Yearly Traffic Safety Analysis

235 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Mills County recorded 235 total crashes, an increase of 12.4% from the 209 crashes reported in 2021. The most significant change was the emergence of 5 fatalities in 2022, compared to zero in the prior year. Crashes involving a driver under the influence also more than doubled, increasing from 8 in 2021 to 19 in 2022.

235

▲ 12.4%was 209

Total Crash Events

5

Persons Killed

102

▲ 30.8%was 78

Persons Injured

5

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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

Crash trends in Mills County showed a notable increase from 2021 to 2022. Total collisions rose by 12.4%, from 209 to 235. This was accompanied by a 30.8% increase in total injuries, from 78 to 102, and a rise in fatalities from zero to five.

Vulnerable Road User Casualties

5

Motorists Killed

Prior: 0→%

102

Motorists Injured

Prior: 77↑32.5%

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 shifted between the two periods. In 2021, the peak day for crashes was Saturday with 41 incidents, while in 2022, Tuesday and Sunday shared the highest volume with 35 crashes each. The peak hour also changed, with 2021's busiest time being the 1 p.m. to 2 p.m. window. In 2022, a dual peak emerged at 2 p.m. and 7 p.m., with the 7 p.m. hour seeing a substantial increase from 3 crashes in 2021 to 18 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

Crash severity worsened in 2022 compared to the prior year. The county recorded 5 fatal crashes, resulting in 5 deaths, up from zero fatal crashes in 2021. The count of serious injury crashes increased from 10 to 14, and minor injury crashes rose from 32 to 38. Consequently, the proportion of crashes resulting in no injury decreased from 71.8% of all crashes in 2021 to 65.5% in 2022.

Outcome by Severity (Crash Events)

Fatal5fatal crashes2.1%
Serious Injury14serious injury crashes6%
↑40.0%prior 10
Minor Injury38minor injury crashes16.2%
↑18.8%prior 32
Possible Injury24possible injury crashes10.2%
↑41.2%prior 17
No Injury154no injury crashes65.5%
↑2.7%prior 150

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 involving an animal remained the top contributing factor in both years, with a nearly identical count of 44 in 2022 versus 45 in 2021. The second most common factor, "Lost Control," saw a substantial 42.3% increase in count, rising from 26 incidents in 2021 to 37 in 2022. In contrast, crashes attributed to "Ran off road - straight" decreased from 19 to 14, while incidents of "Ran off road - left" increased from 5 to 16.

Officer-Reported Primary Contributing Cause

Animal44 (18.7%)↓-2.2%prior 45
Lost Control37 (15.7%)↑42.3%prior 26
Ran off road - left16 (6.8%)↑220.0%prior 5
Driving too fast for conditions15 (6.4%)↓-11.8%prior 17
Ran off road - straight14 (6%)↓-26.3%prior 19
Other (explain in narrative): Other9 (3.8%)↓-18.2%prior 11
Followed too close8 (3.4%)→0.0%prior 8
Improper Backing8 (3.4%)
FTYROW: From stop sign8 (3.4%)↓-33.3%prior 12
FTYROW: Making left turn6 (2.6%)

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

Road & Environmental Conditions

The distribution of crashes across environmental conditions remained broadly similar year-over-year. Crashes on dry roads and in clear weather constituted the majority in both periods, with their proportions holding steady. The share of crashes occurring in daylight was also consistent at approximately 56% for both years. There was a notable increase in crashes on dark, lighted roadways, which rose from 11 incidents in 2021 to 17 in 2022.

Weather

Clear162 (76.8%)
↑30.6%prior 124
Cloudy20 (9.5%)
↓-23.1%prior 26
Snow12 (5.7%)
↑50.0%prior 8
Rain8 (3.8%)
↓-11.1%prior 9
Blowing Snow4 (1.9%)
Freezing rain/drizzle3 (1.4%)
↓-40.0%prior 5
Severe Winds1 (0.5%)
Fog, smoke, smog1 (0.5%)

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

Lighting

Daylight133 (63.0%)
↑13.7%prior 117
Dark - roadway not lighted41 (19.4%)
→0.0%prior 41
Dark - roadway lighted17 (8.1%)
↑54.5%prior 11
Dusk9 (4.3%)
↑12.5%prior 8
Dawn8 (3.8%)
↑33.3%prior 6
Dark - unknown roadway lighting3 (1.4%)

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

Road Surface

Dry164 (77.0%)
↑16.3%prior 141
Wet19 (8.9%)
↑58.3%prior 12
Snow18 (8.5%)
↑50.0%prior 12
Gravel7 (3.3%)
↑40.0%prior 5
Ice/frost4 (1.9%)
↓-69.2%prior 13
Slush1 (0.5%)

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

Vehicles & Demographics

Ford and Chevrolet were the top two vehicle makes involved in crashes in both 2021 and 2022, with both makes seeing an increase in collision counts year-over-year. Demographically, the number of people aged 21-25 involved in crashes more than doubled, increasing from 24 in 2021 to 59 in 2022. The 16-20 and 35-44 age groups also saw a rise in involvement, jointly becoming the most represented age groups in 2022 with 72 individuals each.

Top Vehicle Makes (331 vehicles)

1
FORD57 (17.2%)
↑35.7%prior 42
2
CHEV34 (10.3%)
↑54.5%prior 22
3
CHEVROLET24 (7.3%)
↓-7.7%prior 26
4
JEEP14 (4.2%)
↑27.3%prior 11
5
RAM12 (3.6%)
↑100.0%prior 6
6
PETERBILT12 (3.6%)
7
DODG10 (3%)
→0.0%prior 10
8
GMC9 (2.7%)
↑80.0%prior 5
9
NISS9 (2.7%)
10
HOND8 (2.4%)

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

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

Sex Distribution (301 persons with recorded sex)

Male180 (59.8%)
↑17.6%prior 153
Female121 (40.2%)
↑49.4%prior 81

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: 235
  • Total persons involved: 479
  • Total vehicles involved: 331

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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