Yearly Traffic Safety Analysis

351 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Henry County recorded 351 total crashes, a 9.0% increase from the 322 crashes reported in 2021. While total crashes rose, fatalities decreased from 5 to 3, and total injuries fell from 98 to 86. The most significant year-over-year shift in contributing factors was a 26.4% increase in the count of crashes involving an animal, which rose from 106 incidents in 2021 to 134 in 2022.

351

▲ 9.0%was 322

Total Crash Events

3

▼ -40.0%was 5

Persons Killed

86

▼ -12.2%was 98

Persons Injured

2

▼ -33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 crash volume in Henry County increased by 9.0% from 2021 to 2022, rising from 322 to 351 incidents. Despite this increase in total collisions, the number of crashes resulting in an injury or fatality decreased from 81 in 2021 to 73 in 2022. Fatalities also saw a decline, dropping from 5 in the prior period to 3 in the current period.

Vulnerable Road User Casualties

3

Motorists Killed

Prior: 5↓-40.0%

86

Motorists Injured

Prior: 96↓-10.4%

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 temporal patterns of crashes shifted between the two periods. In 2022, the peak day for crashes was Wednesday with 62 incidents, a change from Thursday (57 incidents) in 2021. The peak hour for crashes also moved from the afternoon to the evening, shifting from 3 p.m. (28 crashes) in 2021 to 8 p.m. (29 crashes) 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

The overall severity of crashes lessened from 2021 to 2022. The fatal crash rate, as a percentage of total crashes, decreased from 0.9% in 2021 to 0.6% in 2022. The proportion of crashes resulting in any level of injury (from possible to fatal) also declined, accounting for 20.8% of crashes in 2022 compared to 25.1% in the prior year.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.6%
↓-33.3%prior 3
Serious Injury12serious injury crashes3.4%
↑9.1%prior 11
Minor Injury25minor injury crashes7.1%
↓-19.4%prior 31
Possible Injury34possible injury crashes9.7%
↓-5.6%prior 36
No Injury278no injury crashes79.2%
↑15.4%prior 241

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 periods, with the count increasing by 26.4% from 106 crashes in 2021 to 134 in 2022. 'Lost Control' held its rank as the second-leading factor, with the count of incidents increasing from 23 to 31. 'Driving too fast for conditions' saw its incident count fall slightly from 20 to 19, dropping from the third to the fourth most-cited factor.

Officer-Reported Primary Contributing Cause

Animal134 (38.2%)↑26.4%prior 106
Lost Control31 (8.8%)↑34.8%prior 23
Driving too fast for conditions19 (5.4%)↓-5.0%prior 20
Followed too close15 (4.3%)↑36.4%prior 11
FTYROW: From stop sign14 (4%)↓-26.3%prior 19
Ran off road - straight12 (3.4%)↓-20.0%prior 15
Operating vehicle in an reckless, erratic, careless, negligent manner12 (3.4%)↑20.0%prior 10
Driver Distraction: Other interior distraction10 (2.8%)
Ran Stop Sign10 (2.8%)↑42.9%prior 7
Other (explain in narrative): Other10 (2.8%)↓-23.1%prior 13

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 proportion of crashes occurring under different conditions shifted slightly between the two periods. Crashes on dry roads decreased from 168 in 2021 to 160 in 2022, while crashes on non-dry surfaces (including wet, snow, and gravel) increased from 154 to 191. The share of crashes happening in daylight decreased from 51.6% in 2021 to 45.6% in 2022, though the absolute number of daylight crashes was similar (166 vs. 160).

Weather

Clear162 (69.2%)
↓-0.6%prior 163
Cloudy41 (17.5%)
↓-12.8%prior 47
Rain15 (6.4%)
↑15.4%prior 13
Snow5 (2.1%)
Blowing Snow5 (2.1%)
Fog, smoke, smog2 (0.9%)
Freezing rain/drizzle2 (0.9%)
Other (explain in narrative)1 (0.4%)
Severe Winds1 (0.4%)

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

Lighting

Daylight160 (68.4%)
↓-3.6%prior 166
Dark - roadway not lighted40 (17.1%)
↓-11.1%prior 45
Dark - roadway lighted20 (8.5%)
↑42.9%prior 14
Dusk7 (3.0%)
Dawn6 (2.6%)
→0.0%prior 6
Dark - unknown roadway lighting1 (0.4%)

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

Road Surface

Dry160 (68.4%)
↓-4.8%prior 168
Wet28 (12.0%)
↑12.0%prior 25
Gravel25 (10.7%)
↑38.9%prior 18
Snow14 (6.0%)
→0.0%prior 14
Ice/frost4 (1.7%)
↓-66.7%prior 12
Slush3 (1.3%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Chevrolet and Ford vehicles being the most common in both 2021 and 2022. A significant demographic shift occurred in the age of persons involved in crashes. The number of individuals aged 21-25 involved in collisions increased by 50.9% from 53 to 80, and the 26-34 age group saw a 67.1% increase from 73 to 122 persons.

Top Vehicle Makes (483 vehicles)

1
CHEV70 (14.5%)
↓-11.4%prior 79
2
FORD64 (13.3%)
↓-9.9%prior 71
3
DODG31 (6.4%)
↑63.2%prior 19
4
TOYT31 (6.4%)
↑138.5%prior 13
5
GMC22 (4.6%)
↓-8.3%prior 24
6
JEEP20 (4.1%)
↑33.3%prior 15
7
BUIC20 (4.1%)
↑122.2%prior 9
8
CHEVROLET19 (3.9%)
↓-48.6%prior 37
9
KIA18 (3.7%)
↑80.0%prior 10
10
DODGE13 (2.7%)
↑85.7%prior 7

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

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

Sex Distribution (459 persons with recorded sex)

Male258 (56.2%)
↑14.2%prior 226
Female201 (43.8%)
↑45.7%prior 138

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: 351
  • Total persons involved: 715
  • Total vehicles involved: 483

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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