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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · NOVEMBER 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/november-2019-report
Monthly Traffic Safety Analysis
9,738 CRASHES IN
CONNECTICUT, CT
NOVEMBER 2019
In November 2019, Connecticut recorded 9,738 total vehicle crashes, a 9.3% decrease from the 10,736 crashes reported in November 2018. The most significant year-over-year change was a substantial drop in traffic fatalities, which fell from 28 to 16, a 42.9% decrease. The total number of injuries also decreased from 3,308 to 3,087 during the same period.
9,738
▼ -9.3%was 10,736
Total Crash Events
16
▼ -42.9%was 28
Persons Killed
3,087
▼ -6.7%was 3,308
Persons Injured
1,142
▼ -1.0%was 1,153
Hit-and-Run Crashes
Note: "Persons Killed" (16) counts individual fatalities across all crash events. "Fatal" in the severity table below (16) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2019-11-01 to 2019-11-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic safety metrics showed improvement in November 2019 compared to the same month in the prior year. Total crashes declined by 9.3%, from 10,736 to 9,738. This downward trend was also reflected in casualties, with total injuries decreasing by 6.7% and fatalities dropping by 42.9%.
1,142
Hit-and-Run Crashes — November 2019
▼ -1.0% vs prior (1,153)
The total number of hit-and-run crashes remained relatively stable, decreasing slightly from 1,153 in November 2018 to 1,142 in November 2019. However, due to the overall reduction in total crashes, the hit-and-run rate increased. Hit-and-runs constituted 11.7% of all crashes in the current period, up from 10.7% in the prior year.
Vulnerable Road User Casualties
7
Pedestrians Killed
0
Cyclists Killed
9
Motorists Killed
0
Other Killed
125
Pedestrians Injured
28
Cyclists Injured
2,933
Motorists Injured
1
Other Injured
Source: Connecticut Crash Data · Csv Open Data · 2019-11-01 to 2019-11-30 · 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 some shifts between November 2018 and November 2019. While the 5 p.m. hour remained the peak time for collisions in both periods, the number of crashes during this hour decreased from 1,410 to 1,129. The most crash-prone day of the week changed from Thursday (2,181 crashes) in 2018 to Friday (1,993 crashes) in 2019.
Source: Connecticut Crash Data · Csv Open Data · 2019-11-01 to 2019-11-30 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2019-11-01 to 2019-11-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes saw a notable improvement, with the proportion of fatal incidents decreasing from 0.3% of all crashes in November 2018 to 0.2% in November 2019. In absolute terms, fatal crashes fell from 27 to 16. The share of serious injury crashes increased slightly from 0.7% to 0.8%, while the proportion of minor injury crashes remained stable at approximately 8.6%.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-11-01 to 2019-11-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-11-01 to 2019-11-30 · Most severe injury per crash record
Road & Environmental Conditions
Driving conditions were markedly different between the two periods, which may have influenced crash totals. In November 2019, 83.0% of crashes occurred in clear weather, compared to just 64.7% in November 2018. Correspondingly, the proportion of crashes in rain dropped from 18.3% to 9.5%, and crashes in snow fell from 6.9% to 0.2%. The share of crashes on dry roads also increased from 63.6% to 84.5% year-over-year.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2019-11-01 to 2019-11-30 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2019-11-01 to 2019-11-30 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2019-11-01 to 2019-11-30 · Road surface condition field
Vehicles & Demographics
The makes of vehicles involved in crashes remained consistent, with Honda, Toyota, and Ford being the top three in both November 2018 and 2019. In 2019, Toyota (1,814 vehicles) surpassed Ford (1,656 vehicles) for the second position, while Honda remained first with 2,012 vehicles involved. The age distribution of persons in crashes also showed a similar pattern year-over-year, with the 26-34 age group representing the largest cohort in both periods, though their count decreased from 4,619 to 4,022.
Top Vehicle Makes (18,629 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2019-11-01 to 2019-11-30 · Vehicle unit records
1,587 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (22,475 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2019-11-01 to 2019-11-30 · Person-level records linked to crash events
Speed Limit Zones
In both November 2018 and 2019, the 25 mph speed zone accounted for the highest number of crashes, with 3,018 and 2,927 incidents, respectively. Fatal crashes were distributed across various speed zones, with notable year-over-year decreases in the 25 mph zone (from 5 to 3 fatalities) and the 55 mph zone (from 5 to 2 fatalities). Conversely, the 30 mph zone saw an increase in fatalities from 2 to 4, even as total crashes in that zone decreased from 992 to 793.
Fatal crashes by zone: 25 mph: 3 of 2,927 (0.102%) · 30 mph: 4 of 793 (0.504%) · 35 mph: 2 of 1,196 (0.167%) · 40 mph: 1 of 613 (0.163%) · 50 mph: 1 of 277 (0.361%) · 55 mph: 2 of 909 (0.22%) · 65 mph: 2 of 525 (0.381%) · 88 mph: 1 of 587 (0.17%)
Source: Connecticut Crash Data · Csv Open Data · 2019-11-01 to 2019-11-30 · 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: 2019-11-01 through 2019-11-30
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2019-11-01 through 2019-11-30 (30 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 9,738
- Total persons involved: 24,109
- Total vehicles involved: 18,629
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: November 2019." Published August 20, 2026. Reporting period: 2019-11-01 to 2019-11-30. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/november-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: 2019-11-01 – 2019-11-30
Generated: August 20, 2026 · All rights reserved
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