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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
28,710 CRASHES IN
CONNECTICUT, CT
2019
In Hartford County, total traffic crashes remained stable, with 28,710 incidents in 2019 compared to 28,787 in 2018, a decrease of less than 1%. While overall crash volume was consistent, the most notable year-over-year shift was a 12.3% decrease in total fatalities, which fell from 73 to 64. This included a significant 48% reduction in pedestrian fatalities, from 25 in the prior period to 13 in the current period.
28,710
▼ -0.3%was 28,787
Total Crash Events
64
▼ -12.3%was 73
Persons Killed
10,306
▼ -1.3%was 10,441
Persons Injured
3,733
▲ 3.1%was 3,620
Hit-and-Run Crashes
Note: "Persons Killed" (64) counts individual fatalities across all crash events. "Fatal" in the severity table below (61) 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
Overall crash trends in Hartford County were relatively stable year-over-year, with total crashes decreasing by only 0.27%. However, the severity of outcomes improved, as total injuries declined by 1.3% from 10,441 to 10,306, and total fatalities saw a more significant drop of 12.3%, from 73 to 64.
3,733
Hit-and-Run Crashes — 2019
▲ 3.1% vs prior (3,620)
Hit-and-run incidents trended upward in 2019. The total number of hit-and-run crashes increased from 3,620 in 2018 to 3,733 in 2019. This change pushed the hit-and-run rate up from 12.6% of all crashes in the prior period to 13.0% in the current period.
Vulnerable Road User Casualties
13
Pedestrians Killed
0
Cyclists Killed
51
Motorists Killed
0
Other Killed
339
Pedestrians Injured
112
Cyclists Injured
9,848
Motorists Injured
7
Other 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 showed strong consistency between 2018 and 2019. Friday was the day with the highest number of crashes in both periods, accounting for 4,841 incidents in 2018 and 4,801 in 2019. Similarly, the 5 p.m. hour remained the peak time for crashes, though the count in that hour decreased from 2,847 to 2,643.
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
The number of fatal crashes decreased from 68 in 2018 to 61 in 2019, with the fatal crash rate falling from 0.24% to 0.21%. The distribution of injury-related crashes remained nearly identical across both years. Crashes involving serious, minor, or possible injuries collectively accounted for 25.8% of all incidents in 2019, compared to 25.9% in 2018.
Severity is per crash event (most severe injury). 61 fatal crash events resulted in 64 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
Crash conditions were largely similar between 2018 and 2019, with the vast majority of incidents in both years occurring in clear weather and during daylight hours. There was a decrease in crashes on wet road surfaces, which fell from 5,561 in 2018 to 4,836 in 2019. This corresponds to a similar reduction in crashes that occurred during rainy conditions, which dropped from 3,724 to 3,168.
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 top vehicle makes involved in crashes were consistent year-over-year, with Honda and Toyota leading in both periods. In 2019, Honda was involved in 6,973 crashes and Toyota in 6,368, up from 6,704 and 6,031 respectively in 2018. The primary change in rankings was Nissan (5,008 crashes) overtaking Ford (4,876 crashes) for the third position. The age distribution of persons involved in crashes also remained stable, with the 26-34 age group being the largest in both years.
Top Vehicle Makes (55,142 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records
5,565 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (69,306 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 was consistent year-over-year, with 25 mph zones having the most crashes in both 2018 (5,382) and 2019 (5,535). A notable shift occurred in fatal crash locations; fatalities in 25 mph zones dropped from 19 to 6. In contrast, the number of fatalities in 30 mph zones remained high (11 in 2018 and 12 in 2019), and fatal crashes in 65 mph zones increased from 8 to 10.
Fatal crashes by zone: 1 mph: 5 of 2,219 (0.225%) · 20 mph: 1 of 70 (1.429%) · 25 mph: 6 of 5,535 (0.108%) · 30 mph: 12 of 3,056 (0.393%) · 35 mph: 4 of 4,408 (0.091%) · 40 mph: 4 of 2,283 (0.175%) · 45 mph: 5 of 884 (0.566%) · 50 mph: 5 of 1,979 (0.253%) · 55 mph: 3 of 1,337 (0.224%) · 65 mph: 10 of 2,479 (0.403%) · 88 mph: 5 of 3,220 (0.155%) · 99 mph: 1 of 640 (0.156%)
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 21, 2026
Data Coverage
- Reporting period: 2019-01-01 through 2019-12-31 (365 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 28,710
- Total persons involved: 74,252
- Total vehicles involved: 55,142
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 21, 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 21, 2026 · All rights reserved
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