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.
If someone you love is part of this data, call us.
We'll connect you with a vetted Alaska attorney who handles fatal-crash and wrongful death cases and will evaluate whether you have a case.
ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · ALASKA · 2017
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/2017-annual-report
Yearly Traffic Safety Analysis
75 CRASHES IN
ALASKA
2017
In 2017, the state recorded 75 fatal crashes, a 3.8% decrease from the 78 fatal crashes documented in 2016. These incidents resulted in 79 fatalities in 2017, compared to 84 in the prior year. The most notable year-over-year change was a 75% increase in fatal hit-and-run crashes, which rose from 4 to 7 incidents.
75
▼ -3.8%was 78
Total Crash Events
79
▼ -6.0%was 84
Persons Killed
69
Persons Injured
7
▲ 75.0%was 4
Hit-and-Run Crashes
Note: "Persons Killed" (79) counts individual fatalities across all crash events. "Fatal" in the severity table below (75) 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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, key traffic safety metrics showed a slight improvement year-over-year. The number of fatal crashes decreased from 78 in 2016 to 75 in 2017, and the number of people killed in these crashes fell from 84 to 79. The total number of individuals injured in these fatal incidents remained unchanged at 69 for both periods.
7
Hit-and-Run Crashes — 2017
▲ 75.0% vs prior (4)
Fatal hit-and-run crashes showed a significant upward trend. The number of such incidents increased by 75%, rising from 4 in 2016 to 7 in 2017. As a result, the hit-and-run rate as a share of all fatal crashes grew from 5.1% to 9.3% year-over-year.
Vulnerable Road User Casualties
14
Pedestrians Killed
1
Cyclists Killed
62
Motorists Killed
2
Other Killed
1
Pedestrians Injured
0
Cyclists Injured
68
Motorists Injured
0
Other Injured
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · 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 shifted slightly between the two years. The peak day for fatal crashes moved from Friday (15 crashes) in 2016 to Saturday (18 crashes) in 2017. However, the peak hour for these incidents remained consistent at midnight in both periods, with 8 fatal crashes occurring at that hour in 2017, down from 9 in the previous year.
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)
Road & Environmental Conditions
Comparing conditions, fatal crashes in clear weather were stable, with 40 in 2017 versus 41 in 2016. However, incidents in rainy conditions decreased from 11 to 3, while those in snow increased from 2 to 7. Regarding lighting, fatal crashes in daylight fell from 37 to 31, and the proportion of crashes occurring in darkness (lighted or unlighted) rose slightly, accounting for 54% of incidents in 2017 compared to 49% in 2016.
Weather
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Weather condition at time of crash
Lighting
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Lighting condition field
Vehicles & Demographics
Ford (18 vehicles) and Chevrolet (12 vehicles) were the top two vehicle makes involved in fatal crashes in 2017, though both saw their counts decrease from 20 and 19, respectively, in 2016. Analysis of persons involved shows the 26-34 age group was the largest in both years (37 individuals in 2017 vs. 35 in 2016). Notably, the number of individuals aged 16-20 involved in fatal crashes dropped by more than half, from 29 in 2016 to 12 in 2017.
Top Vehicle Makes (103 vehicles)
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Vehicle unit records
Sex Distribution (189 persons with recorded sex)
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Person-level records linked to crash events
Speed Limit Zones
There was a noticeable shift in fatal crashes toward higher speed zones in 2017. The 65 mph zone became the most common location for fatal crashes with 18 incidents, an increase from 11 in 2016. Conversely, fatal crashes in 55 mph zones, which were the most frequent in 2016 with 25 incidents, decreased to 16 in 2017.
Fatal crashes by zone: 15 mph: 1 of 1 (100%) · 20 mph: 6 of 6 (100%) · 25 mph: 5 of 5 (100%) · 30 mph: 1 of 1 (100%) · 35 mph: 2 of 2 (100%) · 40 mph: 6 of 6 (100%) · 45 mph: 13 of 13 (100%) · 50 mph: 3 of 3 (100%) · 55 mph: 16 of 16 (100%) · 60 mph: 2 of 2 (100%) · 65 mph: 18 of 18 (100%)
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Posted speed limit at crash location
Manner of Collision
Single-vehicle crashes, classified as "Not a Collision with Motor Vehicle In-Transport," were the predominant type of fatal incident in both periods. This category accounted for 51 of 75 fatal crashes (68%) in 2017, a slight decrease from 54 of 78 (69.2%) in 2016. Among crashes involving multiple vehicles, angle collisions were the most common, increasing from 11 incidents in 2016 to 13 in 2017.
Manner of Collision
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Crash-level records
Vehicle Type
In 2017, sedans (29 vehicles) and SUVs/utility vehicles (27 vehicles) were the most common types involved in fatal crashes. This marks a shift from 2016, when pickup trucks were the most frequent type with 31 vehicles involved; in 2017, pickup involvement dropped to 20 vehicles. The number of motorcycles involved in fatal crashes was stable at 6 for both years.
Vehicle Type
Showing top 9 of 19 reported. 10 additional (15 total) not shown: ATV/ATC [All-Terrain Cycle], Utility station wagon (includes suburban limousines, Suburban, Travellall, Grand Wagoneer), Medium/heavy Pickup (>10,000 lbs. GVWR), Truck-tractor (Cab only, or with any number of trailing unit; any weight), Single-unit straight truck or Cab-Chassis (GVWR unknown), Medium/heavy truck based motorhome, Large Van-Includes van-based buses (B150-B350, Sportsman, Royal Maxiwagon, Ram, Tradesman,...), 3-door/2-door hatchback, 5-door/4-door hatchback, Off-road Motorcycle.
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Vehicle unit records
Rural vs Urban
Fatal crashes continued to occur more frequently in rural areas than in urban ones. In 2017, there were 43 fatal crashes on rural roads and 31 on urban roads, compared to 47 rural and 30 urban crashes in 2016. The share of fatal crashes in rural locations was 58% in 2017, a slight decrease from 61% in the prior year, but still representing a majority of all fatal incidents.
Rural vs Urban
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Crash-level records
Roadway Functional Class
Interstates were the leading location for fatal crashes in both years, with 26 incidents recorded in 2017 and 2016. Arterial roadways (Principal and Minor combined) saw an increase in fatal crashes from 23 in 2016 to 27 in 2017, becoming the second-most common road class for these incidents. Fatal crashes on Major Collector roads decreased from 13 to 7 year-over-year.
Roadway Functional Class
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Crash-level records
Roadway Ownership
The vast majority of fatal crashes occurred on roadways maintained by the State Highway Agency in both periods. The count of fatal crashes on these state roads increased from 59 in 2016 to 65 in 2017. In contrast, fatal crashes on roads owned by both county and city/municipal agencies decreased, falling from 8 to 5 and 8 to 3, respectively.
Roadway Ownership
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Crash-level records
Person Type
Drivers constituted the largest group of individuals involved in fatal crashes, with 103 in 2017 compared to 108 in 2016. The number of passengers involved saw a small decline from 71 to 68. Notably, the number of pedestrians involved in these fatal incidents increased from 12 in 2016 to 15 in 2017.
Person Type
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Crash-level records
Person Injury Severity
Of the 189 people involved in fatal crashes in 2017, 79 suffered fatal injuries, a decrease from 84 fatalities among 195 people in 2016. The number of individuals sustaining serious injuries also fell from 37 to 26. Conversely, the count of those with minor injuries rose from 19 in 2016 to 26 in 2017.
Person Injury Severity
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Crash-level records
Occupant Safety Equipment
The data indicates improved safety equipment usage among occupants in fatal crashes. The number of individuals recorded as using a shoulder and lap belt increased from 73 in 2016 to 92 in 2017. Correspondingly, the number of occupants reported as using no restraint at all dropped significantly from 65 to 31. The count of motorcyclists not wearing a helmet increased slightly from 9 to 11.
Occupant Safety Equipment
"Other" combines 4 smaller categories (4 records): Unknown if Helmet Worn (1), Restraint Used - Type Unknown (1), DOT-Compliant Motorcycle Helmet (1), Child Restraint System - Rear Facing (1).
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Person-level records linked to crash events
Point of Impact
Frontal impacts, recorded as '12 Clock Point,' were the most frequent initial point of impact for vehicles in fatal crashes in both years, rising from 46 incidents in 2016 to 49 in 2017. The number of 'Non-Collision' events, such as rollovers, decreased from 17 in 2016 to 9 in 2017. In 2017, front-corner impacts ('1 Clock Point' and '11 Clock Point') were the next most common, with a combined 19 incidents.
Point of Impact
"Other" combines 7 smaller categories (10 records): 2 Clock Point (2), 6 Clock Point (2), Left (2), 8 Clock Point (1), Right-Front Side (1), Top (1), 4 Clock Point (1).
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · Vehicle unit records
Vehicles Per Crash
Single-vehicle incidents remained the most common type of fatal crash, accounting for 50 of 75 crashes (66.7%) in 2017, compared to 53 of 78 (67.9%) in 2016. The number of two-vehicle fatal crashes was stable, with 22 in 2017 versus 21 in the prior year. While 2016 saw two larger incidents involving four and five vehicles, the largest multi-vehicle crash in 2017 involved three vehicles.
Vehicles Per Crash
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
- Report generated: August 5, 2026
Data Coverage
- Reporting period: 2017-01-01 through 2017-12-31 (365 days)
- Geographic scope: Alaska
- Total crash records analyzed: 75
- Total persons involved: 189
- Total vehicles involved: 103
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: 2017." Published August 5, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: NHTSA FARS (Fatal Crashes), NHTSA FARS Open Data. Available at: https://thatcarhitme.com/crash-data/alaska/fatal/statewide/2017-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: 2017-01-01 – 2017-12-31
Generated: August 5, 2026 · All rights reserved
Someone you love in this data? We’ll help you find out if you have a case.
Call our intake team. We connect you to an vetted Alaska attorney who handles fatal-crash and wrongful death cases, and who will treat your family’s situation with care.
Always free for accident victims.