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
3,989 CRASHES IN
CONNECTICUT, CT
2016
In 2016, Litchfield County recorded 3,989 traffic crashes, a 1.3% increase from the 3,939 crashes documented in 2015. While total collisions remained relatively stable, there was a notable divergence in outcomes: total fatalities decreased by 27.3% from 22 to 16, while total injuries rose by 17.3% from 1,108 to 1,300.
3,989
▲ 1.3%was 3,939
Total Crash Events
16
▼ -27.3%was 22
Persons Killed
1,300
▲ 17.3%was 1,108
Persons Injured
366
▲ 9.6%was 334
Hit-and-Run Crashes
Note: "Persons Killed" (16) counts individual fatalities across all crash events. "Fatal" in the severity table below (16) 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
Overall crash volume in Litchfield County saw a slight upward trend, increasing by 1.3% from 3,939 in 2015 to 3,989 in 2016. This was accompanied by a 17.3% rise in the number of people injured. However, the number of fatalities resulting from these crashes decreased by 27.3% year-over-year, from 22 to 16.
366
Hit-and-Run Crashes — 2016
▲ 9.6% vs prior (334)
Hit-and-run incidents increased in both count and as a proportion of total crashes. The number of hit-and-run crashes rose from 334 in 2015 to 366 in 2016. This represents an upward trend in the hit-and-run rate, which climbed from 8.5% to 9.2% of all collisions in Litchfield County.
Vulnerable Road User Casualties
2
Pedestrians Killed
0
Cyclists Killed
14
Motorists Killed
27
Pedestrians Injured
10
Cyclists Injured
1,263
Motorists 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 year-over-year shifts. While Friday remained the peak day for crashes in both 2015 (605 crashes) and 2016 (703 crashes), the busiest hour for collisions moved one hour later, from the 3 PM hour in 2015 to the 4 PM hour in 2016. Monthly crash distribution also varied, with December being the highest-volume month in 2016 (441 crashes), compared to January in the prior year (408 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
The severity of crashes shifted year-over-year, with a notable decrease in fatal incidents but an increase in injuries. The number of fatal crashes fell from 21 in 2015 to 16 in 2016, lowering the fatal crash rate from 0.5% to 0.4%. Conversely, the count of serious injury crashes increased from 50 to 69, and their share of all crashes rose from 1.3% to 1.7%.
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 crashes across lighting and road surface conditions remained largely consistent year-over-year, with most incidents occurring in daylight on dry roads. However, there was a notable shift in weather-related crashes. The number of collisions occurring in snow increased from 180 in 2015 to 267 in 2016, representing a rise from 4.6% to 6.7% of all crashes. Conversely, crashes reported during rainy conditions decreased from 338 to 301.
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 remained consistent, with Ford, Chevrolet, Toyota, and Honda being the most frequent in both years. Ford's involvement decreased from 819 vehicles in 2015 to 764 in 2016, while most other top makes saw minor fluctuations. An analysis of persons involved in crashes shows a largely stable age distribution, though the 45-54 age group saw a notable decrease from 1,365 individuals in 2015 to 1,265 in 2016. Conversely, the 26-34 age group increased from 1,177 to 1,278.
Top Vehicle Makes (6,645 vehicles)
Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records
364 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (7,876 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
The distribution of crashes across speed zones was similar year-over-year, with the 25 mph zone accounting for the most incidents in both 2015 (1,063 crashes) and 2016 (1,095 crashes). However, the location of fatal crashes shifted; in 2016, there were zero fatalities in 25 mph zones, down from four in the prior year. The highest fatal crash rate in 2016 was observed in the 50 mph zone, where 4.2% of crashes were fatal, a shift from 2015 when the 55 mph zone had the highest rate.
Fatal crashes by zone: 1 mph: 1 of 198 (0.505%) · 30 mph: 1 of 469 (0.213%) · 35 mph: 2 of 673 (0.297%) · 40 mph: 3 of 477 (0.629%) · 45 mph: 5 of 331 (1.511%) · 50 mph: 3 of 71 (4.225%) · 88 mph: 1 of 182 (0.549%)
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: September 10, 2026
Data Coverage
- Reporting period: 2016-01-01 through 2016-12-31 (366 days)
- Geographic scope: connecticut, CT
- Total crash records analyzed: 3,989
- Total persons involved: 8,661
- Total vehicles involved: 6,645
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 September 10, 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: September 10, 2026 · All rights reserved