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Yearly Traffic Safety Analysis

6,629 CRASHES IN
CONNECTICUT, CT
2016

All metrics benchmarked against2015

In New London County, total traffic crashes remained stable, decreasing by less than 1% from 6,693 in 2015 to 6,629 in 2016. Fatalities were unchanged at 27 for both years, while total injuries saw a minor increase of 1.4%. The most significant year-over-year change was a 19.9% reduction in crashes involving speeding, which fell from 967 incidents in 2015 to 775 in 2016.

6,629

-1.0%was 6,693

Total Crash Events

27

Persons Killed

1,990

1.4%was 1,962

Persons Injured

745

3.0%was 723

Hit-and-Run Crashes

Note: "Persons Killed" (27) counts individual fatalities across all crash events. "Fatal" in the severity table below (25) 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 in crash volume was largely stable, with a slight decrease of 64 incidents, or 0.96%, from 2015 to 2016. While total crashes remained consistent, the number of people injured rose slightly from 1,962 to 1,990. The number of fatalities held steady at 27 in both annual periods.

745

Hit-and-Run Crashes — 2016

3.0% vs prior (723)

Hit-and-run crashes trended upward between the two periods. The total count of hit-and-run incidents increased from 723 in 2015 to 745 in 2016. The corresponding hit-and-run rate, which measures these events as a percentage of all crashes, also rose from 10.8% to 11.2%.

Vulnerable Road User Casualties

4

Pedestrians Killed

Prior: 2100.0%

1

Cyclists Killed

Prior: 0%

22

Motorists Killed

Prior: 25-12.0%

56

Pedestrians Injured

Prior: 4816.7%

27

Cyclists Injured

Prior: 2317.4%

1,907

Motorists Injured

Prior: 1,8900.9%

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 timing of crashes showed remarkable consistency year-over-year. Friday remained the day with the highest crash volume, accounting for 1,133 incidents in 2016 compared to 1,096 in 2015. Similarly, the 4 p.m. hour was the peak time for collisions in both periods, with 577 crashes in 2016 and 587 in 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 overall severity of crashes showed a slight increase despite stable fatal crash numbers. While the number of fatal crashes remained unchanged at 25 in both 2016 and 2015, crashes resulting in serious injuries rose from 72 to 85. The proportion of crashes involving any injury (fatal, serious, minor, or possible) increased from 21.5% in 2015 to 22.4% in 2016.

Severity is per crash event (most severe injury). 25 fatal crash events resulted in 27 persons killed.

Outcome by Severity (Crash Events)

Fatal25fatal crashes0.4%
0.0%prior 25
Serious Injury85serious injury crashes1.3%
18.1%prior 72
Minor Injury663minor injury crashes10%
5.7%prior 627
Possible Injury713possible injury crashes10.8%
-0.1%prior 714
No Injury5,143no injury crashes77.6%
-2.1%prior 5,255

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

Crash data suggests a shift toward incidents occurring in clearer conditions compared to the prior year. The proportion of crashes on snowy roads decreased from 7.8% in 2015 to 4.7% in 2016, and collisions on wet surfaces also declined from 13.6% to 12.5%. Correspondingly, the share of crashes on dry roads increased from 72.8% to 78.7% of all incidents.

Weather

Clear5,294 (80.2%)
4.7%prior 5,057
Rain567 (8.6%)
-4.4%prior 593
Snow329 (5.0%)
-32.9%prior 490
Cloudy269 (4.1%)
7.2%prior 251
Blowing Snow52 (0.8%)
-44.1%prior 93
Freezing Rain or Freezing Drizzle41 (0.6%)
-35.9%prior 64
Fog, Smog, Smoke24 (0.4%)
-44.2%prior 43
Sleet or Hail14 (0.2%)
-17.6%prior 17
Other9 (0.1%)
-47.1%prior 17
Severe Crosswinds4 (0.1%)

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

Lighting

Daylight4,591 (69.6%)
-0.9%prior 4,635
Dark-Lighted1,106 (16.8%)
2.8%prior 1,076
Dark-Not Lighted661 (10.0%)
-6.9%prior 710
Dusk122 (1.9%)
-4.7%prior 128
Dawn63 (1.0%)
5.0%prior 60
Dark-Unknown Lighting28 (0.4%)
12.0%prior 25
Other22 (0.3%)
100.0%prior 11

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

Road Surface

Dry5,216 (78.9%)
7.1%prior 4,872
Wet828 (12.5%)
-9.3%prior 913
Snow313 (4.7%)
-40.3%prior 524
Ice / Frost148 (2.2%)
-14.5%prior 173
Slush72 (1.1%)
-46.3%prior 134
Mud, Dirt, Gravel12 (0.2%)
-7.7%prior 13
Other7 (0.1%)
-50.0%prior 14
Standing Water7 (0.1%)
-30.0%prior 10
Sand4 (0.1%)
-66.7%prior 12
Moving Water2 (0.0%)

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

Vehicles & Demographics

The top three vehicle makes involved in crashes—Ford, Honda, and Chevrolet—remained consistent in both rank and volume year-over-year. Analysis of persons involved shows a stable distribution across most age groups. However, the proportion of individuals aged 65 and older increased, accounting for 10.4% of all persons involved in crashes in 2016, up from 9.3% in 2015.

Top Vehicle Makes (11,891 vehicles)

1
FORD1,462 (12.3%)
-1.7%prior 1,487
2
HOND718 (6%)
-11.5%prior 811
3
CHEV702 (5.9%)
-10.7%prior 786
4
NISS529 (4.4%)
-13.0%prior 608
5
TOYT509 (4.3%)
-33.2%prior 762
6
JEEP451 (3.8%)
0.9%prior 447
7
HONDA421 (3.5%)
52.5%prior 276
8
HYUN388 (3.3%)
-6.3%prior 414
9
TOYOTA379 (3.2%)
33.9%prior 283
10
DODG377 (3.2%)
-4.8%prior 396

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

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

Sex Distribution (15,063 persons with recorded sex)

Male8,220 (54.6%)
-3.0%prior 8,471
Female6,843 (45.4%)
1.4%prior 6,750

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

Speed Limit Zones

There was a notable shift in crashes away from high-speed zones. Collisions in zones posted at 65 mph or higher decreased by 10.8%, from 1,107 to 987 incidents. Conversely, fatal crash locations changed, with fatalities in the 45 mph zone increasing from 2 to 8, while those in the 35 mph zone decreased from 7 to 2.

Fatal crashes by zone: 15 mph: 1 of 71 (1.408%) · 25 mph: 5 of 2,296 (0.218%) · 30 mph: 3 of 579 (0.518%) · 35 mph: 2 of 1,045 (0.191%) · 45 mph: 8 of 670 (1.194%) · 50 mph: 1 of 177 (0.565%) · 65 mph: 4 of 759 (0.527%) · 88 mph: 1 of 208 (0.481%)

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: 6,629
  • Total persons involved: 15,893
  • Total vehicles involved: 11,891

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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New London County, CT Crash Report — 2016 | ThatCarHitMe.com