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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · 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/2019-annual-report
Yearly Traffic Safety Analysis
2,131 CRASHES IN
CONNECTICUT, CT
2019
In 2019, Windham County recorded 2,131 total crashes, a marginal 0.5% increase from the 2,120 crashes in 2018. While the overall crash volume remained stable, the number of fatalities rose by 30.8%, from 13 in 2018 to 17 in 2019. This increase in fatalities occurred alongside a 7.8% decrease in total injuries, which fell from 733 to 676.
2,131
▲ 0.5%was 2,120
Total Crash Events
17
▲ 30.8%was 13
Persons Killed
676
▼ -7.8%was 733
Persons Injured
165
▼ -6.3%was 176
Hit-and-Run Crashes
Note: "Persons Killed" (17) 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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend in crash volume in Windham County was relatively stable, with total crashes increasing by just 0.5% from 2,120 in 2018 to 2,131 in 2019. However, the outcomes of these crashes shifted towards greater severity, as fatalities increased from 13 to 17. Conversely, the number of people injured decreased from 733 to 676.
165
Hit-and-Run Crashes — 2019
▼ -6.3% vs prior (176)
Hit-and-run incidents showed a downward trend in 2019 compared to the previous year. The total number of hit-and-run crashes decreased from 176 in 2018 to 165 in 2019. Correspondingly, the hit-and-run rate, which represents the percentage of total crashes that were hit-and-runs, fell from 8.3% to 7.7%.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
16
Motorists Killed
10
Pedestrians Injured
8
Cyclists Injured
658
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes saw a shift in the peak day of the week between the two periods. In 2018, Friday was the busiest day with 365 crashes, while in 2019, the peak shifted to Thursday with 340 crashes. The peak hour for collisions remained consistent year-over-year, occurring at 3 p.m. in both 2018 (176 crashes) and 2019 (187 crashes).
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity worsened in 2019 compared to the prior year, with the fatal crash rate increasing from 0.61 to 0.75 per 100 crashes. Fatal crashes accounted for 0.8% of all incidents in 2019, up from 0.6% in 2018. Concurrently, the proportion of all injury-related crashes (Serious, Minor, and Possible) decreased from a combined 26.0% in 2018 to 23.6% in 2019.
Severity is per crash event (most severe injury). 16 fatal crash events resulted in 17 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record
Road & Environmental Conditions
The environmental conditions during crashes remained largely consistent between 2018 and 2019. In both years, approximately 75% of crashes occurred in clear weather and about 71% happened on dry road surfaces. Crashes in daylight accounted for 66.7% of incidents in 2019, nearly identical to the 66.9% recorded in 2018, indicating no significant shift in crash patterns related to lighting, weather, or road surface conditions.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field
Vehicles & Demographics
The makes of vehicles involved in crashes were consistent, with Ford, Toyota, and Chevrolet remaining the top three most common makes in both 2018 and 2019. An analysis of persons involved shows the 26-34 age group was the most represented in both periods, with its count increasing from 775 to 821. The 16-20 age group saw a notable increase in involvement, rising from 593 individuals in 2018 to 642 in 2019.
Top Vehicle Makes (3,511 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records
137 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (4,504 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones remained similar, with 25 mph zones accounting for the highest volume in both years (560 in 2018 and 569 in 2019). However, the location of fatal crashes shifted; the 45 mph zone saw the most fatal crashes in 2019 with 7, an increase from 6 in the prior year. Additionally, fatal crashes in 30 mph zones increased from one in 2018 to four in 2019, indicating a concentration of fatal outcomes in these specific speed zones.
Fatal crashes by zone: 20 mph: 1 of 15 (6.667%) · 25 mph: 1 of 569 (0.176%) · 30 mph: 4 of 231 (1.732%) · 35 mph: 1 of 343 (0.292%) · 45 mph: 7 of 292 (2.397%) · 65 mph: 1 of 241 (0.415%)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2019-01-01 through 2019-12-31 (365 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 2,131
- Total persons involved: 4,672
- Total vehicles involved: 3,511
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: 2019." Published August 20, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2019-annual-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: 2019-01-01 – 2019-12-31
Generated: August 20, 2026 · All rights reserved
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