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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · MARCH 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/march-2019-report
Monthly Traffic Safety Analysis
8,953 CRASHES IN
CONNECTICUT, CT
MARCH 2019
In March 2019, there were 8,953 total crashes recorded in Connecticut, a 3.9% increase from the 8,619 crashes in March 2018. While overall crash volumes and injury counts remained relatively stable, incidents involving vulnerable road users saw a notable shift. Crashes involving pedestrians increased by 28.4% from 95 to 122, and motorcycle-involved crashes rose by 43.5% from 23 to 33.
8,953
▲ 3.9%was 8,619
Total Crash Events
16
▲ 6.7%was 15
Persons Killed
2,828
▲ 0.8%was 2,806
Persons Injured
1,021
▼ -4.5%was 1,069
Hit-and-Run Crashes
Note: "Persons Killed" (16) counts individual fatalities across all crash events. "Fatal" in the severity table below (14) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2019-03-01 to 2019-03-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Crash data for March 2019 indicates an upward trend compared to the same month in the prior year. Total crashes increased by 3.9%, rising from 8,619 to 8,953. This was accompanied by a slight increase in total fatalities from 15 to 16 and a marginal rise in injuries from 2,806 to 2,828.
1,021
Hit-and-Run Crashes — March 2019
▼ -4.5% vs prior (1,069)
Hit-and-run incidents showed a downward trend in March 2019 compared to the same month in 2018. The total number of hit-and-run crashes decreased from 1,069 to 1,021. This decline was also reflected in the hit-and-run rate, which fell by a full percentage point from 12.4% to 11.4% of all crashes.
Vulnerable Road User Casualties
5
Pedestrians Killed
0
Cyclists Killed
11
Motorists Killed
111
Pedestrians Injured
23
Cyclists Injured
2,694
Motorists Injured
Source: Connecticut Crash Data · Csv Open Data · 2019-03-01 to 2019-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes remained broadly consistent year-over-year, with Friday being the peak day and 3 PM the peak hour in both March 2019 and March 2018. However, the concentration of crashes on Fridays intensified, with 1,894 incidents in the current period compared to 1,705 in the prior year. A notable shift also occurred during the morning commute, where crashes between 7 AM and 8 AM increased from 948 to 1,211.
Source: Connecticut Crash Data · Csv Open Data · 2019-03-01 to 2019-03-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2019-03-01 to 2019-03-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The overall severity distribution of crashes remained stable between March 2018 and March 2019, with the proportion of crashes in each severity category changing by less than half a percentage point. The fatal crash rate saw a slight decrease from 0.17% to 0.16%, corresponding to 14 fatal crashes in March 2019 compared to 15 in the prior year. Despite the drop in fatal crashes, the total number of people injured saw a marginal increase from 2,806 to 2,828.
Severity is per crash event (most severe injury). 14 fatal crash events resulted in 16 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-03-01 to 2019-03-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2019-03-01 to 2019-03-31 · Most severe injury per crash record
Road & Environmental Conditions
Crashes in March 2019 occurred more frequently under clear and dry conditions compared to the prior year. Crashes on dry roads accounted for 79.4% of the total, up from 78.2% in March 2018, while crashes in clear weather made up 81.7% of incidents, an increase from 78.2%. Correspondingly, crashes attributed to adverse conditions like snow and wet roads saw a decrease; for instance, crashes on wet surfaces fell from 1,001 to 884. The proportion of crashes occurring in daylight also rose from 71.5% to 73.7%.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2019-03-01 to 2019-03-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2019-03-01 to 2019-03-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2019-03-01 to 2019-03-31 · Road surface condition field
Vehicles & Demographics
The makes of vehicles involved in crashes showed a consistent pattern, with the top five remaining the same year-over-year. Honda continued to be the most frequently involved make, with its count increasing from 1,615 to 1,750 vehicles. Toyota moved from the third to the second most common make as its involvement rose from 1,367 to 1,598 vehicles, while Ford moved from second to third. Chevrolet, while remaining the fifth most common make, saw a notable increase in involved vehicles from 844 to 1,019.
Top Vehicle Makes (16,778 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2019-03-01 to 2019-03-31 · Vehicle unit records
1,403 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (20,630 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2019-03-01 to 2019-03-31 · Person-level records linked to crash events
Speed Limit Zones
Year-over-year, there was a noticeable increase in crashes occurring in both lower (25 mph or less) and higher (65 mph or more) speed zones. Crashes in zones of 25 mph or less rose from 3,849 to 4,070, while incidents in zones of 65 mph or more increased from 989 to 1,103. While the total number of fatal crashes decreased from 15 to 14, their distribution shifted towards higher speed areas. In March 2019, 10 of the 14 fatal crashes occurred in zones of 45 mph or higher, compared to 7 of 15 fatal crashes in the same zones during March 2018.
Fatal crashes by zone: 25 mph: 1 of 2,830 (0.035%) · 30 mph: 1 of 717 (0.139%) · 35 mph: 1 of 1,019 (0.098%) · 40 mph: 1 of 502 (0.199%) · 45 mph: 3 of 343 (0.875%) · 50 mph: 1 of 230 (0.435%) · 55 mph: 1 of 724 (0.138%) · 65 mph: 3 of 444 (0.676%) · 88 mph: 2 of 555 (0.36%)
Source: Connecticut Crash Data · Csv Open Data · 2019-03-01 to 2019-03-31 · 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: 2019-03-01 through 2019-03-31
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2019-03-01 through 2019-03-31 (31 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 8,953
- Total persons involved: 21,894
- Total vehicles involved: 16,778
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 2019." Published August 20, 2026. Reporting period: 2019-03-01 to 2019-03-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/march-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: 2019-03-01 – 2019-03-31
Generated: August 20, 2026 · All rights reserved
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