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YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · MARCH 2024
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/march-2024-report
Monthly Traffic Safety Analysis
7,913 CRASHES IN
CONNECTICUT, CT
MARCH 2024
In March 2024, Connecticut recorded 7,913 total traffic crashes, a 1.3% increase from the 7,814 crashes reported in March 2023. While overall crash and injury figures remained relatively stable, the most notable year-over-year change was a significant increase in crashes involving vulnerable road users, including a 61.8% rise in motorcycle-involved incidents and a 31.8% rise in pedestrian-involved incidents.
7,913
▲ 1.3%was 7,814
Total Crash Events
22
▼ -8.3%was 24
Persons Killed
2,426
▼ -0.6%was 2,440
Persons Injured
1,100
▲ 7.1%was 1,027
Hit-and-Run Crashes
Note: "Persons Killed" (22) counts individual fatalities across all crash events. "Fatal" in the severity table below (22) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2024-03-01 to 2024-03-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash trends in Connecticut remained relatively stable between March 2023 and March 2024. Total crashes increased by a modest 1.3%, from 7,814 to 7,913. Conversely, the human toll saw a slight decline, with total injuries decreasing by 0.6% and fatalities falling from 24 to 22.
1,100
Hit-and-Run Crashes — March 2024
▲ 7.1% vs prior (1,027)
Hit-and-run incidents increased in both count and as a proportion of total crashes from March 2023 to March 2024. The number of hit-and-run crashes rose from 1,027 to 1,100, an increase of 7.1%. This pushed the hit-and-run rate up from 13.1% to 13.9% of all crashes, indicating an upward trend for this type of collision.
Vulnerable Road User Casualties
7
Pedestrians Killed
0
Cyclists Killed
15
Motorists Killed
104
Pedestrians Injured
17
Cyclists Injured
2,305
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2024-03-01 to 2024-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes showed some shifts between March 2023 and March 2024. Friday remained the peak day for collisions in both periods, with 1,441 crashes in the current period and 1,447 in the prior. However, the peak hour for crashes shifted slightly earlier, from 4 p.m. in 2023 (683 crashes) to 3 p.m. in 2024 (703 crashes). Notably, weekend crashes increased, with Saturday collisions rising by 26.3% and Sunday collisions by 28.7% year-over-year.
Source: Connecticut Crash Data · Csv Open Data · 2024-03-01 to 2024-03-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2024-03-01 to 2024-03-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes saw mixed changes year-over-year. While the number of fatalities decreased from 24 to 22, the count of fatal crashes increased slightly from 21 to 22. The proportion of crashes resulting in serious injuries rose from 0.9% to 1.0% of all incidents (68 to 79 crashes). Crashes resulting in possible injuries decreased from 11.5% to 10.7% of the total, while those with no injuries increased proportionally from 76.7% to 77.2%.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2024-03-01 to 2024-03-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2024-03-01 to 2024-03-31 · Most severe injury per crash record
Road & Environmental Conditions
Environmental conditions during crashes differed notably year-over-year, primarily concerning precipitation. The proportion of crashes occurring in clear weather decreased from 80.4% in March 2023 to 75.8% in March 2024. Correspondingly, crashes during rain more than doubled from 698 to 1,498 incidents, and collisions on wet road surfaces increased from 14.9% to 21.9% of all crashes. Lighting conditions remained consistent, with approximately 70% of crashes in both periods occurring during daylight.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2024-03-01 to 2024-03-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2024-03-01 to 2024-03-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2024-03-01 to 2024-03-31 · Road surface condition field
Vehicles & Demographics
An analysis of vehicles and persons involved in crashes shows high consistency between March 2023 and March 2024. The top five vehicle makes involved in collisions remained the same: Honda, Toyota, Ford, Nissan, and Chevrolet, with very similar involvement counts in both periods. Similarly, the age distribution of all persons involved in crashes was stable, with the 26-34 age group representing the largest cohort in both March 2024 (3,107 individuals) and March 2023 (3,160 individuals).
Top Vehicle Makes (14,832 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2024-03-01 to 2024-03-31 · Vehicle unit records
1,243 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (17,311 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2024-03-01 to 2024-03-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across different speed zones showed some year-over-year changes, particularly in higher-speed areas. The number of crashes in 55 mph zones increased from 744 to 775, and collisions in 65 mph zones rose from 456 to 543. Most notably, the 65 mph zones, which had zero fatal crashes in March 2023, recorded 5 fatal crashes in March 2024. Conversely, fatal crashes in 30-35 mph zones decreased from 9 to 4 over the same period.
Fatal crashes by zone: 25 mph: 6 of 2,193 (0.274%) · 30 mph: 1 of 592 (0.169%) · 35 mph: 3 of 892 (0.336%) · 40 mph: 2 of 430 (0.465%) · 45 mph: 2 of 280 (0.714%) · 50 mph: 1 of 188 (0.532%) · 55 mph: 2 of 775 (0.258%) · 65 mph: 5 of 543 (0.921%)
Source: Connecticut Crash Data · Csv Open Data · 2024-03-01 to 2024-03-31 · 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: 2024-03-01 through 2024-03-31
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2024-03-01 through 2024-03-31 (31 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 7,913
- Total persons involved: 18,798
- Total vehicles involved: 14,832
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: March 2024." Published August 20, 2026. Reporting period: 2024-03-01 to 2024-03-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/march-2024-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: 2024-03-01 – 2024-03-31
Generated: August 20, 2026 · All rights reserved
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