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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · SEPTEMBER 2022
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-2022-report
Monthly Traffic Safety Analysis
8,540 CRASHES IN
CONNECTICUT, CT
SEPTEMBER 2022
In September 2022, Connecticut recorded 8,540 total traffic crashes, a 9.6% decrease from the 9,446 crashes that occurred in September 2021. Despite this overall reduction in collisions and a 7.7% drop in injuries, the number of fatalities increased slightly from 27 to 28 over the same period.
8,540
▼ -9.6%was 9,446
Total Crash Events
28
▲ 3.7%was 27
Persons Killed
2,997
▼ -7.7%was 3,246
Persons Injured
1,024
▼ -13.7%was 1,187
Hit-and-Run Crashes
Note: "Persons Killed" (28) counts individual fatalities across all crash events. "Fatal" in the severity table below (25) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2022-09-01 to 2022-09-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic crashes in Connecticut showed a downward trend in September 2022 compared to the same month in the prior year. Total collisions fell by 9.6%, from 9,446 to 8,540. Similarly, the number of people injured decreased by 7.7% (from 3,246 to 2,997), while the number of fatalities saw a slight increase from 27 to 28.
1,024
Hit-and-Run Crashes — September 2022
▼ -13.7% vs prior (1,187)
The number of hit-and-run crashes decreased in September 2022 compared to the same month in 2021. There were 1,024 hit-and-run incidents, down from 1,187 in the prior year, representing a 13.7% reduction. The hit-and-run rate, or the percentage of total crashes that were hit-and-runs, also saw a slight decrease, falling from 12.6% to 12.0%.
Vulnerable Road User Casualties
3
Pedestrians Killed
0
Cyclists Killed
25
Motorists Killed
110
Pedestrians Injured
34
Cyclists Injured
2,853
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2022-09-01 to 2022-09-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes showed a shift in the peak day of the week between the two periods. In September 2022, Friday was the day with the most crashes (1,638), whereas in September 2021, Wednesday had the highest volume (1,706). The peak hour for crashes remained consistent year-over-year, occurring at 4 p.m. in both periods, though the number of crashes during that hour decreased from 821 to 738.
Source: Connecticut Crash Data · Csv Open Data · 2022-09-01 to 2022-09-30 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2022-09-01 to 2022-09-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The number of fatal crashes remained unchanged at 25 in September 2022 compared to September 2021, though the total number of fatalities increased from 27 to 28. The counts for all injury-related crash categories decreased year-over-year, with serious injury crashes dropping from 131 to 121. Crashes resulting in no injury also declined from 7,075 to 6,320, consistent with the overall decrease in total crashes.
Severity is per crash event (most severe injury). 25 fatal crash events resulted in 28 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2022-09-01 to 2022-09-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2022-09-01 to 2022-09-30 · Most severe injury per crash record
Road & Environmental Conditions
The distribution of crashes across different environmental conditions remained largely stable year-over-year. In both September 2022 and September 2021, the vast majority of crashes occurred in clear weather (85.0% and 83.8%, respectively) and on dry roads (85.4% and 83.5%). The proportion of crashes happening during daylight hours was also consistent, accounting for 72.7% of crashes in the current period and 73.6% in the prior period.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2022-09-01 to 2022-09-30 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2022-09-01 to 2022-09-30 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2022-09-01 to 2022-09-30 · Road surface condition field
Vehicles & Demographics
The top five vehicle makes involved in crashes remained unchanged between September 2021 and September 2022, with Honda, Toyota, Ford, Nissan, and Chevrolet leading in both periods, although the total count for each make decreased. The age distribution of persons involved in crashes also showed consistency. The 26-34 age group constituted the largest share of individuals in both years, accounting for 16.8% of persons in the current period and 17.6% in the prior period.
Top Vehicle Makes (16,180 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2022-09-01 to 2022-09-30 · Vehicle unit records
1,407 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (19,158 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2022-09-01 to 2022-09-30 · Person-level records linked to crash events
Speed Limit Zones
Crashes decreased in number across most posted speed limit zones in September 2022 compared to the previous year, reflecting the overall decline in collisions. There was no significant shift in the distribution of crashes toward higher or lower speed zones. However, the concentration of fatal crashes changed; in September 2022, zones with a 40 mph speed limit saw the highest number of fatal crashes (7), up from 4 in the prior year. In September 2021, 35 mph zones had the most fatal crashes with 5.
Fatal crashes by zone: 1 mph: 1 of 1,039 (0.096%) · 25 mph: 1 of 2,513 (0.04%) · 30 mph: 3 of 651 (0.461%) · 35 mph: 4 of 920 (0.435%) · 40 mph: 7 of 521 (1.344%) · 45 mph: 3 of 339 (0.885%) · 55 mph: 2 of 829 (0.241%) · 65 mph: 4 of 498 (0.803%)
Source: Connecticut Crash Data · Csv Open Data · 2022-09-01 to 2022-09-30 · 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: 2022-09-01 through 2022-09-30
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2022-09-01 through 2022-09-30 (30 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 8,540
- Total persons involved: 20,689
- Total vehicles involved: 16,180
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 2022." Published August 20, 2026. Reporting period: 2022-09-01 to 2022-09-30. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/september-2022-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: 2022-09-01 – 2022-09-30
Generated: August 20, 2026 · All rights reserved
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