Yearly Traffic Safety Analysis

295 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Clayton County, total traffic crashes decreased by 6.3% from 315 in 2021 to 295 in 2022. While overall collisions declined, the number of people injured increased from 56 to 73. The most significant year-over-year change was a 66.7% reduction in fatalities, which fell from 6 in the prior period to 2 in the current period.

295

▼ -6.3%was 315

Total Crash Events

2

▼ -66.7%was 6

Persons Killed

73

▲ 30.4%was 56

Persons Injured

2

▼ -66.7%was 6

Fatal Crash Events

Note: "Persons Killed" (2) 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

The overall trend in Clayton County shows a decrease in total crashes, with collisions falling from 315 to 295 year-over-year. Despite this 6.3% decline in crash volume, the number of people injured rose by 30.4%, from 56 to 73. Conversely, fatalities saw a substantial drop from 6 to 2.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 5↓-60.0%

73

Motorists Injured

Prior: 55↑32.7%

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

Temporal crash patterns shifted between the two periods. In 2022, the peak day for crashes was Friday with 56 incidents, and the peak hour was 6 p.m. with 25 incidents. This contrasts with the previous year, when Saturday was the peak day (53 crashes) and the peak hour for collisions was later, at 9 p.m. (28 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 shifted year-over-year. Fatal crashes decreased from 6 to 2, with their share of all crashes falling from 1.9% to 0.7%. While the number of serious injury crashes increased slightly from 7 to 8, the total count of all injury-resulting crashes (serious, minor, and possible) grew from 51 to 59. Consequently, the proportion of crashes involving any injury rose from 16.2% in 2021 to 20.0% in 2022.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.7%
↓-66.7%prior 6
Serious Injury8serious injury crashes2.7%
↑14.3%prior 7
Minor Injury27minor injury crashes9.2%
↑3.8%prior 26
Possible Injury24possible injury crashes8.1%
↑33.3%prior 18
No Injury234no injury crashes79.3%
↓-9.3%prior 258

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 remained consistent, though their counts changed. Collisions involving an animal continued to be the top factor but decreased in count from 157 to 146. The second-ranked factor, 'Lost Control,' saw its count increase from 23 to 26 incidents. Crashes attributed to 'Driving too fast for conditions' experienced a notable 40% decrease in count, falling from 15 incidents in 2021 to 9 in 2022.

Officer-Reported Primary Contributing Cause

Animal146 (49.5%)↓-7.0%prior 157
Lost Control26 (8.8%)↑13.0%prior 23
Ran off road - left18 (6.1%)↑12.5%prior 16
Other (explain in narrative): Other12 (4.1%)↓-14.3%prior 14
Ran off road - straight12 (4.1%)↑9.1%prior 11
Driving too fast for conditions9 (3.1%)↓-40.0%prior 15
FTYROW: From stop sign8 (2.7%)↑14.3%prior 7
Driver Distraction: Other interior distraction6 (2%)↓-33.3%prior 9
Followed too close5 (1.7%)
FTYROW: From driveway5 (1.7%)

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

Road & Environmental Conditions

Crash conditions saw a shift between periods. In 2022, more crashes occurred during Daylight (113) compared to 2021 (94), while incidents in 'Dark - roadway not lighted' conditions decreased from 50 to 32. Crashes on wet road surfaces increased from 11 to 19 year-over-year, whereas crashes on snowy surfaces decreased from 20 to 16. Collisions in clear weather conditions remained the most frequent scenario in both years but saw a slight decline from 122 to 108 incidents.

Weather

Clear108 (69.2%)
↓-11.5%prior 122
Cloudy24 (15.4%)
↑14.3%prior 21
Snow10 (6.4%)
→0.0%prior 10
Rain8 (5.1%)
↓-20.0%prior 10
Blowing Snow4 (2.6%)
Fog, smoke, smog2 (1.3%)

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

Lighting

Daylight113 (72.0%)
↑20.2%prior 94
Dark - roadway not lighted32 (20.4%)
↓-36.0%prior 50
Dark - roadway lighted6 (3.8%)
↓-57.1%prior 14
Dusk3 (1.9%)
Dawn2 (1.3%)
↓-75.0%prior 8
Dark - unknown roadway lighting1 (0.6%)

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

Road Surface

Dry100 (63.7%)
↓-3.8%prior 104
Wet19 (12.1%)
↑72.7%prior 11
Gravel16 (10.2%)
↓-20.0%prior 20
Snow16 (10.2%)
↓-20.0%prior 20
Ice/frost4 (2.5%)
↓-60.0%prior 10
Slush2 (1.3%)

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 shifted year-over-year. Chevrolet became the most frequent make with 76 vehicles, an increase from 53 in the prior year, surpassing Ford, which had 70 vehicles involved compared to 72 previously. Demographically, there was a notable increase in the number of people aged 21-25 involved in crashes, rising from 39 to 72, and in the 65+ age group, which grew from 71 to 98 persons involved.

Top Vehicle Makes (367 vehicles)

1
CHEV76 (20.7%)
↑43.4%prior 53
2
FORD70 (19.1%)
↓-2.8%prior 72
3
GMC23 (6.3%)
↑35.3%prior 17
4
DODG18 (4.9%)
↓-18.2%prior 22
5
JEEP16 (4.4%)
↑14.3%prior 14
6
DODGE12 (3.3%)
↓-14.3%prior 14
7
CHEVROLET12 (3.3%)
↓-69.2%prior 39
8
BUIC11 (3%)
↑120.0%prior 5
9
SUBA10 (2.7%)
↑42.9%prior 7
10
HOND10 (2.7%)
↑42.9%prior 7

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

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

Sex Distribution (348 persons with recorded sex)

Male199 (57.2%)
↑15.0%prior 173
Female149 (42.8%)
↑22.1%prior 122

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: 295
  • Total persons involved: 592
  • Total vehicles involved: 367

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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