clear6,981 (77.0%)

↑1.2%prior 6,896

Rain1,319 (14.6%)

↓-7.5%prior 1,426

Cloudy459 (5.1%)

↑26.4%prior 363

Fog, Smog, Smoke211 (2.3%)

↑502.9%prior 35

Freezing Rain or Freezing Drizzle52 (0.6%)

↓-26.8%prior 71

Other19 (0.2%)

↑171.4%prior 7

Blowing Snow8 (0.1%)

↓-85.2%prior 54

Severe Crosswinds7 (0.1%)

↓-46.2%prior 13

Snow3 (0.0%)

↓-99.2%prior 374

Sleet or Hail2 (0.0%)

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

Lighting

Daylight4,664 (51.6%)

↓-1.8%prior 4,749

Dark-Lighted3,131 (34.6%)

↓-4.7%prior 3,284

Dark-Not Lighted919 (10.2%)

↑4.7%prior 878

Dusk120 (1.3%)

↓-22.1%prior 154

Dark-Unknown Lighting105 (1.2%)

↑47.9%prior 71

Dawn90 (1.0%)

↑36.4%prior 66

Other16 (0.2%)

↑60.0%prior 10

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

Road Surface

Dry6,737 (74.4%)

↑0.9%prior 6,675

Wet2,086 (23.0%)

↑3.1%prior 2,023

Ice / Frost186 (2.1%)

↑25.7%prior 148

Standing Water14 (0.2%)

Moving Water13 (0.1%)

Mud, Dirt, Gravel7 (0.1%)

↑40.0%prior 5

Other5 (0.1%)

→0.0%prior 5

Snow4 (0.0%)

↓-98.8%prior 324

Sand1 (0.0%)

↓-87.5%prior 8

Slush1 (0.0%)

↓-97.9%prior 47

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

Vehicles & Demographics

The top five vehicle makes involved in crashes—Honda, Toyota, Ford, Nissan, and Chevrolet—were identical in both December 2023 and December 2022, with minimal changes in their respective counts. The demographic profile of individuals involved in crashes also remained stable. The 26-34 age group was the most frequently involved in both periods, accounting for 3,685 people in the current period compared to 3,769 in the prior year.

Top Vehicle Makes (17,017 vehicles)

HONDA1,934 (11.4%)

↓-1.5%prior 1,964

TOYOTA1,891 (11.1%)

↑3.9%prior 1,820

FORD1,510 (8.9%)

↓-1.3%prior 1,530

NISSAN1,233 (7.2%)

↓-5.3%prior 1,302

CHEVROLET1,024 (6%)

↓-1.1%prior 1,035

SUBARU852 (5%)

↑5.4%prior 808

JEEP759 (4.5%)

↓-6.2%prior 809

HYUNDAI719 (4.2%)

↓-5.4%prior 760

KIA426 (2.5%)

↑7.0%prior 398

BMW390 (2.3%)

↓-8.0%prior 424

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

1,402 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (19,781 persons with recorded sex)

Male11,073 (56.0%)

↓-3.7%prior 11,501

Female8,708 (44.0%)

↓-3.2%prior 8,998

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

Speed Limit Zones

Crashes in 25 mph zones were the most numerous in both periods, with 2,601 incidents in December 2023 and 2,581 in December 2022. A notable shift occurred in the distribution of fatal crashes by speed zone. While the 25 mph zone saw a decrease in fatal crashes from 10 to 5, the 40 mph zone saw an increase from 2 to 6 fatal crashes year-over-year.

Fatal crashes by zone: 20 mph: 1 of 53 (1.887%) · 25 mph: 5 of 2,601 (0.192%) · 30 mph: 1 of 734 (0.136%) · 35 mph: 2 of 1,061 (0.189%) · 40 mph: 6 of 591 (1.015%) · 45 mph: 2 of 371 (0.539%) · 50 mph: 4 of 268 (1.493%) · 55 mph: 2 of 846 (0.236%) · 65 mph: 2 of 495 (0.404%) · 99 mph: 1 of 47 (2.128%)

Source: Connecticut Crash Data · Csv Open Data · 2023-12-01 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: 2023-12-01 through 2023-12-31
Report generated: August 20, 2026

Data Coverage

Reporting period: 2023-12-01 through 2023-12-31 (31 days)
Geographic scope: connecticut, CT
Total crash records analyzed: 9,109
Total persons involved: 21,409
Total vehicles involved: 17,017

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: December 2023." Published August 20, 2026. Reporting period: 2023-12-01 to 2023-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/december-2023-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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