Yearly Traffic Safety Analysis

56 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Wayne County recorded 56 total crashes, a slight decrease from the 57 crashes reported in 2021. Despite the stable number of incidents, the total number of injuries increased by 32%, rising from 25 in 2021 to 33 in 2022. The number of fatalities decreased from two to one during the same period.

56

▼ -1.8%was 57

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

33

▲ 32.0%was 25

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 crash volume in Wayne County remained stable, with a minor decrease from 57 incidents in 2021 to 56 in 2022. However, the outcomes of these crashes shifted, with total injuries increasing by 32% from 25 to 33. Fatalities fell by 50%, from two in 2021 to one in 2022.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 1→0.0%

33

Motorists Injured

Prior: 24↑37.5%

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 2022, the peak day for crashes was Thursday with 13 incidents, a change from Wednesday (11 crashes) in the prior year. The peak hour for collisions also moved significantly, from the evening at 7 p.m. in 2021 (6 crashes) to the mid-afternoon at 2 p.m. in 2022 (7 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 saw mixed changes year-over-year. The number of fatal crashes decreased from two in 2021 to one in 2022, with the fatal crash rate dropping from 3.5% to 1.8%. Conversely, the proportion of crashes resulting in any injury increased from 38.6% in 2021 to 42.8% in 2022, driven by a rise in minor injury crashes from 7 to 11.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.8%
↓-50.0%prior 2
Serious Injury4serious injury crashes7.1%
→0.0%prior 4
Minor Injury11minor injury crashes19.6%
↑57.1%prior 7
Possible Injury9possible injury crashes16.1%
↓-18.2%prior 11
No Injury31no injury crashes55.4%
↓-6.1%prior 33

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 of such crashes increasing from 13 in 2021 to 16 in 2022. A notable shift occurred in the second-ranked factor: 'Driving too fast for conditions' became the second-most common cause in 2022 with 7 incidents, up from just one the previous year. 'Ran Stop Sign,' which was the second-leading factor with 6 crashes in 2021, was not a top-ranked factor in 2022.

Officer-Reported Primary Contributing Cause

Animal16 (28.6%)↑23.1%prior 13
Driving too fast for conditions7 (12.5%)
Ran off road - straight5 (8.9%)
Swerving/Evasive Action4 (7.1%)
Lost Control4 (7.1%)
FTYROW: From stop sign3 (5.4%)
FTYROW: Making left turn2 (3.6%)
Followed too close2 (3.6%)
Driver Distraction: Talking on a hand-held device2 (3.6%)
Improper Backing1 (1.8%)

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

Road & Environmental Conditions

Crashes in 2022 occurred less frequently under ideal conditions compared to 2021. The proportion of incidents on dry roads fell from 84.2% of all crashes in 2021 to 58.9% in 2022. Concurrently, the count of crashes on adverse surfaces like wet, icy, or snowy roads increased from 8 to 12. Similarly, the share of crashes happening in clear weather dropped from 84.2% to 62.5% year-over-year.

Weather

Clear35 (71.4%)
↓-27.1%prior 48
Cloudy5 (10.2%)
→0.0%prior 5
Rain4 (8.2%)
Other (explain in narrative)3 (6.1%)
Fog, smoke, smog1 (2.0%)
Blowing sand, soil, dirt1 (2.0%)

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

Lighting

Daylight30 (62.5%)
↓-11.8%prior 34
Dark - roadway not lighted12 (25.0%)
↓-20.0%prior 15
Dark - roadway lighted4 (8.3%)
↓-20.0%prior 5
Dawn1 (2.1%)
Dusk1 (2.1%)

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

Road Surface

Dry33 (67.3%)
↓-31.3%prior 48
Wet5 (10.2%)
Ice/frost4 (8.2%)
Gravel3 (6.1%)
Mud, dirt2 (4.1%)
Other (explain in narrative)1 (2.0%)
Snow1 (2.0%)

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

Vehicles & Demographics

An analysis of persons involved shows a shift in demographics. The 55-64 age group's involvement in crashes doubled, increasing from 13 individuals in 2021 to 26 in 2022, making it the most represented group. In contrast, the 16-20 age group's involvement remained steady at 19 persons in both years. Among vehicle makes, Chevrolet (combining 'CHEV' and 'CHEVROLET' entries) remained the most common with 21 vehicles involved in 2022, up from 19 in 2021, while Ford's involvement decreased from 14 to 9 vehicles.

Top Vehicle Makes (75 vehicles)

1
CHEV16 (21.3%)
↑14.3%prior 14
2
FORD9 (12%)
↓-35.7%prior 14
3
NISS6 (8%)
4
GMC5 (6.7%)
5
CHEVROLET5 (6.7%)
→0.0%prior 5
6
DEER3 (4%)
7
DODG3 (4%)
8
KIA3 (4%)
9
TOYT3 (4%)
10
CHRY2 (2.7%)

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

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

Sex Distribution (73 persons with recorded sex)

Male48 (65.8%)
↓-5.9%prior 51
Female25 (34.2%)
↑4.2%prior 24

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: 56
  • Total persons involved: 118
  • Total vehicles involved: 75

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

ThatCarHitMe.com · An Injuria.ai Company