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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · SEPTEMBER 2019
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-2019-report
Monthly Traffic Safety Analysis
9,204 CRASHES IN
CONNECTICUT, CT
SEPTEMBER 2019
In September 2019, Connecticut recorded 9,204 total traffic crashes, a 1.6% increase from the 9,063 crashes reported in September 2018. While total fatalities remained unchanged at 23 for both periods, the most notable year-over-year change was a 43.5% decrease in crashes where speeding was a factor, which fell from 754 to 426.
9,204
▲ 1.6%was 9,063
Total Crash Events
23
Persons Killed
3,197
▲ 2.5%was 3,119
Persons Injured
1,057
▲ 2.8%was 1,028
Hit-and-Run Crashes
Note: "Persons Killed" (23) counts individual fatalities across all crash events. "Fatal" in the severity table below (21) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2019-09-01 to 2019-09-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Year-over-year data for September shows a slight increase in traffic collisions across Connecticut. Total crashes rose by 1.6%, from 9,063 in September 2018 to 9,204 in September 2019. Similarly, the number of people injured in these incidents increased by 2.5% to 3,197, while the number of fatalities held steady at 23 for both months.
1,057
Hit-and-Run Crashes — September 2019
▲ 2.8% vs prior (1,028)
Hit-and-run incidents increased in both absolute numbers and as a percentage of total crashes. In September 2019, there were 1,057 hit-and-run crashes, up from 1,028 in September 2018. This represents a slight upward trend in the hit-and-run rate, which rose from 11.3% to 11.5% of all collisions year-over-year.
Vulnerable Road User Casualties
2
Pedestrians Killed
0
Cyclists Killed
21
Motorists Killed
110
Pedestrians Injured
58
Cyclists Injured
3,029
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2019-09-01 to 2019-09-30 · 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 distinct shift between September 2018 and September 2019. The peak day for collisions moved from Tuesday (1,488 crashes) in the prior year to Friday (1,486 crashes) in the current year. The busiest hour also shifted slightly later in the afternoon, from the 3 p.m. hour in 2018 to the 4 p.m. hour in 2019, which recorded 846 crashes.
Source: Connecticut Crash Data · Csv Open Data · 2019-09-01 to 2019-09-30 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2019-09-01 to 2019-09-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While the total number of fatalities was unchanged year-over-year, the severity of crashes showed some changes. The number of fatal crashes decreased from 23 to 21, even as the number of people killed remained 23. Crashes resulting in serious injuries saw a notable increase of 17.9%, rising from 95 incidents in September 2018 to 112 in September 2019. The proportion of crashes with no reported injuries remained stable at approximately 74% for both periods.
Severity is per crash event (most severe injury). 21 fatal crash events resulted in 23 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-09-01 to 2019-09-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-09-01 to 2019-09-30 · Most severe injury per crash record
Road & Environmental Conditions
Environmental conditions during crashes differed significantly between the two periods, primarily related to weather. In September 2019, 90.0% of crashes occurred in clear weather, compared to just 72.1% in September 2018. Correspondingly, crashes on wet roads plummeted, accounting for only 7.8% of incidents in 2019 versus 23.5% in the prior year. The proportion of crashes occurring in daylight remained consistent at approximately 75% for both periods.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2019-09-01 to 2019-09-30 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2019-09-01 to 2019-09-30 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2019-09-01 to 2019-09-30 · Road surface condition field
Vehicles & Demographics
The composition of vehicles involved in crashes remained largely stable year-over-year. The top five vehicle makes involved in collisions were identical in both September 2018 and September 2019: Honda, Toyota, Ford, Nissan, and Chevrolet, with only minor fluctuations in their total counts. Similarly, the age distribution of all persons involved in crashes showed a consistent pattern, with the 26-34 age group representing the largest share in both periods.
Top Vehicle Makes (17,679 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2019-09-01 to 2019-09-30 · Vehicle unit records
1,490 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (21,930 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2019-09-01 to 2019-09-30 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across different speed zones showed minor changes, with most collisions in both periods occurring in zones posted at 25 mph (2,936 in 2019 vs. 2,787 in 2018). However, the location of fatal crashes shifted; fatal crashes in 25 mph zones decreased from 6 to 3. Conversely, fatalities increased in 30 mph zones (from 4 to 5) and 65 mph zones (from 1 to 3) compared to the prior year.
Fatal crashes by zone: 1 mph: 1 of 1,080 (0.093%) · 25 mph: 3 of 2,936 (0.102%) · 30 mph: 5 of 687 (0.728%) · 35 mph: 3 of 1,038 (0.289%) · 40 mph: 2 of 541 (0.37%) · 45 mph: 2 of 314 (0.637%) · 55 mph: 1 of 801 (0.125%) · 65 mph: 3 of 496 (0.605%) · 88 mph: 1 of 642 (0.156%)
Source: Connecticut Crash Data · Csv Open Data · 2019-09-01 to 2019-09-30 · 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: 2019-09-01 through 2019-09-30
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2019-09-01 through 2019-09-30 (30 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 9,204
- Total persons involved: 23,406
- Total vehicles involved: 17,679
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 2019." Published August 20, 2026. Reporting period: 2019-09-01 to 2019-09-30. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/september-2019-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: 2019-09-01 – 2019-09-30
Generated: August 20, 2026 · All rights reserved
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