Yearly Traffic Safety Analysis

88 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Mitchell County, total traffic crashes increased by 4.8%, from 84 incidents in 2021 to 88 in 2022. While the number of fatalities and fatal crashes decreased, the number of crashes involving a driver under the influence of alcohol tripled, rising from 3 in the prior year to 9 in the current period.

88

▲ 4.8%was 84

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

35

▲ 12.9%was 31

Persons Injured

1

▼ -50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 Mitchell County showed a slight upward trend, increasing from 84 in 2021 to 88 in 2022. The number of people injured rose by 12.9% from 31 to 35. Conversely, the number of fatalities was halved, decreasing from 2 in the prior year to 1 in the current year.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 2↓-50.0%

35

Motorists Injured

Prior: 31↑12.9%

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 year-over-year. In 2022, Wednesday and Friday were the peak days for crashes with 15 incidents each, a change from 2021 when Friday was the sole peak day with 17 incidents. The peak hour for crashes moved earlier in the day, from the 2 p.m. to 4 p.m. block in 2021 (8 crashes each hour) to 1 p.m. in 2022 (9 crashes).

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

The severity of crashes decreased in the most serious categories between the two periods. Fatal crashes fell from 2 in 2021 to 1 in 2022, and serious injury crashes dropped from 4 to 2. However, crashes resulting in minor injuries doubled, increasing from 7 incidents (8.3% of total) in 2021 to 14 incidents (15.9% of total) in 2022.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.1%
↓-50.0%prior 2
Serious Injury2serious injury crashes2.3%
↓-50.0%prior 4
Minor Injury14minor injury crashes15.9%
↑100.0%prior 7
Possible Injury15possible injury crashes17%
↓-6.3%prior 16
No Injury56no injury crashes63.6%
↑1.8%prior 55

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

The leading contributing factors for crashes shifted between 2021 and 2022. In 2021, 'Failure to yield at an uncontrolled intersection' was the top factor with 13 crashes, but this count fell by 53.8% to 6 crashes in 2022. 'Lost Control' became the primary factor in 2022 with 12 incidents, a slight increase from 11 incidents the previous year. Crashes involving an animal remained consistent at 8 incidents in both periods.

Officer-Reported Primary Contributing Cause

Lost Control12 (13.6%)↑9.1%prior 11
Animal8 (9.1%)→0.0%prior 8
Ran off road - straight8 (9.1%)
Ran off road - left7 (8%)↑16.7%prior 6
Other (explain in narrative): Other6 (6.8%)↓-25.0%prior 8
Driving too fast for conditions6 (6.8%)↑20.0%prior 5
FTYROW: At uncontrolled intersection6 (6.8%)↓-53.8%prior 13
Made improper turn3 (3.4%)
FTYROW: Making left turn2 (2.3%)
Swerving/Evasive Action2 (2.3%)

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

Road & Environmental Conditions

While 'Clear' weather and 'Dry' road surfaces were the most common conditions in both years, there was a notable shift in lighting conditions. Crashes in 'Daylight' increased from 51 to 57. More significantly, crashes on dark, unlighted roadways nearly doubled, increasing from 10 incidents in 2021 to 19 in 2022.

Weather

Clear54 (64.3%)
→0.0%prior 54
Cloudy15 (17.9%)
↓-11.8%prior 17
Snow4 (4.8%)
Rain4 (4.8%)
Blowing Snow4 (4.8%)
Severe Winds2 (2.4%)
Freezing rain/drizzle1 (1.2%)

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

Lighting

Daylight57 (68.7%)
↑11.8%prior 51
Dark - roadway not lighted19 (22.9%)
↑90.0%prior 10
Dark - roadway lighted4 (4.8%)
↓-33.3%prior 6
Dusk2 (2.4%)
Dawn1 (1.2%)
↓-90.9%prior 11

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

Road Surface

Dry57 (67.9%)
↑3.6%prior 55
Snow11 (13.1%)
↓-8.3%prior 12
Wet7 (8.3%)
Ice/frost6 (7.1%)
↓-25.0%prior 8
Gravel2 (2.4%)
Slush1 (1.2%)

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 changed year-over-year. Chevrolet became the most common make in 2022 with a combined 35 vehicles (CHEV/CHEVROLET), up from 25 in 2021. Ford, the top make in 2021 with 29 vehicles, saw its involvement decrease to 21 vehicles in 2022. Demographically, the number of people aged 21-25 involved in crashes more than doubled from 7 to 18, and the 65+ age group saw an increase from 26 to 35 individuals.

Top Vehicle Makes (134 vehicles)

1
CHEV30 (22.4%)
↑130.8%prior 13
2
FORD21 (15.7%)
↓-27.6%prior 29
3
DODG8 (6%)
→0.0%prior 8
4
GMC7 (5.2%)
↑16.7%prior 6
5
TOYT6 (4.5%)
6
RAM6 (4.5%)
7
CHEVROLET5 (3.7%)
↓-58.3%prior 12
8
JEEP5 (3.7%)
↓-16.7%prior 6
9
BUIC4 (3%)
10
TOYOTA4 (3%)

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

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

Sex Distribution (124 persons with recorded sex)

Male81 (65.3%)
↑15.7%prior 70
Female43 (34.7%)
↓-2.3%prior 44

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: 88
  • Total persons involved: 180
  • Total vehicles involved: 134

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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