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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · APRIL 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/april-2019-report
Monthly Traffic Safety Analysis
8,463 CRASHES IN
CONNECTICUT, CT
APRIL 2019
In April 2019, Connecticut recorded 8,463 traffic crashes, a 1.9% decrease from the 8,623 crashes reported in April 2018. The most significant year-over-year change was a substantial drop in fatalities, which fell by 55.6% from 27 to 12. While total crashes and fatalities decreased, the number of reported injuries saw a slight increase from 2,746 to 2,802.
8,463
▼ -1.9%was 8,623
Total Crash Events
12
▼ -55.6%was 27
Persons Killed
2,802
▲ 2.0%was 2,746
Persons Injured
957
▼ -9.9%was 1,062
Hit-and-Run Crashes
Note: "Persons Killed" (12) 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 · 2019-04-01 to 2019-04-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash volume in Connecticut showed a slight decline in April 2019 compared to the same month in the prior year, with total crashes falling by 1.9% from 8,623 to 8,463. While total crashes decreased, the number of reported injuries rose by 2.0%. However, traffic fatalities saw a significant year-over-year reduction, dropping by 55.6% from 27 to 12.
957
Hit-and-Run Crashes — April 2019
▼ -9.9% vs prior (1,062)
The number of hit-and-run incidents decreased from 1,062 in April 2018 to 957 in April 2019. This represents a downward trend in the hit-and-run rate, which fell from 12.3% of all crashes in the prior period to 11.3% in the current period.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
11
Motorists Killed
98
Pedestrians Injured
25
Cyclists Injured
2,679
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2019-04-01 to 2019-04-30 · 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 consistent year-over-year. In both April 2019 and April 2018, Monday was the peak day for crashes, with 1,451 and 1,584 incidents, respectively. Similarly, the 3 p.m. hour was the most frequent time for crashes in both periods, accounting for 725 crashes in the current period and 758 in the prior.
Source: Connecticut Crash Data · Csv Open Data · 2019-04-01 to 2019-04-30 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2019-04-01 to 2019-04-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes shifted notably between the two periods. The number of fatal crashes decreased from 25 in April 2018 to 11 in April 2019, and the fatal crash rate fell from 0.29 to 0.13 per 100 crashes. Conversely, crashes resulting in serious injuries increased from 70 to 91, and their share of all crashes rose from 0.8% to 1.1%. Crashes with no injuries accounted for 75.5% of incidents in the current period, a slight decrease from 76.3% in the prior year.
Severity is per crash event (most severe injury). 11 fatal crash events resulted in 12 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-04-01 to 2019-04-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-04-01 to 2019-04-30 · Most severe injury per crash record
Road & Environmental Conditions
Crash conditions were broadly similar year-over-year, with most incidents in both periods occurring in daylight (6,629 in 2019 vs. 6,681 in 2018) and on dry roads (6,433 vs. 6,247). A key difference was the presence of winter weather in April 2018, which saw 306 crashes during snowy conditions and 213 on snowy road surfaces. These conditions were almost entirely absent in April 2019, where only 3 crashes occurred in snow.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2019-04-01 to 2019-04-30 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2019-04-01 to 2019-04-30 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2019-04-01 to 2019-04-30 · Road surface condition field
Vehicles & Demographics
The top five vehicle makes involved in crashes remained unchanged between April 2018 and April 2019: Honda, Toyota, Ford, Nissan, and Chevrolet, with all five seeing a slight increase in their crash counts. The age distribution of individuals involved in crashes also showed strong consistency, with the 26-34 age group representing the largest cohort in both years, accounting for 3,636 people in 2019 and 3,681 in 2018.
Top Vehicle Makes (16,290 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2019-04-01 to 2019-04-30 · Vehicle unit records
1,330 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (20,045 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2019-04-01 to 2019-04-30 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across different speed zones was largely stable, with the 25 mph zone accounting for the most crashes in both periods (2,697 in 2019 vs. 2,610 in 2018). However, there was a notable shift in where fatal crashes occurred. In April 2018, the 45 mph and 25 mph zones recorded the most fatalities with 6 and 5, respectively. In April 2019, the 45 mph zone had 3 fatalities, but no fatal crashes were recorded in the 25 mph zone, while the 50 mph zone saw 2 fatal crashes compared to none the previous year.
Fatal crashes by zone: 1 mph: 2 of 1,019 (0.196%) · 35 mph: 2 of 940 (0.213%) · 45 mph: 3 of 288 (1.042%) · 50 mph: 2 of 251 (0.797%) · 65 mph: 1 of 394 (0.254%) · 88 mph: 1 of 566 (0.177%)
Source: Connecticut Crash Data · Csv Open Data · 2019-04-01 to 2019-04-30 · 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: 2019-04-01 through 2019-04-30
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2019-04-01 through 2019-04-30 (30 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 8,463
- Total persons involved: 21,339
- Total vehicles involved: 16,290
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: April 2019." Published August 20, 2026. Reporting period: 2019-04-01 to 2019-04-30. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/april-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: 2019-04-01 – 2019-04-30
Generated: August 20, 2026 · All rights reserved
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