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YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · MARCH 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/march-2023-report
Monthly Traffic Safety Analysis
7,814 CRASHES IN
CONNECTICUT, CT
MARCH 2023
In March 2023, Connecticut recorded 7,814 total vehicle crashes, a 2.9% decrease from the 8,048 crashes reported in March 2022. While overall crashes, injuries, and fatalities saw a modest decline, the most significant year-over-year change was a substantial reduction in bicycle-involved incidents, with total bicycle crashes falling from 28 to 11 and cyclist injuries decreasing from 25 to 9.
7,814
▼ -2.9%was 8,048
Total Crash Events
24
▼ -4.0%was 25
Persons Killed
2,440
▼ -4.8%was 2,564
Persons Injured
1,027
▲ 0.8%was 1,019
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 · 2023-03-01 to 2023-03-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, traffic incidents in Connecticut showed a downward trend in March 2023 compared to the same month in the previous year. Total crashes decreased by 2.9%, from 8,048 to 7,814. Similarly, the number of people injured fell by 4.8% to 2,440, and total fatalities saw a slight decrease from 25 to 24.
1,027
Hit-and-Run Crashes — March 2023
▲ 0.8% vs prior (1,019)
The number of hit-and-run incidents saw a slight increase, rising from 1,019 in March 2022 to 1,027 in March 2023. Because the total number of crashes decreased during the same period, the hit-and-run rate showed a more pronounced upward trend. These incidents accounted for 13.1% of all crashes in the current period, compared to 12.7% in the prior year.
Vulnerable Road User Casualties
6
Pedestrians Killed
1
Cyclists Killed
17
Motorists Killed
77
Pedestrians Injured
9
Cyclists Injured
2,354
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2023-03-01 to 2023-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes shifted slightly between March 2022 and March 2023. The day with the most crashes moved from Wednesday (1,358 incidents) in the prior year to Friday (1,447 incidents) in the current period. The peak hour for collisions also shifted one hour later, from 3 p.m. in 2022 to 4 p.m. in 2023, though the number of crashes during the peak hour decreased from 724 to 683.
Source: Connecticut Crash Data · Csv Open Data · 2023-03-01 to 2023-03-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2023-03-01 to 2023-03-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The distribution of crash severity remained largely consistent year-over-year, with fatal crashes accounting for 0.3% of all incidents in both March 2023 and March 2022. The rate of fatal crashes per 100 incidents decreased slightly from 0.31 to 0.27. The proportion of crashes resulting in any level of injury (serious, minor, or possible) saw a marginal decline, from 23.4% in the prior period to 23.0% in the current period.
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 · 2023-03-01 to 2023-03-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2023-03-01 to 2023-03-31 · Most severe injury per crash record
Road & Environmental Conditions
Crash conditions in March 2023 were broadly similar to the previous year, with the vast majority of incidents occurring in clear weather and during daylight hours. Crashes in clear weather accounted for 80.4% of the total, up from 77.9% in March 2022. Correspondingly, crashes on dry road surfaces increased from 76.8% to 80.1% of all incidents, while crashes attributed to snow on the road decreased from 209 to 118.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2023-03-01 to 2023-03-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2023-03-01 to 2023-03-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2023-03-01 to 2023-03-31 · Road surface condition field
Vehicles & Demographics
The makes of vehicles involved in crashes remained consistent year-over-year, with Honda (1,632), Toyota (1,498), and Ford (1,280) being the top three in March 2023, the same as in the prior year. The number of vehicles from each of these top makes involved in collisions saw little change. Similarly, the age demographics of individuals involved in crashes were stable, with the 26-34 age group consistently being the largest segment in both periods.
Top Vehicle Makes (14,634 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2023-03-01 to 2023-03-31 · Vehicle unit records
1,287 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (16,857 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2023-03-01 to 2023-03-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across various speed limit zones remained relatively stable between the two periods. However, there was a notable shift in the location of fatal crashes. In March 2023, fatalities became more concentrated in lower-speed zones, with crashes in 25 mph zones resulting in 6 deaths, up from 2 the prior year. Conversely, zones with speed limits of 55 mph and 65 mph, which accounted for a combined 8 fatalities in March 2022, recorded zero fatalities in March 2023.
Fatal crashes by zone: 25 mph: 6 of 2,325 (0.258%) · 30 mph: 4 of 590 (0.678%) · 35 mph: 5 of 877 (0.57%) · 45 mph: 3 of 270 (1.111%) · 50 mph: 1 of 144 (0.694%) · 88 mph: 1 of 391 (0.256%) · 99 mph: 1 of 34 (2.941%)
Source: Connecticut Crash Data · Csv Open Data · 2023-03-01 to 2023-03-31 · 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: 2023-03-01 through 2023-03-31
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2023-03-01 through 2023-03-31 (31 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 7,814
- Total persons involved: 18,324
- Total vehicles involved: 14,634
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 2023." Published August 20, 2026. Reporting period: 2023-03-01 to 2023-03-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/march-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: 2023-03-01 – 2023-03-31
Generated: August 20, 2026 · All rights reserved
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