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YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · JANUARY 2024
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/connecticut/statewide/january-2024-report
Monthly Traffic Safety Analysis
8,858 CRASHES IN
CONNECTICUT, CT
JANUARY 2024
In January 2024, Connecticut recorded 8,858 traffic crashes, a 15.0% increase from the 7,706 crashes in January 2023. While total fatalities remained nearly stable with 26 deaths compared to 25 the previous year, the most significant year-over-year change was a 95.0% increase in crashes where speeding was a factor, rising from 694 to 1,353 incidents.
8,858
▲ 14.9%was 7,706
Total Crash Events
26
▲ 4.0%was 25
Persons Killed
2,534
▲ 4.0%was 2,436
Persons Injured
1,010
▲ 1.1%was 999
Hit-and-Run Crashes
Note: "Persons Killed" (26) counts individual fatalities across all crash events. "Fatal" in the severity table below (23) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Crash data for January 2024 indicates a rising trend in collisions compared to the same month in 2023. Total crashes increased by 15.0% from 7,706 to 8,858. This upward trend was also reflected in total injuries, which rose by 4.0% to 2,534, while fatalities remained nearly unchanged at 26.
1,010
Hit-and-Run Crashes — January 2024
▲ 1.1% vs prior (999)
The total number of hit-and-run crashes remained nearly stable, with 1,010 incidents in January 2024 compared to 999 in January 2023. However, because total crashes increased significantly, the hit-and-run rate—the proportion of all crashes that were hit-and-runs—decreased. The rate fell from 13.0% in the prior year to 11.4% in the current period, indicating a downward trend in the prevalence of these incidents relative to overall crash volume.
Vulnerable Road User Casualties
6
Pedestrians Killed
0
Cyclists Killed
20
Motorists Killed
119
Pedestrians Injured
12
Cyclists Injured
2,403
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-01-31 · 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 consistent year-over-year, with Tuesday being the peak day for collisions in both January 2024 (1,680 crashes) and January 2023 (1,282 crashes). The 5 PM hour also remained the peak time for crashes in both periods, recording 780 and 801 incidents, respectively. While the peak times did not shift, the volume of crashes on the peak day, Tuesday, increased by 31.0%.
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-01-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-01-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The overall severity of crashes decreased slightly in January 2024 compared to the previous year, even as total crashes rose. The fatal crash rate fell from 0.31 to 0.26 per 100 crashes. The proportion of crashes resulting in any level of injury (Serious, Minor, or Possible) also declined from 23.7% in January 2023 to 21.1% in January 2024, while the share of no-injury crashes increased from 76.0% to 78.6%.
Severity is per crash event (most severe injury). 23 fatal crash events resulted in 26 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-01-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-01-31 · Most severe injury per crash record
Road & Environmental Conditions
Environmental conditions showed a significant shift between the two periods, primarily related to winter weather. In January 2024, crashes occurring in snow conditions increased five-fold to 1,128 from 224 the previous year. Correspondingly, collisions on snow- or ice-covered roads accounted for 16.6% of all crashes, up from just 3.8% in January 2023. Crashes in rainy conditions and on wet roads saw a proportional decrease.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-01-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-01-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-01-31 · Road surface condition field
Vehicles & Demographics
The makes of vehicles involved in crashes remained consistent, with Honda, Toyota, and Ford being the top three in both January 2024 and January 2023. The ranking of the top five most common vehicle makes did not change year-over-year. Analysis of persons involved shows a stable age distribution, with all age groups maintaining a similar share of total persons involved in crashes compared to the prior year.
Top Vehicle Makes (15,942 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-01-31 · Vehicle unit records
1,337 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (18,587 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-01-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes increased across most speed zones in January 2024, with a notable 69% rise in incidents on roads with a 65 mph speed limit (679 crashes, up from 402). Despite the overall increase in crashes, the fatal crash rate decreased in several higher speed zones, including the 50 mph and 65 mph zones. Conversely, the fatal crash rate in 25 mph zones more than doubled, increasing from 0.14% to 0.31% year-over-year.
Fatal crashes by zone: 1 mph: 2 of 1,114 (0.18%) · 25 mph: 8 of 2,596 (0.308%) · 30 mph: 1 of 744 (0.134%) · 35 mph: 2 of 977 (0.205%) · 40 mph: 2 of 528 (0.379%) · 45 mph: 2 of 360 (0.556%) · 50 mph: 1 of 220 (0.455%) · 55 mph: 2 of 723 (0.277%) · 65 mph: 3 of 679 (0.442%)
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-01-31 · 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: 2024-01-01 through 2024-01-31
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2024-01-01 through 2024-01-31 (31 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 8,858
- Total persons involved: 19,977
- Total vehicles involved: 15,942
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: January 2024." Published August 20, 2026. Reporting period: 2024-01-01 to 2024-01-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/january-2024-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Connecticut Crash Data · Csv
Period: 2024-01-01 – 2024-01-31
Generated: August 20, 2026 · All rights reserved
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