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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · FEBRUARY 2023
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-2023-report
Monthly Traffic Safety Analysis
7,567 CRASHES IN
CONNECTICUT, CT
FEBRUARY 2023
In February 2023, Connecticut recorded 7,567 total traffic crashes, a 1.7% decrease from the 7,695 crashes reported in February 2022. While overall crashes saw a slight decline, the most significant change was a 35.7% year-over-year decrease in total fatalities, which fell from 28 to 18. Total injuries, however, increased by 5.2% from 2,366 to 2,490.
7,567
▼ -1.7%was 7,695
Total Crash Events
18
▼ -35.7%was 28
Persons Killed
2,490
▲ 5.2%was 2,366
Persons Injured
942
▼ -2.5%was 966
Hit-and-Run Crashes
Note: "Persons Killed" (18) counts individual fatalities across all crash events. "Fatal" in the severity table below (17) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2023-02-01 to 2023-02-28 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash volume in Connecticut showed a slight year-over-year decrease of 1.7% between February 2022 and February 2023. This period saw a significant positive trend in road safety, with total fatalities dropping by 35.7% from 28 to 18. Conversely, the number of people injured in crashes rose by 5.2%, from 2,366 to 2,490.
942
Hit-and-Run Crashes — February 2023
▼ -2.5% vs prior (966)
Hit-and-run incidents saw a slight decline in both count and rate year-over-year. In February 2023, there were 942 hit-and-run crashes, down 2.5% from 966 in the same month of 2022. The hit-and-run rate, representing the percentage of all crashes that were hit-and-runs, also decreased slightly from 12.6% to 12.4%.
Vulnerable Road User Casualties
6
Pedestrians Killed
0
Cyclists Killed
12
Motorists Killed
85
Pedestrians Injured
12
Cyclists Injured
2,393
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2023-02-01 to 2023-02-28 · 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 2022 and February 2023. The day with the most crashes moved from Friday (1,244 crashes) in the prior period to Saturday (1,342 crashes) in the current period. Similarly, the peak hour for collisions shifted from the 3 p.m. hour, with 623 crashes, to the 12 p.m. hour, with 608 crashes.
Source: Connecticut Crash Data · Csv Open Data · 2023-02-01 to 2023-02-28 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2023-02-01 to 2023-02-28 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes saw a notable improvement year-over-year, with the fatal crash rate decreasing from 0.29% in February 2022 to 0.22% in February 2023. Crashes resulting in serious injuries also declined from 98 to 78. However, crashes involving minor injuries increased from 758 to 786, making up 10.4% of all crashes in the current period compared to 9.9% a year ago.
Severity is per crash event (most severe injury). 17 fatal crash events resulted in 18 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2023-02-01 to 2023-02-28 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2023-02-01 to 2023-02-28 · Most severe injury per crash record
Road & Environmental Conditions
Crash conditions appeared to reflect different weather patterns between the two periods. In February 2023, there was a significant increase in crashes occurring in snow, with 647 incidents compared to 392 in the prior year, and a corresponding rise in crashes on snowy road surfaces from 356 to 532. Conversely, crashes in rainy conditions and on wet roads saw a substantial decrease, falling from 735 to 384 and 1,300 to 903, respectively. Crashes in daylight and on dry roads remained the most common scenario in both periods.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2023-02-01 to 2023-02-28 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2023-02-01 to 2023-02-28 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2023-02-01 to 2023-02-28 · Road surface condition field
Vehicles & Demographics
The composition of vehicles and persons involved in crashes remained stable year-over-year. The top five vehicle makes involved in collisions were Honda, Toyota, Ford, Nissan, and Chevrolet in both February 2022 and February 2023, with counts for each make changing by less than 2%. The age distribution of individuals involved in crashes also showed little change, with the 26-34 age group consistently being the largest cohort.
Top Vehicle Makes (14,040 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2023-02-01 to 2023-02-28 · Vehicle unit records
1,122 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (16,571 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2023-02-01 to 2023-02-28 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across different speed zones showed a shift away from higher speed zones. Crashes in 55 mph zones decreased from 760 to 689, and those in 65 mph zones dropped from 495 to 386. Despite the lower crash volume in the 65 mph zone, the fatal crash rate within that zone increased from 0.61% to 1.04%. Crashes in 25 mph zones remained the most frequent, with nearly identical counts in both periods (2,211 vs 2,212).
Fatal crashes by zone: 25 mph: 6 of 2,211 (0.271%) · 30 mph: 1 of 552 (0.181%) · 35 mph: 2 of 896 (0.223%) · 40 mph: 2 of 454 (0.441%) · 50 mph: 1 of 174 (0.575%) · 65 mph: 4 of 386 (1.036%) · 99 mph: 1 of 40 (2.5%)
Source: Connecticut Crash Data · Csv Open Data · 2023-02-01 to 2023-02-28 · 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: 2023-02-01 through 2023-02-28
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2023-02-01 through 2023-02-28 (28 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 7,567
- Total persons involved: 17,851
- Total vehicles involved: 14,040
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 2023." Published August 20, 2026. Reporting period: 2023-02-01 to 2023-02-28. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/february-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Connecticut Crash Data · Csv
Period: 2023-02-01 – 2023-02-28
Generated: August 20, 2026 · All rights reserved
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