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ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CONNECTICUT, CT · 2016
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/2016-annual-report
Yearly Traffic Safety Analysis
115,935 CRASHES IN
CONNECTICUT, CT
2016
In 2016, there were 115,935 total traffic crashes, a 4.3% increase from the 111,169 crashes recorded in 2015. This upward trend was accompanied by a 7.9% rise in injuries to 38,760 and a 15.3% increase in fatalities to 309. The most significant year-over-year shift was a 45.7% increase in the number of pedestrians killed, which rose from 46 in 2015 to 67 in 2016.
115,935
▲ 4.3%was 111,169
Total Crash Events
309
▲ 15.3%was 268
Persons Killed
38,760
▲ 7.9%was 35,918
Persons Injured
13,360
▲ 11.2%was 12,018
Hit-and-Run Crashes
Note: "Persons Killed" (309) counts individual fatalities across all crash events. "Fatal" in the severity table below (297) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend for traffic incidents is rising year-over-year. Total crashes increased by 4.3%, from 111,169 in 2015 to 115,935 in 2016. More significantly, total fatalities rose by 15.3% (from 268 to 309), and total injuries increased by 7.9% (from 35,918 to 38,760), indicating a growth in both the frequency and severity of crashes.
13,360
Hit-and-Run Crashes — 2016
▲ 11.2% vs prior (12,018)
Hit-and-run crashes increased in both absolute numbers and as a percentage of total crashes. The count rose by 11.2%, from 12,018 incidents in 2015 to 13,360 in 2016. The corresponding hit-and-run rate also trended upward, increasing from 10.8% of all crashes in 2015 to 11.5% in 2016.
Vulnerable Road User Casualties
67
Pedestrians Killed
6
Cyclists Killed
235
Motorists Killed
1
Other Killed
1,426
Pedestrians Injured
451
Cyclists Injured
36,874
Motorists Injured
9
Other Injured
Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · 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 changes between the two periods. While Friday remained the peak day for crashes in both 2015 (18,767 crashes) and 2016 (20,263 crashes), the peak hour shifted. In 2015, the highest volume of crashes occurred at 3 p.m. (9,445 crashes), whereas in 2016, the peak shifted later to the 5 p.m. hour (10,061 crashes).
Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday
Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity increased from 2015 to 2016. The number of fatal crashes rose from 253 to 297, and their proportion of all crashes increased from 0.2% to 0.3%. Similarly, serious injury crashes increased from 1,269 (1.1% of total) to 1,447 (1.2% of total). While the number of no-injury crashes also grew, their share of all crashes decreased from 76.5% to 75.8%, suggesting that crashes in 2016 were, on average, more severe than in the prior year.
Severity is per crash event (most severe injury). 297 fatal crash events resulted in 309 persons killed.
Outcome by Severity (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record
Road & Environmental Conditions
The distribution of environmental conditions shifted slightly between the two years. In 2016, a greater proportion of crashes occurred on dry roads (81.3% vs. 76.8% in 2015) and in clear weather (81.7% vs. 78.7% in 2015). Correspondingly, the share of crashes on wet, snowy, or icy surfaces decreased. Lighting conditions remained consistent, with crashes in daylight accounting for 70.7% of the total in both years.
Weather
Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash
Lighting
Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field
Road Surface
Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field
Vehicles & Demographics
The top vehicle makes involved in crashes, primarily Ford, Honda, and Toyota, remained consistent across both years, with counts increasing in line with the overall trend. An analysis of persons involved shows that the age distributions were largely stable. However, the 26-34 age group's share of individuals involved in crashes grew slightly, from 16.3% of the total in 2015 to 16.9% in 2016.
Top Vehicle Makes (219,504 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records
17,706 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (272,133 persons with recorded sex)
Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Person-level records linked to crash events
Speed Limit Zones
Year-over-year data shows a shift in crashes toward lower speed zones. Crashes in 25 mph zones increased from 32,602 to 36,085, while collisions in 65 mph zones decreased from 5,716 to 5,382. Despite more crashes occurring at lower posted speeds, the fatality rate within several of these zones increased, most notably in 45 mph zones where the rate rose from 0.575% in 2015 to 0.819% in 2016.
Fatal crashes by zone: 1 mph: 11 of 11,989 (0.092%) · 15 mph: 1 of 665 (0.15%) · 20 mph: 3 of 549 (0.546%) · 25 mph: 73 of 36,085 (0.202%) · 30 mph: 28 of 10,281 (0.272%) · 35 mph: 37 of 13,452 (0.275%) · 40 mph: 28 of 7,160 (0.391%) · 45 mph: 38 of 4,639 (0.819%) · 50 mph: 15 of 3,220 (0.466%) · 55 mph: 25 of 10,036 (0.249%) · 65 mph: 24 of 5,382 (0.446%) · 88 mph: 12 of 7,488 (0.16%) · 99 mph: 2 of 1,729 (0.116%)
Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
- Report generated: August 20, 2026
Data Coverage
- Reporting period: 2016-01-01 through 2016-12-31 (366 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 115,935
- Total persons involved: 288,643
- Total vehicles involved: 219,504
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: 2016." Published August 20, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Connecticut Crash Data, Csv Open Data. Available at: https://thatcarhitme.com/crash-data/connecticut/statewide/2016-annual-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: 2016-01-01 – 2016-12-31
Generated: August 20, 2026 · All rights reserved
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