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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · FEBRUARY 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/february-2019-report
Monthly Traffic Safety Analysis
8,459 CRASHES IN
CONNECTICUT, CT
FEBRUARY 2019
In February 2019, Connecticut recorded 8,459 total crashes, a 2.4% increase from the 8,260 crashes reported in February 2018. Despite the rise in total collisions, the number of fatalities saw a significant decrease, falling 35% from 20 to 13 year-over-year. Total injuries also declined by 5.8%, from 2,647 in the prior period to 2,493 in the current period.
8,459
▲ 2.4%was 8,260
Total Crash Events
13
▼ -35.0%was 20
Persons Killed
2,493
▼ -5.8%was 2,647
Persons Injured
978
▲ 1.3%was 965
Hit-and-Run Crashes
Note: "Persons Killed" (13) counts individual fatalities across all crash events. "Fatal" in the severity table below (11) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2019-02-01 to 2019-02-28 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash trends for February show a mixed picture year-over-year. While the total number of crashes increased by 2.4% from 8,260 in 2018 to 8,459 in 2019, the severity of these incidents decreased. Fatalities dropped by 35% and total injuries declined by 5.8% compared to the same month in the prior year.
978
Hit-and-Run Crashes — February 2019
▲ 1.3% vs prior (965)
Hit-and-run incidents remained relatively stable year-over-year. The total number of hit-and-run crashes increased slightly from 965 in February 2018 to 978 in February 2019. However, as a proportion of all crashes, the hit-and-run rate saw a marginal decrease from 11.7% to 11.6%.
Vulnerable Road User Casualties
3
Pedestrians Killed
0
Cyclists Killed
10
Motorists Killed
0
Other Killed
109
Pedestrians Injured
11
Cyclists Injured
2,369
Motorists Injured
4
Other Injured
Source: Connecticut Crash Data · Csv Open Data · 2019-02-01 to 2019-02-28 · 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 February 2018 and February 2019. The day with the most crashes moved from Friday (1,422 incidents) in the prior year to Tuesday (1,440 incidents) in the current period. Similarly, the peak hour for collisions shifted from the 3 PM hour (626 crashes) to the 4 PM hour (722 crashes), indicating a change in the busiest time on the roads.
Source: Connecticut Crash Data · Csv Open Data · 2019-02-01 to 2019-02-28 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2019-02-01 to 2019-02-28 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes decreased in February 2019 compared to the previous year. The proportion of fatal crashes fell from 0.2% to 0.1% of all incidents, with the absolute number of fatal crashes dropping from 20 to 11. Crashes resulting in minor or possible injuries also saw a proportional decline, while no-injury crashes increased from 76.6% to 78.3% of the total.
Severity is per crash event (most severe injury). 11 fatal crash events resulted in 13 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-02-01 to 2019-02-28 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-02-01 to 2019-02-28 · Most severe injury per crash record
Road & Environmental Conditions
Crash conditions varied significantly year-over-year, reflecting different environmental factors. Crashes in rainy conditions dropped from 1,256 to 396, and those on wet road surfaces decreased from 2,039 to 1,113. Conversely, collisions during clear weather increased from 5,372 to 6,421, and crashes in snow increased from 540 to 738. The majority of crashes in both periods occurred in daylight on dry roads.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2019-02-01 to 2019-02-28 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2019-02-01 to 2019-02-28 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2019-02-01 to 2019-02-28 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes remained consistent, with Honda, Ford, and Toyota leading in both periods, though Honda moved from third to first place year-over-year with 1,566 vehicles involved. Analysis of persons involved shows the 26-34 age group was the most represented in both February 2019 (3,545 persons) and February 2018 (3,330 persons). The age distribution of individuals in crashes remained relatively stable across both periods.
Top Vehicle Makes (15,566 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2019-02-01 to 2019-02-28 · Vehicle unit records
1,346 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (18,772 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2019-02-01 to 2019-02-28 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones showed a slight shift towards higher-speed roads. While collisions in 25 mph zones decreased from 2,630 to 2,598, crashes in 55 mph and 65 mph zones increased. A significant change occurred in fatal crash locations; fatalities in 25 mph zones dropped from 9 to 1. Conversely, the 55 mph zone, which had zero fatal crashes in the prior period, recorded 3 in February 2019.
Fatal crashes by zone: 1 mph: 2 of 971 (0.206%) · 25 mph: 1 of 2,598 (0.038%) · 30 mph: 1 of 739 (0.135%) · 40 mph: 1 of 480 (0.208%) · 55 mph: 3 of 694 (0.432%) · 65 mph: 2 of 515 (0.388%) · 99 mph: 1 of 107 (0.935%)
Source: Connecticut Crash Data · Csv Open Data · 2019-02-01 to 2019-02-28 · 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: 2019-02-01 through 2019-02-28
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2019-02-01 through 2019-02-28 (28 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 8,459
- Total persons involved: 20,021
- Total vehicles involved: 15,566
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: February 2019." Published August 20, 2026. Reporting period: 2019-02-01 to 2019-02-28. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/february-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: 2019-02-01 – 2019-02-28
Generated: August 20, 2026 · All rights reserved
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