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.
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YEAR-OVER-YEAR CRASH REPORT · ALASKA · 2019
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/2019-annual-report
Yearly Traffic Safety Analysis
62 CRASHES IN
ALASKA
2019
In 2019, Alaska recorded 62 fatal crashes resulting in 67 fatalities, a decrease from 69 fatal crashes and 80 fatalities in 2018. This represents a 10.1% decrease in fatal crashes and a 16.3% decrease in deaths year-over-year. One of the most significant shifts was the reduction in fatal crashes involving pedestrians, which fell from 14 in 2018 to 6 in 2019.
62
▼ -10.1%was 69
Total Crash Events
67
▼ -16.3%was 80
Persons Killed
59
▲ 9.3%was 54
Persons Injured
1
▼ -87.5%was 8
Hit-and-Run Crashes
Note: "Persons Killed" (67) counts individual fatalities across all crash events. "Fatal" in the severity table below (62) 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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Fatal traffic incidents in Alaska showed a downward trend from 2018 to 2019. The number of fatal crashes decreased by 10.1% from 69 to 62, and the number of people killed fell by 16.3% from 80 to 67. However, the number of people sustaining non-fatal injuries in these crashes increased slightly from 54 to 59.
1
Hit-and-Run Crashes — 2019
▼ -87.5% vs prior (8)
Fatal hit-and-run crashes saw a substantial decrease between the two periods. The number of such incidents fell from 8 in 2018 to 1 in 2019. As a result, the rate of hit-and-runs among all fatal crashes declined from 11.6% in 2018 to 1.6% in 2019.
Vulnerable Road User Casualties
6
Pedestrians Killed
2
Cyclists Killed
59
Motorists Killed
0
Pedestrians Injured
0
Cyclists Injured
59
Motorists Injured
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · 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 between the two periods. The peak day for fatal crashes moved from Tuesday (14 crashes) in 2018 to Friday (17 crashes) in 2019. The peak time for these incidents also changed, from 5 p.m. (7 crashes) in the prior year to a dual peak at midnight and 2 p.m. (7 crashes each) in the current period.
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)
Road & Environmental Conditions
In both 2019 and 2018, the majority of fatal crashes occurred in clear weather and daylight conditions. There were 33 fatal crashes in clear weather in 2019, compared to 36 in 2018. A notable shift occurred in adverse conditions, with fatal crashes in snow increasing from 3 to 8 year-over-year, while crashes in dark, unlit conditions decreased from 21 to 14.
Weather
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Weather condition at time of crash
Lighting
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Lighting condition field
Vehicles & Demographics
Ford, Chevrolet, and Dodge were the most common vehicle makes involved in fatal crashes in both 2019 and 2018. Ford-made vehicles were the most numerous in both periods, with 15 involved in 2019, down from 18 in 2018. A significant change was observed in motorcycle involvement, with Harley-Davidson vehicles dropping from 8 incidents in 2018 to 2 in 2019.
Top Vehicle Makes (90 vehicles)
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Vehicle unit records
1 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (147 persons with recorded sex)
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of fatal crashes across speed zones changed year-over-year. In 2019, the 55 mph speed zone saw the highest number of fatal crashes with 20 incidents, an increase from 13 in 2018. Conversely, fatal crashes in 45 mph zones decreased significantly, from 14 in 2018 to 6 in 2019. Crashes in 50 mph zones also increased, from 7 to 12.
Fatal crashes by zone: 20 mph: 1 of 1 (100%) · 25 mph: 2 of 2 (100%) · 30 mph: 2 of 2 (100%) · 35 mph: 3 of 3 (100%) · 40 mph: 5 of 5 (100%) · 45 mph: 6 of 6 (100%) · 50 mph: 12 of 12 (100%) · 55 mph: 20 of 20 (100%) · 60 mph: 2 of 2 (100%) · 65 mph: 7 of 7 (100%)
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Posted speed limit at crash location
Manner of Collision
In both periods, the most frequent manner of collision involved single-vehicle incidents where the first harmful event was not with another motor vehicle, accounting for 37 fatal crashes in 2019 and 45 in 2018. While angle collisions remained stable at 13 incidents in both years, front-to-front fatal collisions increased from 6 in 2018 to 11 in 2019.
Manner of Collision
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Crash-level records
Vehicle Type
Light pickups were the most common vehicle type in fatal crashes in both years, with their involvement increasing from 22 vehicles in 2018 to 28 in 2019. The most significant change was a sharp decline in motorcycle involvement, which fell from 14 in 2018 to 5 in 2019. In contrast, the number of large trucks (truck-tractors and single-unit trucks) involved in fatal crashes rose from 4 to 9.
Vehicle Type
Showing top 9 of 16 reported. 7 additional (8 total) not shown: Minivan (Chrysler Town and Country, Caravan, Grand Caravan, Voyager, Voyager, Honda-Odyssey, ...), Snowmobile, Convertible(excludes sun-roof,t-bar), ATV/ATC [All-Terrain Cycle], Unknown body type, Unknown (pickup style) light conventional truck type, 3-door/2-door hatchback.
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Vehicle unit records
Rural vs Urban
The geographic distribution of fatal crashes shifted from urban to rural areas. In 2019, 33 fatal crashes occurred on rural roads and 28 on urban roads. This is a reversal from 2018, which saw 31 rural and 38 urban fatal crashes. This change represents a 6.5% increase in fatal crashes in rural settings and a 26.3% decrease in urban ones.
Rural vs Urban
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Crash-level records
Roadway Functional Class
In 2019, Principal Arterials were the site of the most fatal crashes (25), an increase from 19 in 2018. Fatal crashes on Interstates decreased from 22 in 2018 to 15 in 2019. Combined, Interstates and Principal Arterials accounted for 40 of the 62 fatal crashes in 2019, compared to 41 of 69 in the prior year.
Roadway Functional Class
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Crash-level records
Roadway Ownership
State-owned highways continued to be the location for the majority of fatal crashes, though the number decreased from 56 in 2018 to 50 in 2019. Fatal crashes on county-owned roads remained unchanged with 8 incidents in both years. Fatal crashes on city or municipal roads saw a decline from 5 in 2018 to 2 in 2019.
Roadway Ownership
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Crash-level records
Person Type
Drivers were the largest group of individuals involved in fatal crashes in both periods, with 89 in 2019 compared to 104 in 2018. The number of involved pedestrians decreased significantly, from 14 in 2018 to 6 in 2019. In 2019, two bicyclists were involved in fatal crashes, whereas none were recorded in the prior year.
Person Type
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Crash-level records
Person Injury Severity
The total number of persons involved in fatal crashes decreased from 179 in 2018 to 147 in 2019. This included a drop in fatalities from 80 to 67. The number of individuals sustaining serious injuries remained constant at 29 for both years, while the count of uninjured persons decreased from 45 to 21.
Person Injury Severity
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Crash-level records
Occupant Safety Equipment
The use of shoulder and lap belts was reported for 71 occupants in 2019's fatal crashes, slightly down from 76 in 2018. The number of individuals recorded with 'None Used/Not Applicable' safety equipment increased from 30 in 2018 to 41 in 2019. The 2018 data included 16 individuals with 'No Helmet', a category not prominent in 2019, which aligns with the lower number of motorcycle fatalities.
Occupant Safety Equipment
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Person-level records linked to crash events
Point of Impact
Frontal impacts ('12 Clock Point') were the most common point of initial impact for vehicles in fatal crashes in both years, recorded on 47 vehicles in 2019 and 54 in 2018. The number of vehicles involved in a non-collision event as the first harmful event, such as a rollover, increased from 12 in 2018 to 16 in 2019.
Point of Impact
"Other" combines 5 smaller categories (8 records): Left (3), Right-Front Side (2), Left-Front Side (1), Reported as Unknown (1), 7 Clock Point (1).
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · Vehicle unit records
Vehicles Per Crash
Single-vehicle incidents remained the most common type of fatal crash, though their number decreased from 41 in 2018 to 37 in 2019. The count of two-vehicle fatal crashes was identical at 22 for both years. In 2019, no fatal crashes involved more than three vehicles, unlike 2018 which recorded one four-vehicle and one five-vehicle fatal crash.
Vehicles Per Crash
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
- Report generated: August 5, 2026
Data Coverage
- Reporting period: 2019-01-01 through 2019-12-31 (365 days)
- Geographic scope: Alaska
- Total crash records analyzed: 62
- Total persons involved: 147
- Total vehicles involved: 90
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: 2019." Published August 5, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: NHTSA FARS (Fatal Crashes), NHTSA FARS Open Data. Available at: https://thatcarhitme.com/crash-data/alaska/fatal/statewide/2019-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: 2019-01-01 – 2019-12-31
Generated: August 5, 2026 · All rights reserved
Someone you love in this data? We’ll help you find out if you have a case.
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