Yearly Traffic Safety Analysis

151 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Humboldt County, total traffic crashes decreased from 162 in 2021 to 151 in 2022, a 6.8% reduction. Despite the overall decline in collisions, the number of fatalities tragically increased from one person in the prior year to four in the current year. This sharp rise in fatalities, which occurred across three separate fatal crashes compared to just one in the previous year, represents the most significant year-over-year shift in the data.

151

▼ -6.8%was 162

Total Crash Events

4

▲ 300.0%was 1

Persons Killed

50

▼ -19.4%was 62

Persons Injured

3

▲ 200.0%was 1

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crashes in Humboldt County showed a downward trend, decreasing by 6.8% from 162 incidents in 2021 to 151 in 2022. This trend included a 19.4% decrease in total injuries, which fell from 62 to 50. However, this positive trend was countered by a severe increase in crash lethality, with fatalities rising from 1 to 4 year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 1↑300.0%

1

Pedestrians Injured

Prior: 0→%

49

Motorists Injured

Prior: 61↓-19.7%

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes showed some shifts between the two periods. The peak day for crashes moved from Thursday in 2021 (29 crashes) to Monday in 2022, which also saw 29 crashes. The peak hour for collisions remained consistent at 5 p.m. in both years, though the number of crashes during that hour decreased from 16 in 2021 to 13 in 2022.

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes decreased, the severity of outcomes worsened significantly. Fatal crashes tripled from 1 in 2021 to 3 in 2022, causing the fatal crash rate to increase from 0.6% to 2.0% of all incidents. Consequently, fatalities rose from 1 to 4. Conversely, the number of crashes resulting in any level of injury (serious, minor, or possible) decreased from 46 in 2021 to 34 in 2022.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes2%
↑200.0%prior 1
Serious Injury8serious injury crashes5.3%
↓-11.1%prior 9
Minor Injury11minor injury crashes7.3%
↓-31.3%prior 16
Possible Injury15possible injury crashes9.9%
↓-28.6%prior 21
No Injury114no injury crashes75.5%
↓-0.9%prior 115

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the leading contributing factor for crashes in both years, with the count increasing from 35 incidents in 2021 to 40 in 2022. 'Lost Control' remained a top factor but saw a decrease in count from 15 to 11 crashes. 'Ran off road - straight' held steady as a leading cause, with its count changing minimally from 11 to 12 crashes year-over-year. The top three contributing factors were consistent across both periods, though their exact counts fluctuated.

Officer-Reported Primary Contributing Cause

Animal40 (26.5%)↑14.3%prior 35
Ran off road - straight12 (7.9%)↑9.1%prior 11
Lost Control11 (7.3%)↓-26.7%prior 15
FTYROW: From stop sign9 (6%)↑12.5%prior 8
Ran off road - left8 (5.3%)↑60.0%prior 5
Other (explain in narrative): Other8 (5.3%)↓-20.0%prior 10
Improper Backing6 (4%)
Ran Stop Sign4 (2.6%)
Made improper turn4 (2.6%)
Driving too fast for conditions4 (2.6%)↓-33.3%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Year-over-year data indicates a shift in the conditions under which crashes occurred. While crashes under clear weather, daylight, and dry road surface conditions all decreased, incidents in adverse conditions saw relative increases. For instance, crashes on icy or frosty roads increased from 9 to 12, and crashes in snowy weather increased from 2 to 6. Crashes in daylight fell from 90 to 67, while those on unlit dark roadways held steady at 26 and 27, respectively.

Weather

Clear76 (69.1%)
↓-16.5%prior 91
Cloudy18 (16.4%)
↓-30.8%prior 26
Snow6 (5.5%)
Blowing Snow5 (4.5%)
Rain3 (2.7%)
Freezing rain/drizzle2 (1.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash

Lighting

Daylight67 (59.8%)
↓-25.6%prior 90
Dark - roadway not lighted27 (24.1%)
↑3.8%prior 26
Dark - roadway lighted7 (6.3%)
↑40.0%prior 5
Dusk5 (4.5%)
Dawn4 (3.6%)
Dark - unknown roadway lighting2 (1.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field

Road Surface

Dry79 (70.5%)
↓-12.2%prior 90
Ice/frost12 (10.7%)
↑33.3%prior 9
Wet11 (9.8%)
↓-15.4%prior 13
Snow9 (8.0%)
↓-10.0%prior 10
Gravel1 (0.9%)
↓-83.3%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent year-over-year. Ford was the most common make in both periods with an identical count of 39 vehicles. Chevrolet and Dodge vehicles also featured prominently in both years' data with stable numbers. The age demographics of persons involved in crashes also showed little change; the 35-44 age group was the most represented in both 2021 (58 people) and 2022 (58 people), followed by the 16-20 age group.

Top Vehicle Makes (223 vehicles)

1
FORD39 (17.5%)
→0.0%prior 39
2
CHEV31 (13.9%)
↑10.7%prior 28
3
DODG17 (7.6%)
↑21.4%prior 14
4
GMC15 (6.7%)
↓-11.8%prior 17
5
TOYT11 (4.9%)
↑83.3%prior 6
6
CHEVROLET11 (4.9%)
↓-21.4%prior 14
7
CHRY11 (4.9%)
8
BUIC9 (4%)
9
DODGE7 (3.1%)
↑16.7%prior 6
10
JEEP6 (2.7%)
↓-60.0%prior 15

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records

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

Sex Distribution (202 persons with recorded sex)

Male116 (57.4%)
↓-10.8%prior 130
Female86 (42.6%)
↑24.6%prior 69

Source: Iowa Crash Data · ArcGIS Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Iowa Crash Data, accessed programmatically via the ArcGIS 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: ArcGIS 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: 2022-01-01 through 2022-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 151
  • Total persons involved: 331
  • Total vehicles involved: 223

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). "iowa, IA Crash Intelligence Report: 2022." Published September 9, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2022-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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