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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · SEPTEMBER 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/september-2024-report
Monthly Traffic Safety Analysis
8,490 CRASHES IN
CONNECTICUT, CT
SEPTEMBER 2024
In September 2024, Connecticut recorded 8,490 traffic crashes, a 2.6% decrease from the 8,714 crashes in September 2023. While total fatalities remained unchanged at 25, injuries fell by 3.7% from 2,992 to 2,881. The most notable year-over-year change was a significant increase in crashes involving vulnerable road users, with bicycle-related crashes rising by 35.4% and motorcycle-related crashes increasing by 29.6%.
8,490
▼ -2.6%was 8,714
Total Crash Events
25
Persons Killed
2,881
▼ -3.7%was 2,992
Persons Injured
1,099
▲ 5.0%was 1,047
Hit-and-Run Crashes
Note: "Persons Killed" (25) counts individual fatalities across all crash events. "Fatal" in the severity table below (23) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2024-09-01 to 2024-09-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic safety trends showed a slight improvement year-over-year. Total crashes decreased by 2.6% (from 8,714 to 8,490) and total injuries fell by 3.7% (from 2,992 to 2,881). The number of fatalities, however, remained constant at 25 for both September 2023 and September 2024.
1,099
Hit-and-Run Crashes — September 2024
▲ 5.0% vs prior (1,047)
Hit-and-run incidents trended upward in September 2024 compared to the previous year. The total number of hit-and-run crashes increased from 1,047 to 1,099. Because this increase occurred alongside a decrease in total crashes, the hit-and-run rate rose more significantly, climbing from 12.0% to 12.9% of all reported crashes.
Vulnerable Road User Casualties
1
Pedestrians Killed
1
Cyclists Killed
23
Motorists Killed
112
Pedestrians Injured
44
Cyclists Injured
2,725
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2024-09-01 to 2024-09-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The timing of crashes showed a mixed pattern compared to the prior year. The peak day for crashes remained Friday and the peak hour remained 3 p.m. in both periods. However, crash volume on Friday decreased from 1,709 to 1,331, while crashes on Thursday increased, making it the second-highest day in the current period. Crashes on Sunday also saw an increase from 976 to 1,113 year-over-year.
Source: Connecticut Crash Data · Csv Open Data · 2024-09-01 to 2024-09-30 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2024-09-01 to 2024-09-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes remained largely stable between the two periods. The fatal crash rate saw a minor decrease from 0.28% to 0.27% of all crashes. The proportion of crashes resulting in any injury was nearly identical, at 24.6% in September 2024 compared to 24.5% in the prior year. However, the number of crashes classified as resulting in a 'Serious Injury' increased by 9%, from 111 to 121 incidents.
Severity is per crash event (most severe injury). 23 fatal crash events resulted in 25 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2024-09-01 to 2024-09-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2024-09-01 to 2024-09-30 · Most severe injury per crash record
Road & Environmental Conditions
Crash conditions were markedly different year-over-year, primarily due to weather. In September 2024, 90% of crashes occurred in clear weather and 91% on dry roads. This contrasts sharply with September 2023, when only 73% of crashes were in clear weather and 26% of all incidents occurred on wet roads. The proportion of crashes happening in daylight versus darkness remained consistent across both periods.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2024-09-01 to 2024-09-30 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2024-09-01 to 2024-09-30 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2024-09-01 to 2024-09-30 · Road surface condition field
Vehicles & Demographics
The makes of vehicles involved in crashes showed high consistency year-over-year, with Toyota, Honda, and Ford remaining the top three most common makes in both periods. Toyota (1,803) and Honda (1,753) swapped the top two positions from the prior year. The age distribution of persons involved in crashes also remained stable, with the 26-34 age group consistently representing the largest cohort in both September 2024 and September 2023.
Top Vehicle Makes (16,207 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2024-09-01 to 2024-09-30 · Vehicle unit records
1,353 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (19,317 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2024-09-01 to 2024-09-30 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes by speed zone saw minor shifts compared to the prior year. Crashes in 55 mph zones increased from 769 to 855, while crashes in 25 mph zones decreased slightly from 2,477 to 2,435. Notably, fatal crashes in 25 mph zones dropped from 7 to 4. Conversely, the number of fatal crashes in 40 mph zones rose from 3 to 5.
Fatal crashes by zone: 1 mph: 2 of 1,102 (0.181%) · 25 mph: 4 of 2,435 (0.164%) · 30 mph: 3 of 640 (0.469%) · 35 mph: 4 of 898 (0.445%) · 40 mph: 5 of 508 (0.984%) · 50 mph: 1 of 235 (0.426%) · 55 mph: 2 of 855 (0.234%) · 65 mph: 2 of 551 (0.363%)
Source: Connecticut Crash Data · Csv Open Data · 2024-09-01 to 2024-09-30 · 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: 2024-09-01 through 2024-09-30
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2024-09-01 through 2024-09-30 (30 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 8,490
- Total persons involved: 20,916
- Total vehicles involved: 16,207
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: September 2024." Published August 20, 2026. Reporting period: 2024-09-01 to 2024-09-30. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/september-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: 2024-09-01 – 2024-09-30
Generated: August 20, 2026 · All rights reserved
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