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 · 2018
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/2018-annual-report
Yearly Traffic Safety Analysis
69 CRASHES IN
ALASKA
2018
In 2018, Alaska recorded 69 fatal crashes, an 8% decrease from the 75 fatal crashes recorded in 2017. These incidents resulted in 80 fatalities in 2018, nearly unchanged from 79 the previous year. The most significant year-over-year change was an 83% increase in fatal motorcycle crashes, which rose from 6 in 2017 to 11 in 2018.
69
▼ -8.0%was 75
Total Crash Events
80
▲ 1.3%was 79
Persons Killed
54
▼ -21.7%was 69
Persons Injured
8
▲ 14.3%was 7
Hit-and-Run Crashes
Note: "Persons Killed" (80) counts individual fatalities across all crash events. "Fatal" in the severity table below (69) 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 · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, the number of fatal crashes in Alaska trended downward, decreasing by 8% from 75 in 2017 to 69 in 2018. While total fatalities remained stable (80 in 2018 vs. 79 in 2017), the number of people injured in these fatal crashes fell by 22%, from 69 to 54.
8
Hit-and-Run Crashes — 2018
▲ 14.3% vs prior (7)
The number of fatal hit-and-run crashes increased from 7 in 2017 to 8 in 2018. Correspondingly, the rate of fatal hit-and-runs as a percentage of all fatal crashes rose from 9.3% to 11.6%. This indicates an upward trend in fatal crashes where a driver left the scene.
Vulnerable Road User Casualties
14
Pedestrians Killed
65
Motorists Killed
1
Other Killed
0
Pedestrians Injured
54
Motorists Injured
0
Other Injured
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The timing of fatal crashes shifted between the two periods. In 2018, the peak day for fatal crashes was Tuesday with 14 incidents, and the peak hour was 5 p.m. with 7 incidents. This contrasts with 2017, which saw a peak on Saturday (18 fatal crashes) and at the 12 a.m. hour (8 fatal crashes), indicating a shift from a weekend, late-night pattern to a weekday, evening commute pattern.
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)
Road & Environmental Conditions
The conditions under which fatal crashes occurred remained largely consistent year-over-year. In both 2018 and 2017, clear and cloudy weather conditions accounted for the vast majority of incidents, with 55 and 59 fatal crashes respectively. Similarly, the distribution by lighting conditions was stable, with daylight crashes being most frequent (32 in 2018 vs. 31 in 2017), followed by crashes in dark, unlighted conditions (21 in 2018 vs. 19 in 2017).
Weather
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Weather condition at time of crash
Lighting
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Lighting condition field
Vehicles & Demographics
Ford was the most common vehicle make involved in fatal crashes in both years, with 18 vehicles each year. The involvement of Harley-Davidson motorcycles saw a substantial increase, from 2 vehicles in 2017 to 8 in 2018. The demographics of persons involved also shifted; involvement of the 55-64 age group grew from 20 to 30 individuals, while the 26-34 age group's involvement decreased from 37 to 29 individuals.
Top Vehicle Makes (104 vehicles)
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Vehicle unit records
3 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (177 persons with recorded sex)
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Person-level records linked to crash events
Speed Limit Zones
Fatal crashes in 2018 were most common in 45 mph zones (14 crashes), 55 mph zones (13 crashes), and 65 mph zones (13 crashes). This represents a shift from 2017, when the highest speed zones saw more incidents, led by 65 mph zones (18 crashes) and 55 mph zones (16 crashes). The data indicates a slight downward shift in the speed zones where fatal crashes occurred.
Fatal crashes by zone: 25 mph: 5 of 5 (100%) · 30 mph: 1 of 1 (100%) · 35 mph: 6 of 6 (100%) · 40 mph: 6 of 6 (100%) · 45 mph: 14 of 14 (100%) · 50 mph: 7 of 7 (100%) · 55 mph: 13 of 13 (100%) · 60 mph: 1 of 1 (100%) · 65 mph: 13 of 13 (100%)
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Posted speed limit at crash location
Manner of Collision
In both periods, the dominant manner of collision was 'Not a Collision with Motor Vehicle In-Transport,' which includes single-vehicle crashes and collisions with pedestrians. This category accounted for 45 fatal crashes (65.2%) in 2018, a decrease from 51 fatal crashes (68%) in 2017. Angle collisions were the second most common type, holding steady with 13 incidents in both years.
Manner of Collision
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Crash-level records
Vehicle Type
In 2018, Light Pickups were the most common vehicle type in fatal crashes with 22 involved, an increase from 18 in 2017. The most notable change was the involvement of Two Wheel Motorcycles, which nearly tripled from 5 vehicles in 2017 to 14 in 2018. Conversely, the involvement of 4-door sedans decreased significantly from 26 to 15 vehicles.
Vehicle Type
Showing top 9 of 18 reported. 9 additional (14 total) not shown: 5-door/4-door hatchback, Snowmobile, ATV/ATC [All-Terrain Cycle], 3-door/2-door hatchback, Unknown light vehicle type (automobile,utility vehicle, van, or light truck), Convertible(excludes sun-roof,t-bar), Medium/heavy truck based motorhome, Single-unit straight truck or Cab-Chassis (GVWR range 10,001 to 19,500 lbs.), Unknown body type.
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Vehicle unit records
Rural vs Urban
A significant shift occurred in the location of fatal crashes, with urban incidents increasing from 31 in 2017 to 38 in 2018. Correspondingly, fatal crashes on rural roads decreased from 43 to 31. Despite this change, rural roads still accounted for 45% of all fatal crashes in 2018, a disproportionately high share relative to traffic volume.
Rural vs Urban
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Crash-level records
Roadway Functional Class
Interstates and other Principal Arterials were the site of most fatal crashes in both years, accounting for a combined 41 incidents in 2018, down slightly from 43 in 2017. Fatal crashes on Interstates decreased from 26 to 22 year-over-year, while incidents on Principal Arterials increased from 17 to 19. Crashes on collector roads also increased, from 8 to 15.
Roadway Functional Class
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Crash-level records
Roadway Ownership
Roadways owned by the State Highway Agency were the location for the majority of fatal crashes, though the count decreased from 65 in 2017 to 56 in 2018. In contrast, fatal crashes on roadways owned by County Highway Agencies increased from 5 to 8. State-owned roads accounted for 81% of all fatal crashes in 2018.
Roadway Ownership
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Crash-level records
Person Type
The roles of individuals involved in fatal crashes remained consistent. Drivers were the largest group, with 104 involved in 2018 compared to 103 in 2017. The number of passengers involved decreased from 68 to 60, while the number of pedestrians involved was stable at 14 in 2018 versus 15 in the prior year.
Person Type
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Crash-level records
Person Injury Severity
The number of fatalities resulting from crashes was nearly unchanged, with 80 deaths in 2018 compared to 79 in 2017. However, there was a notable decrease in non-fatal injuries, which fell from 69 to 54. This was driven by a 54% reduction in minor injuries, which dropped from 26 in 2017 to 12 in 2018.
Person Injury Severity
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Crash-level records
Occupant Safety Equipment
Reported use of shoulder and lap belts by occupants in fatal crashes declined from 92 individuals in 2017 to 76 in 2018. Among motorcyclists, the number recorded as not wearing a helmet increased from 11 to 16. The number of people recorded with 'None Used / Not Applicable' was stable at 30 in 2018 versus 31 in 2017.
Occupant Safety Equipment
"Other" combines 1 smaller categories (1 records): Unknown if Helmet Worn (1).
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Person-level records linked to crash events
Point of Impact
Frontal impacts ('12 Clock Point') were the most common point of impact on vehicles involved in fatal crashes, increasing from 49 vehicles in 2017 to 54 in 2018. Non-collision events, such as rollovers, were the second most frequent category in 2018, rising to 12 events from 9 in the previous year.
Point of Impact
"Other" combines 10 smaller categories (16 records): Top (2), 3 Clock Point (2), 9 Clock Point (2), Cargo/Vehicle Parts Set-In-Motion (2), Left (2), 10 Clock Point (2), 7 Clock Point (1), 2 Clock Point (1), Right-Front Side (1), Left-Front Side (1).
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · Vehicle unit records
Vehicles Per Crash
Single-vehicle fatal crashes remained the most common type but decreased from 50 incidents in 2017 to 41 in 2018. As a result, their share of all fatal crashes fell from 67% to 59%. The number of two-vehicle fatal crashes held steady at 22 in both years.
Vehicles Per Crash
Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
- Report generated: August 5, 2026
Data Coverage
- Reporting period: 2018-01-01 through 2018-12-31 (365 days)
- Geographic scope: Alaska
- Total crash records analyzed: 69
- Total persons involved: 179
- Total vehicles involved: 104
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: 2018." Published August 5, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: NHTSA FARS (Fatal Crashes), NHTSA FARS Open Data. Available at: https://thatcarhitme.com/crash-data/alaska/fatal/statewide/2018-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: 2018-01-01 – 2018-12-31
Generated: August 5, 2026 · All rights reserved
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