If you're a data point in this report, call us.
We'll evaluate whether you have a case. Free, and no pressure.
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
31,594 CRASHES IN
CONNECTICUT, CT
2019
In New Haven County, total traffic crashes decreased by 1.1% from 31,932 in 2018 to 31,594 in 2019. The most notable year-over-year shift was a significant 23.3% reduction in fatalities, which fell from 86 in the prior period to 66 in the current period. Total injuries remained nearly unchanged, with a slight decrease from 11,351 to 11,346.
31,594
▼ -1.1%was 31,932
Total Crash Events
66
▼ -23.3%was 86
Persons Killed
11,346
▼ -0.0%was 11,351
Persons Injured
3,907
▼ -2.3%was 4,001
Hit-and-Run Crashes
Note: "Persons Killed" (66) counts individual fatalities across all crash events. "Fatal" in the severity table below (64) 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 New Haven County shows a slight decrease in traffic collisions year-over-year. Total crashes fell by 338 incidents, a 1.1% reduction from 2018 to 2019. This decrease was accompanied by a more pronounced 23.3% drop in fatalities, from 86 to 66, while the number of injuries remained stable with a negligible 0.04% decrease.
3,907
Hit-and-Run Crashes — 2019
▼ -2.3% vs prior (4,001)
Hit-and-run incidents saw a slight decrease in both count and rate year-over-year. The total number of hit-and-run crashes fell from 4,001 in 2018 to 3,907 in 2019. As a percentage of all crashes, the hit-and-run rate remained nearly stable, moving from 12.5% in the prior period to 12.4% in the current period.
Vulnerable Road User Casualties
22
Pedestrians Killed
1
Cyclists Killed
43
Motorists Killed
473
Pedestrians Injured
146
Cyclists Injured
10,727
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 remained consistent between the two periods. Friday was the peak day for crashes in both 2019 (5,419 crashes) and 2018 (5,362 crashes). Similarly, the 5 p.m. hour was the peak time for collisions in both years, though the count decreased from 2,973 crashes in 2018 to 2,755 in 2019. There were no significant shifts in the days or hours when crashes were most frequent.
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
Year-over-year, the severity of crashes showed a positive trend with a notable decrease in fatalities. The number of fatal crashes fell from 82 in 2018 to 64 in 2019, and the corresponding fatal crash rate dropped from 0.3% to 0.2% of all crashes. While fatal incidents decreased, crashes involving serious injuries increased from 337 to 359, and those with minor injuries rose from 2,629 to 2,776.
Severity is per crash event (most severe injury). 64 fatal crash events resulted in 66 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 broadly similar year-over-year, with a slight shift toward clearer conditions. Crashes on dry roads increased from 24,637 in 2018 to 25,149 in 2019, while crashes on wet roads decreased from 5,613 to 4,858. This corresponds with a decrease in crashes during rain, from 3,854 to 3,363. The proportion of crashes occurring in daylight remained stable, accounting for 21,896 incidents in 2019 compared to 22,085 in 2018.
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 remained largely the same between 2018 and 2019, with some changes in ranking. In 2019, the top three makes were Honda (5,793 vehicles), Toyota (5,220), and Nissan (5,093). This represents a shift from 2018, where the top three were Honda (5,468), Ford (5,087), and Toyota (4,816). Ford dropped from the second to the fourth most common make involved in crashes in 2019.
Top Vehicle Makes (60,452 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records
4,996 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (74,534 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
Crashes continued to be most prevalent in 25 mph zones, with the count increasing from 12,067 in 2018 to 12,410 in 2019. This zone also accounted for the highest number of fatal crashes in both years (25 in 2018 and 23 in 2019). There was a notable decrease in fatal crashes in higher speed zones, with fatalities in 35 mph zones dropping from 13 to 10 and in 65 mph zones from 8 to 4.
Fatal crashes by zone: 1 mph: 1 of 4,112 (0.024%) · 25 mph: 23 of 12,410 (0.185%) · 30 mph: 5 of 1,789 (0.279%) · 35 mph: 10 of 2,805 (0.357%) · 40 mph: 6 of 1,417 (0.423%) · 45 mph: 4 of 1,028 (0.389%) · 50 mph: 3 of 528 (0.568%) · 55 mph: 3 of 3,234 (0.093%) · 65 mph: 4 of 1,313 (0.305%) · 88 mph: 1 of 699 (0.143%)
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: 31,594
- Total persons involved: 79,395
- Total vehicles involved: 60,452
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
The data is step one. Get a Connecticut attorney on the line.
Call our intake team. We connect you to a vetted Connecticut personal injury attorney who calls you back within minutes. No phone tag. No voicemails.
Always free for accident victims.