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Yearly Traffic Safety Analysis

2,103 CRASHES IN
CONNECTICUT, CT
2016

All metrics benchmarked against2015

In 2016, Windham County recorded 2,103 total crashes, a 5.7% increase from the 1,990 crashes documented in 2015. While total fatalities decreased from 19 to 16 year-over-year, total injuries rose by 6.2%. The most notable shift was a 20.2% increase in crashes involving a driver under the influence, which grew from 94 incidents in 2015 to 113 in 2016.

2,103

▲ 5.7%was 1,990

Total Crash Events

▼ -15.8%was 19

Persons Killed

722

▲ 6.2%was 680

Persons Injured

189

▲ 8.0%was 175

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 traffic crashes in Windham County trended upward in 2016 compared to the prior year. Total crashes rose by 5.7% from 1,990 to 2,103, and the number of people injured increased by 6.2% from 680 to 722. Conversely, the number of people killed in crashes decreased from 19 in 2015 to 16 in 2016.

189

Hit-and-Run Crashes — 2016

▲ 8.0% vs prior (175)

Hit-and-run incidents increased in both count and as a percentage of total crashes in 2016 compared to the prior year. The number of hit-and-run crashes rose from 175 to 189. This represents a slight increase in the hit-and-run rate, which moved from 8.8% of all crashes in 2015 to 9.0% in 2016.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 3→0.0%

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 16↓-18.8%

Pedestrians Injured

Prior: 17↓-29.4%

Cyclists Injured

Prior: 7→0.0%

703

Motorists Injured

Prior: 656↑7.2%

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 a notable shift in the peak day of the week between the two periods. While the 3 p.m. hour remained the busiest time for crashes in both 2015 and 2016, the peak day shifted from Friday (327 crashes) in 2015 to Monday (365 crashes) in 2016. Crashes occurring on Mondays increased by 35.7% year-over-year.

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 overall severity of crashes remained relatively stable, though with some shifts in specific categories. The fatal crash rate decreased from 0.85% in 2015 to 0.76% in 2016. While the proportion of crashes resulting in any injury was nearly unchanged, the number of crashes classified as 'Serious Injury' increased from 22 to 29, representing a proportional increase from 1.1% to 1.4% of all crashes.

Outcome by Severity (Crash Events)

Fatal16fatal crashes0.8%

↓-5.9%prior 17

Serious Injury29serious injury crashes1.4%

↑31.8%prior 22

Minor Injury297minor injury crashes14.1%

↑3.5%prior 287

Possible Injury205possible injury crashes9.7%

↓-0.5%prior 206

No Injury1,556no injury crashes74%

↑6.7%prior 1,458

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 different environmental conditions remained largely consistent year-over-year. In both 2015 and 2016, the majority of crashes occurred in clear weather (75.7% in 2016 vs 74.6% in 2015) and on dry roads (72.0% vs 69.4%). Similarly, most incidents happened during daylight hours in both periods, with no significant change in the proportion of crashes occurring in darkness or adverse weather.

Weather

Clear1,593 (76.1%)

↑7.3%prior 1,484

Rain181 (8.6%)

↑14.6%prior 158

Snow163 (7.8%)

↑3.2%prior 158

Cloudy91 (4.3%)

↑9.6%prior 83

Blowing Snow27 (1.3%)

↑3.8%prior 26

Freezing Rain or Freezing Drizzle22 (1.1%)

↓-33.3%prior 33

Fog, Smog, Smoke15 (0.7%)

↓-21.1%prior 19

Sleet or Hail1 (0.0%)

Severe Crosswinds1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight1,429 (68.4%)

↑7.8%prior 1,325

Dark-Not Lighted333 (15.9%)

↓-5.4%prior 352

Dark-Lighted237 (11.3%)

↓-1.3%prior 240

Dusk39 (1.9%)

↑39.3%prior 28

Dawn25 (1.2%)

↑66.7%prior 15

Dark-Unknown Lighting14 (0.7%)

↑7.7%prior 13

Other12 (0.6%)

↑71.4%prior 7

Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry1,514 (72.1%)

↑9.6%prior 1,382

Wet294 (14.0%)

↑6.1%prior 277

Snow165 (7.9%)

↓-8.3%prior 180

Ice / Frost61 (2.9%)

↓-16.4%prior 73

Slush38 (1.8%)

↓-15.6%prior 45

Sand10 (0.5%)

↓-23.1%prior 13

Mud, Dirt, Gravel7 (0.3%)

↑40.0%prior 5

Other5 (0.2%)

Standing Water4 (0.2%)

↓-20.0%prior 5

Moving Water1 (0.0%)

Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes showed consistency, with Ford being the most frequent make in both 2015 (517 vehicles) and 2016 (531 vehicles). An analysis of persons involved shows the 26-34 age group was the most represented demographic in both years, accounting for 767 individuals in 2015 and 732 in 2016. The number of individuals in the 21-25 age group involved in crashes increased from 569 to 607 year-over-year.

Top Vehicle Makes (3,452 vehicles)

FORD531 (15.4%)

↑2.7%prior 517

CHEV285 (8.3%)

↓-10.9%prior 320

HOND196 (5.7%)

↑33.3%prior 147

TOYO150 (4.3%)

↑120.6%prior 68

NISS148 (4.3%)

↓-6.9%prior 159

JEEP131 (3.8%)

↑9.2%prior 120

HYUN115 (3.3%)

↑5.5%prior 109

DODG112 (3.2%)

↓-5.1%prior 118

TOYOTA96 (2.8%)

↑26.3%prior 76

SUBA90 (2.6%)

↓-9.1%prior 99

Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

146 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (4,454 persons with recorded sex)

Male2,470 (55.5%)

↓-1.3%prior 2,503

Female1,984 (44.5%)

↓-0.8%prior 1,999

Source: Connecticut Crash Data · Csv Open Data · 2016-01-01 to 2016-12-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 2016 continued to be most frequent in 25 mph zones, with the count increasing from 515 to 555 year-over-year. A significant shift occurred in the location of fatal crashes; while the 30 mph zone had the most fatalities in 2015 (4 deaths), the 45 mph zone became the deadliest in 2016, with fatal crashes in this zone resulting in 7 deaths, up from just 1 the previous year.

Fatal crashes by zone: 25 mph: 1 of 555 (0.18%) · 30 mph: 1 of 258 (0.388%) · 35 mph: 2 of 340 (0.588%) · 40 mph: 2 of 202 (0.99%) · 45 mph: 7 of 205 (3.415%) · 50 mph: 1 of 56 (1.786%) · 65 mph: 2 of 232 (0.862%)

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: 2,103
Total persons involved: 4,611
Total vehicles involved: 3,452

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

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