ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · 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/2024-annual-report
Yearly Traffic Safety Analysis
3,895 CRASHES IN
CONNECTICUT, CT
2024
In Litchfield County, total traffic crashes increased by 6.2% from 3,666 in 2023 to 3,895 in 2024. While the number of collisions and injuries rose slightly, the most significant year-over-year change was a 48% decrease in total fatalities, which fell from 27 to 14. This suggests a shift in crash severity despite the higher overall volume.
3,895
▲ 6.2%was 3,666
Total Crash Events
14
▼ -48.1%was 27
Persons Killed
1,253
▲ 1.9%was 1,230
Persons Injured
293
▲ 22.1%was 240
Hit-and-Run Crashes
Note: "Persons Killed" (14) counts individual fatalities across all crash events. "Fatal" in the severity table below (13) 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 2024-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash trends in Litchfield County show a 6.2% increase in total collisions year-over-year, rising from 3,666 to 3,895. While total injuries remained relatively stable with a 1.9% increase, there was a substantial 48.1% reduction in fatalities, dropping from 27 in the prior year to 14 in the current year.
293
Hit-and-Run Crashes — 2024
▲ 22.1% vs prior (240)
Hit-and-run incidents trended upward in Litchfield County. The absolute number of hit-and-run crashes increased from 240 in the prior year to 293 in the current year. The hit-and-run rate, as a percentage of all crashes, also rose from 6.5% to 7.5% over the same period.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
13
Motorists Killed
27
Pedestrians Injured
13
Cyclists Injured
1,213
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · 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, with Friday being the peak day for collisions in both periods (594 in 2023 vs. 670 in 2024). The peak hour for crashes shifted slightly earlier, from the 5 p.m. hour in the prior year (327 crashes) to the 4 p.m. hour in the current year (333 crashes). The afternoon commute, from 3 p.m. to 5 p.m., continued to be the time block with the highest number of incidents in both years.
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While total crashes increased, the severity of those crashes decreased notably year-over-year. The number of fatal crashes was halved, falling from 26 to 13, and the fatal crash rate dropped from 0.7% to 0.3%. Conversely, serious injury crashes increased from 51 to 82, representing a rise from 1.4% to 2.1% of all crashes. The proportion of crashes resulting in no injury also saw a slight increase from 74.5% to 75.1%.
Severity is per crash event (most severe injury). 13 fatal crash events resulted in 14 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record
Road & Environmental Conditions
The distribution of crashes by environmental conditions showed minimal change, with clear weather and dry road surfaces accounting for the vast majority of incidents in both years. Crashes in daylight comprised approximately 71% of the total in both periods. However, collisions occurring in snow more than doubled, increasing from 107 incidents in the prior year to 214 in the current year, and crashes on snowy road surfaces followed a similar trend, rising from 85 to 181.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field
Vehicles & Demographics
The top five vehicle makes involved in crashes—Ford, Honda, Toyota, Subaru, and Chevrolet—remained the same across both years, with only minor changes in their ranking. A notable demographic shift occurred among persons involved in crashes; the number of individuals aged 65 and older increased from 1,132 to 1,315. Conversely, involvement of the 16-20 age group decreased from 958 to 891 persons.
Top Vehicle Makes (6,575 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
322 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (7,483 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes increased across most speed zones year-over-year. In the current period, the highest number of fatal crashes (6 of 13) occurred in 45 mph zones, which was also the zone with the most fatalities in the prior year (7 of 26). However, fatalities in the current year were more concentrated, with no fatal crashes recorded in 30 mph or 35 mph zones, unlike the prior year which saw a combined 8 fatalities in those zones.
Fatal crashes by zone: 25 mph: 2 of 891 (0.224%) · 40 mph: 3 of 460 (0.652%) · 45 mph: 6 of 363 (1.653%) · 65 mph: 1 of 225 (0.444%) · 99 mph: 1 of 62 (1.613%)
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
- Report generated: September 10, 2026
Data Coverage
- Reporting period: 2024-01-01 through 2024-12-31 (366 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 3,895
- Total persons involved: 8,335
- Total vehicles involved: 6,575
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: 2024." Published September 10, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2024-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Connecticut Crash Data · Csv
Period: 2024-01-01 – 2024-12-31
Generated: September 10, 2026 · All rights reserved