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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
6,258 CRASHES IN
CONNECTICUT, CT
2024
In New London County, total traffic crashes increased by 8.4% from 5,775 in 2023 to 6,258 in 2024. During this same period, the number of fatalities rose from 30 to 38, an increase of 26.7%. The most notable shift was a 51.3% increase in motorcycle-involved crashes, which grew from 80 in the prior year to 121 in the current year.
6,258
▲ 8.4%was 5,775
Total Crash Events
38
▲ 26.7%was 30
Persons Killed
1,842
▼ -3.6%was 1,910
Persons Injured
851
▲ 23.7%was 688
Hit-and-Run Crashes
Note: "Persons Killed" (38) counts individual fatalities across all crash events. "Fatal" in the severity table below (36) 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, the trend in New London County shows a rise in crash incidents year-over-year. Total crashes increased by 8.4%, from 5,775 to 6,258. While total injuries saw a slight decrease of 3.6% (from 1,910 to 1,842), the number of fatalities increased by 26.7% (from 30 to 38).
851
Hit-and-Run Crashes — 2024
▲ 23.7% vs prior (688)
Hit-and-run crashes increased significantly, rising 23.7% from 688 incidents in the prior year to 851 in the current year. This increase outpaced the overall growth in crashes. As a result, the hit-and-run rate, which measures the proportion of all crashes that are hit-and-runs, trended upward from 11.9% to 13.6%.
Vulnerable Road User Casualties
3
Pedestrians Killed
0
Cyclists Killed
35
Motorists Killed
58
Pedestrians Injured
29
Cyclists Injured
1,755
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 broadly similar year-over-year. Friday was the peak day for crashes in both periods, though the total count on that day decreased slightly from 995 to 967. The peak hour for collisions shifted an hour earlier, from 4 p.m. in the prior year (517 crashes) to 3 p.m. in the current year (598 crashes). Crash volumes saw notable year-over-year increases on Tuesdays and Saturdays.
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
The severity of crashes worsened slightly, with the proportion of fatal crashes increasing from 0.5% to 0.6% of all incidents. While the absolute number of crashes involving any injury (serious, minor, or possible) increased slightly from 1,364 to 1,383, their share of total crashes decreased from 23.6% to 22.1%. Consequently, the proportion of crashes resulting in no injury rose from 75.9% to 77.3% of all incidents.
Severity is per crash event (most severe injury). 36 fatal crash events resulted in 38 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 across different environmental conditions remained highly stable year-over-year. In both periods, approximately 81% of crashes occurred in clear weather and 70% took place during daylight hours. Crashes on dry road surfaces accounted for 81.4% of the total in the current year, a slight increase from 80.7% in the prior year, while the proportion of crashes on wet surfaces declined from 15.4% to 13.4%.
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 three vehicle makes involved in collisions were consistent across both periods: Ford, Toyota, and Honda. While the number of Fords involved remained flat, Toyota and Honda saw their involvement increase by 19.1% and 20.5% respectively. The age distribution of persons involved in crashes was also stable, with the 26-34 age group representing the largest share in both years (15.5% in the prior period vs. 15.9% in the current period).
Top Vehicle Makes (11,214 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
815 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (13,744 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
Crash volumes increased across most speed zones, with the largest absolute gains in zones posted at 25 mph (from 1,967 to 2,213 crashes) and 35 mph (from 900 to 1,023 crashes). There was a significant shift in where fatal crashes occurred; fatalities in 25 mph zones rose sharply from 5 to 14. Conversely, fatal crashes in 40 mph zones decreased from 6 to 1 year-over-year.
Fatal crashes by zone: 1 mph: 3 of 281 (1.068%) · 25 mph: 14 of 2,213 (0.633%) · 30 mph: 3 of 408 (0.735%) · 35 mph: 3 of 1,023 (0.293%) · 40 mph: 1 of 252 (0.397%) · 45 mph: 2 of 611 (0.327%) · 50 mph: 2 of 184 (1.087%) · 55 mph: 1 of 196 (0.51%) · 65 mph: 4 of 828 (0.483%) · 88 mph: 3 of 88 (3.409%)
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: August 21, 2026
Data Coverage
- Reporting period: 2024-01-01 through 2024-12-31 (366 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 6,258
- Total persons involved: 14,705
- Total vehicles involved: 11,214
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 August 21, 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: August 21, 2026 · All rights reserved
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