Fatal Crashes Only
This report covers fatal crashes only, from the NHTSA Fatality Analysis Reporting System — the federal census of every crash on a US public road that killed someone within 30 days. It does not include injury or property-damage-only crashes, and its totals are not comparable with the all-severity crash reports published elsewhere on this site.
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YEAR-OVER-YEAR CRASH REPORT · ALASKA · 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/alaska/fatal/statewide/2016-annual-report
Yearly Traffic Safety Analysis
78 CRASHES IN
ALASKA
2016
Fatal crashes in Alaska increased by 30% from 60 in 2015 to 78 in 2016, with fatalities rising from 65 to 84. This upward trend was marked by several notable shifts in crash characteristics. The most significant year-over-year change was the number of fatal crashes involving speeding, which increased by 83% from 18 to 33 incidents.
78
▲ 30.0%was 60
Total Crash Events
84
▲ 29.2%was 65
Persons Killed
69
▲ 25.5%was 55
Persons Injured
4
▲ 33.3%was 3
Hit-and-Run Crashes
Note: "Persons Killed" (84) counts individual fatalities across all crash events. "Fatal" in the severity table below (78) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Analysis of fatal crash data from 2015 to 2016 reveals a significant upward trend. The number of fatal crashes rose by 30%, from 60 to 78. Correspondingly, the number of people killed increased by 29% from 65 to 84, and the number of people injured in these incidents grew by 25.5%, from 55 to 69.
4
Hit-and-Run Crashes — 2016
▲ 33.3% vs prior (3)
The number of fatal hit-and-run crashes saw a slight increase from 3 incidents in 2015 to 4 in 2016. The corresponding rate remained stable, moving from 5.0% of fatal crashes in the prior year to 5.1% in the current year. This indicates that the hit-and-run trend was relatively flat despite the overall increase in fatal crashes.
Vulnerable Road User Casualties
12
Pedestrians Killed
1
Cyclists Killed
71
Motorists Killed
0
Pedestrians Injured
0
Cyclists Injured
69
Motorists Injured
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 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 fatal crashes showed some shifts between the two years. In 2016, Friday was the peak day with 15 fatal crashes, a shift from 2015 when Saturday was the peak day with 15 incidents. The midnight hour (12a) was a peak time for fatal crashes in both periods, recording 9 incidents in 2016 and sharing the top spot with 8 incidents in 2015.
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)
Road & Environmental Conditions
Lighting conditions saw a notable shift, with fatal crashes in 'Dark - Not Lighted' areas increasing from 8 incidents in 2015 to 21 in 2016. Fatal crashes in daylight also rose from 31 to 37. Regarding weather, clear conditions accounted for the majority of fatal crashes in both years (41 in 2016, 35 in 2015), with the number of incidents increasing across clear, cloudy, and rainy conditions.
Weather
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Weather condition at time of crash
Lighting
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Lighting condition field
Vehicles & Demographics
Comparing vehicles involved in fatal crashes, Ford was the most frequent make in both years, with 20 vehicles in 2016 and 18 in 2015. The number of Chevrolet vehicles involved more than doubled from 9 to 19. Analysis of persons involved shows a significant increase in the 16-20 age group, which grew from 9 individuals in 2015 to 29 in 2016.
Top Vehicle Makes (109 vehicles)
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Vehicle unit records
7 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (189 persons with recorded sex)
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Person-level records linked to crash events
Speed Limit Zones
Fatal crashes in 55 mph zones remained the most frequent, increasing from 18 incidents in 2015 to 25 in 2016. Crashes also increased in 65 mph zones, from 9 to 11. Because this dataset comprises only fatal crashes, the fatality rate for incidents within any speed zone is 100% for both periods, precluding a comparative analysis of fatality rates by zone.
Fatal crashes by zone: 5 mph: 1 of 1 (100%) · 20 mph: 2 of 2 (100%) · 25 mph: 9 of 9 (100%) · 30 mph: 1 of 1 (100%) · 35 mph: 5 of 5 (100%) · 40 mph: 4 of 4 (100%) · 45 mph: 10 of 10 (100%) · 50 mph: 5 of 5 (100%) · 55 mph: 25 of 25 (100%) · 60 mph: 1 of 1 (100%) · 65 mph: 11 of 11 (100%)
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Posted speed limit at crash location
Manner of Collision
The most common type of fatal crash in both periods was 'Not a Collision with Motor Vehicle In-Transport,' which includes single-vehicle incidents like rollovers or striking fixed objects. This category increased from 41 incidents (68.3% of fatal crashes) in 2015 to 54 incidents (69.2%) in 2016. Angle collisions also saw a notable increase, rising from 6 to 11 incidents year-over-year.
Manner of Collision
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Crash-level records
Vehicle Type
Standard pickups were the vehicle body type most frequently involved in fatal crashes in both years, increasing from 19 in 2015 to 25 in 2016. Fatal crashes involving compact utility vehicles doubled from 10 to 20. Conversely, the number of motorcycles involved in fatal crashes decreased significantly from 11 in 2015 to 4 in 2016.
Vehicle Type
Showing top 9 of 26 reported. 17 additional (20 total) not shown: Truck-tractor (Cab only, or with any number of trailing unit; any weight), Unknown body type, Minivan (Chrysler Town and Country, Caravan, Grand Caravan, Voyager, Voyager, Honda-Odyssey, ...), Single-unit straight truck or Cab-Chassis (GVWR unknown), Snowmobile, 2-door sedan,hardtop,coupe, Utility station wagon (includes suburban limousines, Suburban, Travellall, Grand Wagoneer), 5-door/4-door hatchback, Cab Chassis Based (includes Rescue Vehicle, Light Stake, Dump, and Tow Truck), Farm equipment other than trucks, Medium/heavy Pickup (>10,000 lbs. GVWR), Off-road Motorcycle (2-wheel), Other motored cycle type (mini-bikes, motor scooters, pocket motorcycles, "pocket bikes"), Sedan/Hardtop, number of doors unknown, Single-unit straight truck or Cab-Chassis (10,000 lbs. < GVWR < or = 19,500 lbs.), Single-unit straight truck or Cab-Chassis (19,500 lbs. < GVWR < or = 26,000 lbs.), Single-unit straight truck or Cab-Chassis (GVWR > 26,000 lbs.).
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Vehicle unit records
Rural vs Urban
There was a significant shift in the location of fatal crashes from urban to rural roadways. While fatal crashes in urban areas remained stable at 30 incidents in both years, rural fatal crashes increased by 62%, from 29 in 2015 to 47 in 2016. This made rural roads the location for 61% of all fatal crashes in 2016, a reversal from the nearly even split observed in the prior year.
Rural vs Urban
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Crash-level records
Roadway Functional Class
Interstate highways were the site of the most fatal crashes in both periods, with counts increasing from 22 in 2015 to 26 in 2016. Fatal crashes on Minor Arterial roads doubled from 6 to 12, and incidents on Major Collector roads also rose from 8 to 13. Fatalities on Principal Arterial roads decreased slightly from 13 to 11.
Roadway Functional Class
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Crash-level records
Roadway Ownership
State Highway Agency-owned roadways accounted for the majority of fatal crashes, with incidents on these roads increasing from 48 in 2015 to 59 in 2016. This represents 76% of all fatal crashes in the current period. Fatal crashes on roads owned by county and city/municipal agencies also saw slight increases but remained a much smaller portion of the total.
Roadway Ownership
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Crash-level records
Person Type
Drivers were the largest group of individuals involved in fatal crashes, with their numbers increasing from 88 in 2015 to 108 in 2016. The number of passengers involved also grew from 57 to 71. The number of pedestrians involved remained stable, with 13 in 2015 and 12 in 2016, while one bicyclist was involved in 2016 compared to none in the prior year.
Person Type
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Crash-level records
Person Injury Severity
The number of persons killed (K) in fatal crashes rose from 65 in 2015 to 84 in 2016. More dramatically, the number of persons sustaining serious injuries (A) more than doubled, increasing from 16 to 37. This indicates a sharp rise in severe outcomes for survivors of fatal crashes compared to the previous year.
Person Injury Severity
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Crash-level records
Occupant Safety Equipment
A concerning trend emerged in safety equipment usage, where the number of occupants reported as using no restraint ('None Used') more than tripled, from 18 in 2015 to 65 in 2016. While the use of shoulder and lap belts also increased from 61 to 73, the growth in non-use was far more pronounced. The number of motorcyclists reported with 'No Helmet' was identical at 9 in both years.
Occupant Safety Equipment
"Other" combines 1 smaller categories (1 records): Helmet, Unknown if DOT Compliant (1).
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Person-level records linked to crash events
Point of Impact
Frontal impacts, coded as '12 Clock Point', were the most frequent initial impact point in both years, with an identical count of 46 incidents. 'Non-Collision' events, which can include rollovers, nearly doubled from 9 in 2015 to 17 in 2016. This indicates an increase in single-vehicle loss-of-control crashes that did not involve an initial impact with another vehicle.
Point of Impact
"Other" combines 10 smaller categories (13 records): Undercarriage (2), 2 Clock Point (2), 6 Clock Point (2), 8 Clock Point (1), 5 Clock Point (1), 7 Clock Point (1), 10 Clock Point (1), Left (1), Left-Back Side (1), Right (1).
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Vehicle unit records
Vehicles Per Crash
Single-vehicle incidents were the most common type of fatal crash, increasing from 41 in 2015 to 53 in 2016. Two-vehicle fatal crashes also increased from 13 to 21. Despite the rise in counts, the proportion of single-vehicle crashes remained stable, accounting for 68% of all fatal crashes in both years.
Vehicles Per Crash
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2016-01-01 to 2016-12-31 · Crash-level records
Data Sources & Methodology
Primary Data Source
All crash data in this report is sourced from NHTSA FARS (Fatal Crashes), accessed programmatically via the NHTSA FARS 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: NHTSA FARS 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 5, 2026
Data Coverage
- Reporting period: 2016-01-01 through 2016-12-31 (366 days)
- Geographic scope: Alaska
- Total crash records analyzed: 78
- Total persons involved: 195
- Total vehicles involved: 109
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). "Alaska Crash Intelligence Report: 2016." Published August 5, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: NHTSA FARS (Fatal Crashes), NHTSA FARS Open Data. Available at: https://thatcarhitme.com/crash-data/alaska/fatal/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: NHTSA FARS (Fatal Crashes) · NHTSA FARS
Period: 2016-01-01 – 2016-12-31
Generated: August 5, 2026 · All rights reserved
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