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Yearly Traffic Safety Analysis

3,900 CRASHES IN
CONNECTICUT, CT
2016

All metrics benchmarked against2015

In 2016, Middlesex County recorded 3,900 total crashes, a 1.7% increase from the 3,836 crashes reported in 2015. While the total number of fatalities remained unchanged at 19 for both periods, the number of crashes resulting in serious injuries increased by 27.3% year-over-year, from 33 in 2015 to 42 in 2016.

3,900

1.7%was 3,836

Total Crash Events

19

Persons Killed

1,164

9.0%was 1,068

Persons Injured

287

-5.9%was 305

Hit-and-Run Crashes

Note: "Persons Killed" (19) counts individual fatalities across all crash events. "Fatal" in the severity table below (19) 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 safety metrics in Middlesex County showed a slight negative trend from 2015 to 2016. Total crashes rose by 1.7% from 3,836 to 3,900, and the number of people injured increased by 9.0% from 1,068 to 1,164. The number of fatalities, however, remained constant at 19 for both years.

287

Hit-and-Run Crashes — 2016

-5.9% vs prior (305)

Hit-and-run incidents in Middlesex County trended downward between 2015 and 2016. The total number of hit-and-run crashes decreased from 305 to 287. This resulted in a lower hit-and-run rate, which fell from 8.0% of all crashes in 2015 to 7.4% in 2016.

Vulnerable Road User Casualties

3

Pedestrians Killed

Prior: 250.0%

1

Cyclists Killed

Prior: 0%

15

Motorists Killed

Prior: 17-11.8%

0

Other Killed

Prior: 00.0%

25

Pedestrians Injured

Prior: 37-32.4%

14

Cyclists Injured

Prior: 955.6%

1,124

Motorists Injured

Prior: 1,02210.0%

1

Other Injured

Prior: 0%

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 remained largely consistent year-over-year, with Friday being the peak day for crashes in both 2016 (666 crashes) and 2015 (623 crashes). The peak hour for collisions shifted slightly later, moving from the 3 p.m. hour in 2015 (340 crashes) to the 4 p.m. hour in 2016 (351 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 worsened slightly in 2016. Although the proportion of fatal crashes was stable at 0.5% of all incidents, the share of crashes resulting in serious injury increased from 0.9% (33 crashes) in 2015 to 1.1% (42 crashes) in 2016. Consequently, the proportion of crashes with no reported injuries declined from 79.0% to 77.7% year-over-year.

Outcome by Severity (Crash Events)

Fatal19fatal crashes0.5%
5.6%prior 18
Serious Injury42serious injury crashes1.1%
27.3%prior 33
Minor Injury365minor injury crashes9.4%
4.9%prior 348
Possible Injury445possible injury crashes11.4%
9.9%prior 405
No Injury3,029no injury crashes77.7%
-0.1%prior 3,032

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

In 2016, a greater proportion of crashes occurred under favorable conditions compared to the prior year. Crashes on dry roads increased from 75.2% to 81.2% of the total, while incidents in clear weather rose from 77.1% to 81.8%. Correspondingly, crashes attributed to adverse road surface conditions like snow, ice, or slush decreased from 11.0% of all crashes in 2015 to just 6.3% in 2016. Lighting conditions at the time of crashes remained stable between the two periods.

Weather

Clear3,189 (82.2%)
7.8%prior 2,958
Rain298 (7.7%)
5.7%prior 282
Cloudy172 (4.4%)
-8.0%prior 187
Snow147 (3.8%)
-44.3%prior 264
Blowing Snow27 (0.7%)
-15.6%prior 32
Freezing Rain or Freezing Drizzle23 (0.6%)
-34.3%prior 35
Other9 (0.2%)
-30.8%prior 13
Fog, Smog, Smoke8 (0.2%)
-57.9%prior 19
Sleet or Hail6 (0.2%)
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

Daylight2,849 (73.4%)
3.5%prior 2,753
Dark-Lighted580 (14.9%)
0.2%prior 579
Dark-Not Lighted333 (8.6%)
-3.8%prior 346
Dusk66 (1.7%)
1.5%prior 65
Dawn33 (0.9%)
10.0%prior 30
Dark-Unknown Lighting12 (0.3%)
-36.8%prior 19
Other7 (0.2%)
-68.2%prior 22

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

Road Surface

Dry3,166 (81.4%)
9.8%prior 2,884
Wet458 (11.8%)
-4.0%prior 477
Snow126 (3.2%)
-55.8%prior 285
Ice / Frost75 (1.9%)
-10.7%prior 84
Slush46 (1.2%)
-14.8%prior 54
Sand7 (0.2%)
-41.7%prior 12
Other5 (0.1%)
-37.5%prior 8
Mud, Dirt, Gravel3 (0.1%)
-78.6%prior 14
Moving Water1 (0.0%)
Standing Water1 (0.0%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, including Ford, Toyota, and Honda, remained consistent between 2015 and 2016. An analysis of persons involved in crashes reveals a demographic shift, with a smaller proportion of individuals aged 20 and under (15.7% in 2016 vs. 18.3% in 2015). Conversely, the share of people aged 26-34 involved in crashes increased from 14.9% to 15.8%.

Top Vehicle Makes (7,089 vehicles)

1
FORD767 (10.8%)
5.1%prior 730
2
TOYOTA426 (6%)
45.9%prior 292
3
HOND371 (5.2%)
-16.3%prior 443
4
HONDA325 (4.6%)
31.6%prior 247
5
NISS319 (4.5%)
-14.0%prior 371
6
CHEV317 (4.5%)
-23.2%prior 413
7
JEEP287 (4%)
9.1%prior 263
8
NISSAN286 (4%)
27.1%prior 225
9
TOYO273 (3.9%)
99.3%prior 137
10
CHEVROLET217 (3.1%)
100.9%prior 108

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

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

Sex Distribution (8,681 persons with recorded sex)

Male4,750 (54.7%)
1.1%prior 4,700
Female3,931 (45.3%)
-0.7%prior 3,959

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 increased across most speed zones in 2016 compared to 2015, including in 35 mph zones (645 vs. 567) and 65 mph zones (671 vs. 644). The fatal crash rate within specific zones shifted notably; for instance, the rate in 25 mph zones increased from 0.15% to 0.47%, while the fatality rate for crashes in 65 mph zones decreased from 0.93% to 0.60%.

Fatal crashes by zone: 1 mph: 1 of 207 (0.483%) · 25 mph: 3 of 637 (0.471%) · 30 mph: 1 of 321 (0.312%) · 35 mph: 3 of 645 (0.465%) · 40 mph: 3 of 334 (0.898%) · 45 mph: 4 of 329 (1.216%) · 65 mph: 4 of 671 (0.596%)

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: 3,900
  • Total persons involved: 9,151
  • Total vehicles involved: 7,089

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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