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YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · FEBRUARY 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/february-2024-report
Monthly Traffic Safety Analysis
7,655 CRASHES IN
CONNECTICUT, CT
FEBRUARY 2024
In February 2024, Connecticut recorded 7,655 total vehicle crashes, a 1.2% increase from the 7,567 crashes documented in February 2023. While the overall crash volume remained relatively stable, the most notable year-over-year shift was a significant 77.8% increase in fatalities, which rose from 18 to 32. This occurred even as the total number of injuries reported decreased by 11.8% during the same period.
7,655
▲ 1.2%was 7,567
Total Crash Events
32
▲ 77.8%was 18
Persons Killed
2,197
▼ -11.8%was 2,490
Persons Injured
975
▲ 3.5%was 942
Hit-and-Run Crashes
Note: "Persons Killed" (32) 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 · 2024-02-01 to 2024-02-29 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Year-over-year traffic crash trends for February show a slight increase in the total number of collisions, rising by 1.2% from 7,567 to 7,655. However, the severity of these incidents worsened considerably, with total fatalities increasing by 77.8% from 18 to 32. In contrast, the number of individuals injured in crashes declined by 11.8%, from 2,490 in the prior year to 2,197 in the current period.
975
Hit-and-Run Crashes — February 2024
▲ 3.5% vs prior (942)
Hit-and-run incidents increased from 942 in February 2023 to 975 in February 2024, a 3.5% rise in the total count. The hit-and-run rate, which measures the percentage of all crashes that are hit-and-runs, also trended slightly upward. It increased from 12.4% of all crashes in the prior year to 12.7% in the current year.
Vulnerable Road User Casualties
4
Pedestrians Killed
0
Cyclists Killed
28
Motorists Killed
96
Pedestrians Injured
9
Cyclists Injured
2,092
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2024-02-01 to 2024-02-29 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes shifted between February 2023 and February 2024. The day with the highest number of crashes moved from Saturday (1,342 incidents) in the prior year to Thursday (1,365 incidents) in the current year. Similarly, the peak hour for collisions changed from 12 p.m. (608 crashes) in 2023 to the afternoon rush hour of 3 p.m. (635 crashes) in 2024.
Source: Connecticut Crash Data · Csv Open Data · 2024-02-01 to 2024-02-29 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2024-02-01 to 2024-02-29 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes increased notably year-over-year. The number of fatal crashes rose from 17 to 26, and the fatal crash rate increased from 0.22 to 0.34 per 100 crashes. While fatal incidents were up, the proportion of crashes resulting in any injury decreased from 23.3% to 21.3%. This was driven primarily by a reduction in 'Possible Injury' crashes, which fell from 11.9% to 10.1% of all incidents.
Severity is per crash event (most severe injury). 26 fatal crash events resulted in 32 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2024-02-01 to 2024-02-29 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2024-02-01 to 2024-02-29 · Most severe injury per crash record
Road & Environmental Conditions
A higher proportion of crashes occurred under favorable conditions in February 2024 compared to the previous year. Crashes on dry roads accounted for 80.9% of all incidents, up from 75.1% in February 2023. Similarly, the share of crashes taking place in clear weather increased from 78.6% to 83.0%. The proportion of crashes occurring on wet, snowy, or icy road surfaces decreased correspondingly.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2024-02-01 to 2024-02-29 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2024-02-01 to 2024-02-29 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2024-02-01 to 2024-02-29 · Road surface condition field
Vehicles & Demographics
The top vehicle makes involved in crashes remained consistent, with Honda, Toyota, and Ford leading in both February 2023 and February 2024. The number of Hondas involved was identical at 1,635 vehicles in both periods. The age distribution of persons involved in collisions also showed little change, with the 26-34 age group constituting the largest share in both years, followed by the 35-44 and 45-54 age groups.
Top Vehicle Makes (14,301 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2024-02-01 to 2024-02-29 · Vehicle unit records
1,221 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (16,518 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2024-02-01 to 2024-02-29 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones was similar year-over-year, with 25 mph zones having the highest number of incidents in both periods (2,316 in 2024 vs. 2,211 in 2023). Fatal crashes increased in both lower and higher speed zones, rising from 6 to 8 in 25 mph zones and from 4 to 6 in 65 mph zones. The fatal crash rate was highest in 65 mph zones in both years, increasing from 1.04% to 1.20%.
Fatal crashes by zone: 25 mph: 8 of 2,316 (0.345%) · 30 mph: 2 of 562 (0.356%) · 35 mph: 1 of 874 (0.114%) · 40 mph: 2 of 448 (0.446%) · 45 mph: 3 of 287 (1.045%) · 50 mph: 2 of 177 (1.13%) · 55 mph: 2 of 634 (0.315%) · 65 mph: 6 of 501 (1.198%)
Source: Connecticut Crash Data · Csv Open Data · 2024-02-01 to 2024-02-29 · 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: 2024-02-01 through 2024-02-29
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2024-02-01 through 2024-02-29 (29 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 7,655
- Total persons involved: 17,976
- Total vehicles involved: 14,301
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: February 2024." Published August 20, 2026. Reporting period: 2024-02-01 to 2024-02-29. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/february-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: 2024-02-01 – 2024-02-29
Generated: August 20, 2026 · All rights reserved
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