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 · APRIL 2023
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-2023-report
Monthly Traffic Safety Analysis
7,833 CRASHES IN
CONNECTICUT, CT
APRIL 2023
In April 2023, Connecticut recorded 7,833 total crashes, a slight decrease of 0.9% from the 7,901 crashes reported in April 2022. Despite this overall reduction in collisions, the number of fatal crashes increased by 31.6%, rising from 19 to 25 year-over-year. Consequently, total fatalities rose from 23 to 25 during the same period.
7,833
▼ -0.9%was 7,901
Total Crash Events
25
▲ 8.7%was 23
Persons Killed
2,716
▼ -1.0%was 2,744
Persons Injured
1,090
▲ 7.6%was 1,013
Hit-and-Run Crashes
Note: "Persons Killed" (25) counts individual fatalities across all crash events. "Fatal" in the severity table below (25) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2023-04-01 to 2023-04-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash volume in Connecticut remained relatively stable, with a minor 0.9% decrease from 7,901 crashes in April 2022 to 7,833 in April 2023. Total reported injuries also saw a slight 1.0% decline. However, this stability did not extend to the most severe outcomes, as total fatalities rose by 8.7% from 23 to 25 over the same period.
1,090
Hit-and-Run Crashes — April 2023
▲ 7.6% vs prior (1,013)
Hit-and-run incidents increased in both count and as a proportion of total crashes. The number of hit-and-run crashes rose by 7.6%, from 1,013 in April 2022 to 1,090 in April 2023. This pushed the hit-and-run rate up from 12.8% to 13.9% of all collisions, indicating an upward trend for this crash type.
Vulnerable Road User Casualties
3
Pedestrians Killed
0
Cyclists Killed
22
Motorists Killed
75
Pedestrians Injured
22
Cyclists Injured
2,619
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2023-04-01 to 2023-04-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The timing of crashes showed some shifts between April 2022 and April 2023. The peak day for collisions moved from Friday (1,580 crashes) in the prior year to Saturday (1,306 crashes) in the current period. While the 3 p.m. hour remained the single busiest hour for crashes in both years, there was a notable increase in crashes occurring on Sundays, which rose from 730 to 1,188 year-over-year.
Source: Connecticut Crash Data · Csv Open Data · 2023-04-01 to 2023-04-30 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2023-04-01 to 2023-04-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While the overall proportion of injury-related crashes remained constant at 25.1% in both April 2022 and April 2023, the severity of outcomes worsened. The number of fatal crashes increased from 19 to 25, raising the fatal crash rate from 0.24% to 0.32% of all collisions. The share of serious injury crashes also saw a slight rise from 1.1% to 1.2% year-over-year.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2023-04-01 to 2023-04-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2023-04-01 to 2023-04-30 · Most severe injury per crash record
Road & Environmental Conditions
The distribution of crashes by environmental conditions remained largely consistent year-over-year. In both April 2022 and April 2023, the vast majority of crashes occurred in clear weather and on dry roads. There was a minor shift in conditions, with the proportion of crashes in the rain increasing from 11.2% to 12.9% and on wet roads from 14.8% to 16.7%.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2023-04-01 to 2023-04-30 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2023-04-01 to 2023-04-30 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2023-04-01 to 2023-04-30 · Road surface condition field
Vehicles & Demographics
Analysis of the vehicles involved in crashes shows remarkable stability year-over-year. The top five most frequently involved vehicle makes were identical in both April 2022 and April 2023: Honda, Toyota, Ford, Nissan, and Chevrolet, with their respective crash counts remaining nearly unchanged. Similarly, the age distribution of persons involved in collisions showed no significant shifts between the two periods.
Top Vehicle Makes (14,905 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2023-04-01 to 2023-04-30 · Vehicle unit records
1,405 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (17,656 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2023-04-01 to 2023-04-30 · Person-level records linked to crash events
Speed Limit Zones
Crash distribution across speed zones shifted between the two periods. There was an increase in crashes occurring in zones posted at 65 mph or higher (from 843 to 956 incidents) and a decrease in zones between 45-55 mph (from 1,343 to 1,198 incidents). The number of fatal crashes increased in several zones, most significantly in 65 mph zones, which saw an increase from zero to five fatal crashes year-over-year. Fatalities also rose in 40 mph zones from two to five.
Fatal crashes by zone: 1 mph: 1 of 1,030 (0.097%) · 30 mph: 3 of 579 (0.518%) · 35 mph: 3 of 861 (0.348%) · 40 mph: 5 of 445 (1.124%) · 45 mph: 2 of 250 (0.8%) · 50 mph: 2 of 140 (1.429%) · 55 mph: 3 of 808 (0.371%) · 65 mph: 5 of 495 (1.01%) · 88 mph: 1 of 406 (0.246%)
Source: Connecticut Crash Data · Csv Open Data · 2023-04-01 to 2023-04-30 · 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: 2023-04-01 through 2023-04-30
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2023-04-01 through 2023-04-30 (30 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 7,833
- Total persons involved: 19,167
- Total vehicles involved: 14,905
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 2023." Published August 20, 2026. Reporting period: 2023-04-01 to 2023-04-30. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/april-2023-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: 2023-04-01 – 2023-04-30
Generated: August 20, 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.