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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · JUNE 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/june-2019-report
Monthly Traffic Safety Analysis
9,417 CRASHES IN
CONNECTICUT, CT
JUNE 2019
In June 2019, there were 9,417 total crashes, a 3.3% decrease from the 9,743 crashes recorded in June 2018. This overall decline was accompanied by a notable year-over-year reduction in traffic fatalities, which fell from 28 to 24. While total crashes and fatalities decreased, the number of reported injuries saw a slight increase of 1.7%, rising from 3,336 to 3,392.
9,417
▼ -3.3%was 9,743
Total Crash Events
24
▼ -14.3%was 28
Persons Killed
3,392
▲ 1.7%was 3,336
Persons Injured
1,040
▼ -9.3%was 1,147
Hit-and-Run Crashes
Note: "Persons Killed" (24) counts individual fatalities across all crash events. "Fatal" in the severity table below (21) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2019-06-01 to 2019-06-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Traffic safety data for June shows a downward trend in the total number of crashes compared to the same month in the prior year, with 326 fewer incidents recorded. Fatalities also decreased from 28 to 24. However, the number of individuals injured in crashes slightly increased from 3,336 in June 2018 to 3,392 in June 2019, indicating a mixed but generally improving safety picture.
1,040
Hit-and-Run Crashes — June 2019
▼ -9.3% vs prior (1,147)
Hit-and-run crashes saw a decline between June 2018 and June 2019. The total number of hit-and-run incidents fell from 1,147 to 1,040. The hit-and-run rate, as a percentage of all crashes, also trended downward, decreasing from 11.8% in the prior period to 11.0% in the current period.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
23
Motorists Killed
0
Other Killed
91
Pedestrians Injured
67
Cyclists Injured
3,233
Motorists Injured
1
Other Injured
Source: Connecticut Crash Data · Csv Open Data · 2019-06-01 to 2019-06-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes remained largely consistent year-over-year. Friday was the busiest day for crashes in both June 2019 (1,495 crashes) and June 2018 (1,870 crashes), though the volume of Friday crashes decreased. The afternoon commute continues to be the peak time for collisions, with the peak hour shifting slightly from 5 p.m. in the prior year (858 crashes) to 4 p.m. in the current period (855 crashes).
Source: Connecticut Crash Data · Csv Open Data · 2019-06-01 to 2019-06-30 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2019-06-01 to 2019-06-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes showed a slight improvement year-over-year. The number of fatal crashes fell from 26 to 21, and the corresponding fatal crash rate decreased from 0.27% to 0.22%. While the count of serious injury crashes also declined slightly from 116 to 109, crashes resulting in minor injuries increased from 951 to 1,017, representing a larger share of the total (10.8% vs. 9.8%).
Severity is per crash event (most severe injury). 21 fatal crash events resulted in 24 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-06-01 to 2019-06-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-06-01 to 2019-06-30 · Most severe injury per crash record
Road & Environmental Conditions
While the proportion of crashes occurring in daylight remained stable at around 81% for both periods, there was a significant shift in weather-related incidents. Crashes in rainy conditions more than doubled, increasing from 573 in June 2018 to 1,136 in June 2019. Consequently, the share of crashes on wet road surfaces rose from 8.4% to 15.3% of all collisions year-over-year.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2019-06-01 to 2019-06-30 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2019-06-01 to 2019-06-30 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2019-06-01 to 2019-06-30 · Road surface condition field
Vehicles & Demographics
The primary vehicle makes involved in crashes were consistent across both periods. Honda, Toyota, and Ford were the top three makes in both June 2018 and June 2019, showing no change in their ranking. Similarly, the age distribution of all persons involved in crashes remained stable, with no significant shifts observed among different age groups year-over-year.
Top Vehicle Makes (17,920 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2019-06-01 to 2019-06-30 · Vehicle unit records
1,426 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (22,177 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2019-06-01 to 2019-06-30 · Person-level records linked to crash events
Speed Limit Zones
Year-over-year, the distribution of crashes across different speed zones showed a decrease in incidents on roads with speed limits of 40 mph or higher, falling from 3,400 to 3,112. Crashes in zones with speed limits of 35 mph or less remained relatively stable. There was a notable shift in fatal crash locations; zones posted at 40 mph and 50 mph recorded a combined 7 fatalities in June 2019, whereas they had zero in the prior year.
Fatal crashes by zone: 20 mph: 1 of 46 (2.174%) · 25 mph: 4 of 2,903 (0.138%) · 30 mph: 1 of 770 (0.13%) · 35 mph: 1 of 1,109 (0.09%) · 40 mph: 4 of 513 (0.78%) · 45 mph: 5 of 314 (1.592%) · 50 mph: 3 of 272 (1.103%) · 88 mph: 1 of 593 (0.169%)
Source: Connecticut Crash Data · Csv Open Data · 2019-06-01 to 2019-06-30 · 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: 2019-06-01 through 2019-06-30
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2019-06-01 through 2019-06-30 (30 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 9,417
- Total persons involved: 23,567
- Total vehicles involved: 17,920
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: June 2019." Published August 20, 2026. Reporting period: 2019-06-01 to 2019-06-30. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/june-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: 2019-06-01 – 2019-06-30
Generated: August 20, 2026 · All rights reserved
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