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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · DECEMBER 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/december-2024-report
Monthly Traffic Safety Analysis
9,783 CRASHES IN
CONNECTICUT, CT
DECEMBER 2024
In December 2024, Connecticut recorded 9,783 total traffic crashes, a 7.4% increase from the 9,109 crashes in December 2023. Despite the rise in overall collisions, the most significant year-over-year change was a 50% reduction in traffic fatalities, which fell from 26 to 13. Concurrently, crashes resulting in serious injuries increased from 88 in the prior period to 157.
9,783
▲ 7.4%was 9,109
Total Crash Events
13
▼ -50.0%was 26
Persons Killed
2,946
▲ 0.2%was 2,940
Persons Injured
1,155
▲ 8.3%was 1,066
Hit-and-Run Crashes
Note: "Persons Killed" (13) counts individual fatalities across all crash events. "Fatal" in the severity table below (15) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2024-12-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash trends for December showed a 7.4% increase in total collisions compared to the same month last year, rising from 9,109 to 9,783. In a positive development, total fatalities were halved, dropping from 26 to 13. The total number of injuries remained stable, with 2,946 this period compared to 2,940 in the prior year.
1,155
Hit-and-Run Crashes — December 2024
▲ 8.3% vs prior (1,066)
The number of hit-and-run incidents increased from 1,066 in December 2023 to 1,155 in December 2024, tracking with the overall rise in total crashes. The hit-and-run rate, however, remained nearly flat, moving from 11.7% to 11.8% of all crashes. This indicates that the frequency of drivers leaving the scene of a crash did not change significantly as a proportion of total incidents.
Vulnerable Road User Casualties
4
Pedestrians Killed
0
Cyclists Killed
9
Motorists Killed
135
Pedestrians Injured
16
Cyclists Injured
2,795
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2024-12-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The time of day when crashes were most frequent remained consistent, with the 5 p.m. hour being the peak for both December 2024 (1,004 crashes) and December 2023 (954 crashes). However, the peak day for crashes shifted from Friday (1,776 crashes) in the prior year to Monday (1,849 crashes) in the current period. Overall crash volumes during the weekday evening commute from 4 p.m. to 6 p.m. increased from 1,703 to 1,773.
Source: Connecticut Crash Data · Csv Open Data · 2024-12-01 to 2024-12-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2024-12-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Year-over-year, the severity of crashes showed a mixed pattern. The number of fatal crashes decreased from 26 to 15, and their share of all crashes fell from 0.3% to 0.2%. Conversely, crashes involving serious injuries rose sharply from 88 to 157, increasing their proportion from 1.0% to 1.6% of all incidents. The proportion of crashes with either minor or possible injuries decreased slightly from 22.5% to 20.6%.
Severity is per crash event (most severe injury). 15 fatal crash events resulted in 13 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2024-12-01 to 2024-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2024-12-01 to 2024-12-31 · Most severe injury per crash record
Road & Environmental Conditions
Driving conditions varied significantly between the two periods, primarily due to weather. In December 2024, crashes occurring in snow increased dramatically to 661 from just 3 in the prior year. Correspondingly, incidents on roads with snow or ice rose from a combined 191 to 713. As a result, the share of crashes on dry road surfaces fell from 74.0% in December 2023 to 67.9% in December 2024.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2024-12-01 to 2024-12-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2024-12-01 to 2024-12-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2024-12-01 to 2024-12-31 · Road surface condition field
Vehicles & Demographics
The profile of vehicles and persons involved in crashes remained largely unchanged year-over-year. The top five most frequently involved vehicle makes—Honda, Toyota, Ford, Nissan, and Chevrolet—were identical in both periods, with counts for each increasing in line with the overall rise in collisions. Similarly, the age distribution of persons involved was consistent, with the 26-34 age group representing the largest cohort in both December 2024 (16.9% of persons) and December 2023 (17.2% of persons).
Top Vehicle Makes (18,285 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2024-12-01 to 2024-12-31 · Vehicle unit records
1,322 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (21,293 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2024-12-01 to 2024-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across different speed zones was stable year-over-year, with most incidents occurring in 25 mph zones in both December 2024 (2,747 crashes) and December 2023 (2,601 crashes). However, the profile of fatal crashes shifted; fatalities in higher speed zones of 40 mph or more decreased from 12 to 3. In contrast, fatal crashes in 25 mph zones increased from 5 to 9, making it the zone with the most fatalities in the current period.
Fatal crashes by zone: 25 mph: 9 of 2,747 (0.328%) · 35 mph: 3 of 1,194 (0.251%) · 45 mph: 2 of 341 (0.587%) · 88 mph: 1 of 440 (0.227%)
Source: Connecticut Crash Data · Csv Open Data · 2024-12-01 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: 2024-12-01 through 2024-12-31
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2024-12-01 through 2024-12-31 (31 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 9,783
- Total persons involved: 22,945
- Total vehicles involved: 18,285
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 2024." Published August 20, 2026. Reporting period: 2024-12-01 to 2024-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/december-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: 2024-12-01 – 2024-12-31
Generated: August 20, 2026 · All rights reserved
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