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Yearly Traffic Safety Analysis

2,813 CRASHES IN
CONNECTICUT, CT
2016

All metrics benchmarked against2015

In 2016, Tolland County recorded 2,813 traffic crashes, a 3.8% increase from the 2,710 crashes reported in 2015. While total fatalities decreased from 17 to 14, the number of total injuries rose by 21.0% from 826 to 999. The most significant year-over-year change was a 150% increase in crashes resulting in serious injuries, which grew from 16 in 2015 to 40 in 2016.

2,813

3.8%was 2,710

Total Crash Events

14

-17.6%was 17

Persons Killed

999

20.9%was 826

Persons Injured

231

0.4%was 230

Hit-and-Run Crashes

Note: "Persons Killed" (14) 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 · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crashes in Tolland County showed a slight upward trend, increasing by 3.8% from 2,710 in 2015 to 2,813 in 2016. While fatalities decreased by 17.6% year-over-year, the number of people injured increased significantly by 21.0%, rising from 826 to 999.

231

Hit-and-Run Crashes — 2016

0.4% vs prior (230)

The number of hit-and-run incidents in Tolland County remained stable, with 231 incidents in 2016 compared to 230 in 2015. As a percentage of total crashes, the hit-and-run rate saw a marginal decrease, falling from 8.5% in 2015 to 8.2% in 2016, indicating a relatively stable trend.

Vulnerable Road User Casualties

3

Pedestrians Killed

Prior: 1200.0%

0

Cyclists Killed

Prior: 00.0%

11

Motorists Killed

Prior: 16-31.3%

26

Pedestrians Injured

Prior: 1752.9%

12

Cyclists Injured

Prior: 771.4%

961

Motorists Injured

Prior: 80219.8%

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 consistent year-over-year. Friday was the peak day for crashes in both 2016 (537 crashes) and 2015 (462 crashes). The 3 PM hour was also the peak hour in both periods, with 256 crashes in 2016 and 225 in 2015. Crash volumes on the peak day, Friday, increased by 16.2% from the prior 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 proportion of crashes resulting in any injury increased from 22.6% in 2015 to 25.4% in 2016. This change was driven by a significant rise in the share of serious injury crashes, which more than doubled from 0.6% to 1.4% of all incidents. The rate of fatal crashes saw a slight decrease, falling from 0.55% of all crashes in 2015 to 0.50% in 2016.

Outcome by Severity (Crash Events)

Fatal14fatal crashes0.5%
-6.7%prior 15
Serious Injury40serious injury crashes1.4%
150.0%prior 16
Minor Injury358minor injury crashes12.7%
12.9%prior 317
Possible Injury317possible injury crashes11.3%
14.0%prior 278
No Injury2,084no injury crashes74.1%
0.0%prior 2,084

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 majority of crashes in both periods occurred during daylight and in clear weather. The proportion of crashes happening in daylight remained steady at approximately 68% for both 2015 and 2016. There was a slight increase in the share of crashes occurring in rainy conditions, from 7.7% in 2015 to 9.5% in 2016. Conversely, the proportion of crashes on snowy road surfaces decreased from 9.3% to 7.5%.

Weather

Clear2,153 (76.8%)
6.2%prior 2,028
Rain266 (9.5%)
26.7%prior 210
Snow197 (7.0%)
2.1%prior 193
Cloudy106 (3.8%)
-5.4%prior 112
Blowing Snow40 (1.4%)
-14.9%prior 47
Freezing Rain or Freezing Drizzle19 (0.7%)
-52.5%prior 40
Fog, Smog, Smoke14 (0.5%)
-6.7%prior 15
Severe Crosswinds4 (0.1%)
Sleet or Hail4 (0.1%)
-66.7%prior 12
Other2 (0.1%)
-77.8%prior 9

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

Lighting

Daylight1,935 (69.2%)
4.2%prior 1,857
Dark-Not Lighted400 (14.3%)
12.0%prior 357
Dark-Lighted383 (13.7%)
-4.0%prior 399
Dusk33 (1.2%)
-10.8%prior 37
Dawn23 (0.8%)
-8.0%prior 25
Dark-Unknown Lighting17 (0.6%)
41.7%prior 12
Other5 (0.2%)

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

Road Surface

Dry2,073 (73.8%)
9.3%prior 1,896
Wet393 (14.0%)
4.5%prior 376
Snow212 (7.5%)
-15.9%prior 252
Ice / Frost60 (2.1%)
-35.5%prior 93
Slush48 (1.7%)
-9.4%prior 53
Mud, Dirt, Gravel12 (0.4%)
-33.3%prior 18
Other5 (0.2%)
Sand4 (0.1%)
-42.9%prior 7
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 top vehicle makes involved in crashes remained largely consistent between 2015 and 2016, with Ford being the most common make in both years, increasing its count from 557 to 605. The distribution of persons involved in crashes by age group also showed little change year-over-year. The 26-34 age group represented the largest share of people involved in crashes in both periods, accounting for approximately 15.5% of the total.

Top Vehicle Makes (4,924 vehicles)

1
FORD605 (12.3%)
8.6%prior 557
2
HOND380 (7.7%)
-15.0%prior 447
3
TOYO364 (7.4%)
102.2%prior 180
4
CHEV266 (5.4%)
-8.3%prior 290
5
NISS262 (5.3%)
-2.6%prior 269
6
SUBA188 (3.8%)
1.1%prior 186
7
JEEP186 (3.8%)
-1.6%prior 189
8
HYUN161 (3.3%)
-16.6%prior 193
9
HONDA150 (3%)
97.4%prior 76
10
TOYOTA148 (3%)
89.7%prior 78

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

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

Sex Distribution (6,543 persons with recorded sex)

Male3,666 (56.0%)
4.5%prior 3,509
Female2,877 (44.0%)
4.8%prior 2,745

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 shifted slightly towards mid-range speeds. The number of crashes in zones between 35 and 50 mph increased from 1,292 in 2015 to 1,422 in 2016. Conversely, crashes in zones over 50 mph decreased from 321 to 297. While the number of fatal crashes in the 50 mph speed zone decreased from 5 to 3, fatal crashes in the 35 mph zone increased from 2 to 3.

Fatal crashes by zone: 1 mph: 1 of 163 (0.613%) · 20 mph: 1 of 7 (14.286%) · 35 mph: 3 of 639 (0.469%) · 40 mph: 3 of 373 (0.804%) · 45 mph: 1 of 364 (0.275%) · 50 mph: 3 of 46 (6.522%) · 88 mph: 2 of 39 (5.128%)

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 21, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: connecticut, CT
  • Total crash records analyzed: 2,813
  • Total persons involved: 6,848
  • Total vehicles involved: 4,924

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 21, 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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