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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · SEPTEMBER 2023
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-2023-report
Monthly Traffic Safety Analysis
8,714 CRASHES IN
CONNECTICUT, CT
SEPTEMBER 2023
In September 2023, there were 8,714 total traffic crashes statewide, a 2.0% increase from the 8,540 crashes recorded in September 2022. While overall crash volume saw a modest rise, the most notable year-over-year shift occurred in driving conditions, where the number of crashes happening in rainy weather more than doubled from 915 to 1,918.
8,714
▲ 2.0%was 8,540
Total Crash Events
25
▼ -10.7%was 28
Persons Killed
2,992
▼ -0.2%was 2,997
Persons Injured
1,047
▲ 2.2%was 1,024
Hit-and-Run Crashes
Note: "Persons Killed" (25) counts individual fatalities across all crash events. "Fatal" in the severity table below (24) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2023-09-01 to 2023-09-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash trends show a slight increase in volume year-over-year, with total incidents rising by 174 from September 2022 to September 2023. Despite this increase in crashes, total fatalities decreased from 28 to 25, and the number of injuries remained stable, declining marginally from 2,997 to 2,992.
1,047
Hit-and-Run Crashes — September 2023
▲ 2.2% vs prior (1,024)
Hit-and-run incidents saw a slight increase in absolute numbers, rising from 1,024 in September 2022 to 1,047 in September 2023. However, as a proportion of all crashes, the hit-and-run rate remained unchanged at 12.0% for both periods. This indicates that the frequency of hit-and-run crashes grew in line with the overall increase in traffic incidents.
Vulnerable Road User Casualties
5
Pedestrians Killed
0
Cyclists Killed
20
Motorists Killed
94
Pedestrians Injured
42
Cyclists Injured
2,856
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2023-09-01 to 2023-09-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes remained largely consistent between the two periods. Friday was the peak day for crashes in both September 2022 (1,638 crashes) and September 2023 (1,709 crashes). The peak hour for collisions shifted slightly earlier, from the 4 p.m. hour in the prior year (738 crashes) to the 3 p.m. hour in the current year (743 crashes).
Source: Connecticut Crash Data · Csv Open Data · 2023-09-01 to 2023-09-30 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2023-09-01 to 2023-09-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The overall severity of crashes showed a slight decrease year-over-year. The fatal crash rate per 100 incidents declined from 0.29 to 0.28. The proportion of crashes resulting in any level of injury (Serious, Minor, or Possible) decreased from 25.7% in September 2022 to 24.4% in September 2023, while the share of no-injury crashes increased from 74.0% to 75.3%.
Severity is per crash event (most severe injury). 24 fatal crash events resulted in 25 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2023-09-01 to 2023-09-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2023-09-01 to 2023-09-30 · Most severe injury per crash record
Road & Environmental Conditions
There was a significant year-over-year shift in the conditions under which crashes occurred. The number of crashes in rainy weather more than doubled, increasing from 915 in September 2022 to 1,918 in September 2023, raising their share of total crashes from 10.7% to 22.0%. Consequently, crashes on wet road surfaces also rose sharply from 1,181 to 2,302. In contrast, the distribution of crashes by lighting condition remained stable, with daylight crashes accounting for approximately 73% of incidents in both periods.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2023-09-01 to 2023-09-30 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2023-09-01 to 2023-09-30 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2023-09-01 to 2023-09-30 · Road surface condition field
Vehicles & Demographics
The top vehicle makes involved in crashes were consistent across both periods, with Honda, Toyota, and Ford leading in volume. The number of vehicles from these top three makes involved in crashes increased in September 2023, with Hondas rising from 1,827 to 1,865 and Toyotas from 1,593 to 1,748. The age distribution of all persons involved also remained stable, with the 26-34 age group being the largest cohort in both September 2022 (3,484 persons) and September 2023 (3,526 persons).
Top Vehicle Makes (16,398 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2023-09-01 to 2023-09-30 · Vehicle unit records
1,445 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (19,550 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2023-09-01 to 2023-09-30 · Person-level records linked to crash events
Speed Limit Zones
Crash distribution by speed limit showed notable shifts in severity. The 25 mph zone, while seeing a slight dip in total crashes from 2,513 to 2,477, experienced a significant increase in fatalities from 1 to 7 year-over-year. Crashes in 65 mph zones increased from 498 to 560, but associated fatalities fell from 4 to 3. Meanwhile, incidents in 55 mph zones decreased from 829 to 769, with fatalities increasing slightly from 2 to 3.
Fatal crashes by zone: 25 mph: 7 of 2,477 (0.283%) · 30 mph: 2 of 720 (0.278%) · 35 mph: 5 of 980 (0.51%) · 40 mph: 3 of 460 (0.652%) · 45 mph: 1 of 347 (0.288%) · 55 mph: 3 of 769 (0.39%) · 65 mph: 3 of 560 (0.536%)
Source: Connecticut Crash Data · Csv Open Data · 2023-09-01 to 2023-09-30 · 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: 2023-09-01 through 2023-09-30
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2023-09-01 through 2023-09-30 (30 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 8,714
- Total persons involved: 21,118
- Total vehicles involved: 16,398
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 2023." Published August 20, 2026. Reporting period: 2023-09-01 to 2023-09-30. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/september-2023-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: 2023-09-01 – 2023-09-30
Generated: August 20, 2026 · All rights reserved
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