Yearly Traffic Safety Analysis

140 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Wright County, total traffic crashes decreased by 4.1%, from 146 in 2021 to 140 in 2022. Despite the drop in overall crashes, the number of resulting fatalities tripled, increasing from one in the prior year to three in the current year. The total number of injuries also rose from 38 to 46.

140

▼ -4.1%was 146

Total Crash Events

3

▲ 200.0%was 1

Persons Killed

46

▲ 21.1%was 38

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (3) 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

While the total number of crashes in Wright County showed a slight year-over-year decline of 4.1%, the severity of these incidents increased. Total injuries rose by 21.1% from 38 to 46, and fatalities increased from one to three between 2021 and 2022. This indicates a trend of fewer but more severe crashes.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 1↑200.0%

1

Cyclists Injured

Prior: 0→%

45

Motorists Injured

Prior: 38↑18.4%

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 showed some shifts between the two periods. Thursday remained the peak day for crashes in both 2022 (28 crashes) and 2021 (35 crashes). However, the peak hour became more concentrated in 2022, with 15 crashes occurring at 5 p.m., whereas 2021 saw a broader peak spread across the 3 p.m. to 6 p.m. hours, each with 11 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 worsened from 2021 to 2022. While the number of fatal crashes was unchanged at one in both years, the number of people killed in that crash increased from one to three. The share of crashes resulting in any level of injury (Fatal, Serious, Minor, or Possible) grew from 20.0% in 2021 to 25.7% in 2022. This was driven by an increase in minor injury crashes (from 13 to 16) and possible injury crashes (from 12 to 17).

Severity is per crash event (most severe injury). 1 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.7%
→0.0%prior 1
Serious Injury2serious injury crashes1.4%
↓-33.3%prior 3
Minor Injury16minor injury crashes11.4%
↑23.1%prior 13
Possible Injury17possible injury crashes12.1%
↑41.7%prior 12
No Injury104no injury crashes74.3%
↓-11.1%prior 117

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 with animals remained the top contributing factor in both years, though the count of such incidents decreased by 12.1% from 33 in 2021 to 29 in 2022. The rankings of other top factors shifted, with crashes attributed to "Ran off road - left" doubling in count from 4 to 8. Similarly, crashes involving a driver "operating vehicle in an reckless, erratic, careless, negligent manner" more than doubled, increasing from 3 to 7.

Officer-Reported Primary Contributing Cause

Animal29 (20.7%)↓-12.1%prior 33
Other (explain in narrative): Other14 (10%)↓-6.7%prior 15
Ran off road - left8 (5.7%)
Operating vehicle in an reckless, erratic, careless, negligent manner7 (5%)
Lost Control7 (5%)↓-22.2%prior 9
Driving too fast for conditions6 (4.3%)↓-25.0%prior 8
Ran off road - straight6 (4.3%)↓-14.3%prior 7
FTYROW: At uncontrolled intersection5 (3.6%)
FTYROW: From driveway5 (3.6%)
Driver Distraction: Other interior distraction4 (2.9%)

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

Road & Environmental Conditions

There was a significant shift in road surface conditions between the two periods. Crashes on dry roads decreased from 87 in 2021 to 66 in 2022, while crashes on snow-covered roads more than doubled from 6 to 13. The proportion of crashes in daylight increased from 55.5% to 60.0% year-over-year, while crashes in dark, unlighted conditions decreased from 18 to 11.

Weather

Clear79 (70.5%)
↓-7.1%prior 85
Cloudy11 (9.8%)
↓-26.7%prior 15
Snow6 (5.4%)
Blowing Snow4 (3.6%)
Freezing rain/drizzle4 (3.6%)
Rain4 (3.6%)
Severe Winds2 (1.8%)
Fog, smoke, smog1 (0.9%)
Other (explain in narrative)1 (0.9%)

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

Lighting

Daylight84 (74.3%)
↑3.7%prior 81
Dark - roadway not lighted11 (9.7%)
↓-38.9%prior 18
Dark - roadway lighted8 (7.1%)
→0.0%prior 8
Dawn6 (5.3%)
Dusk3 (2.7%)
Dark - unknown roadway lighting1 (0.9%)

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

Road Surface

Dry66 (58.4%)
↓-24.1%prior 87
Snow13 (11.5%)
↑116.7%prior 6
Ice/frost12 (10.6%)
↑9.1%prior 11
Gravel10 (8.8%)
↑66.7%prior 6
Wet8 (7.1%)
Mud, dirt2 (1.8%)
Slush2 (1.8%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, Ford and Chevrolet, remained consistent across both years. Ford-involved crashes decreased slightly from 46 to 44, while combined Chevrolet/CHEV models also saw a small decrease from 60 to 53. Among persons involved in crashes, there was a substantial 80% increase in the 65+ age group, which grew from 25 individuals in 2021 to 45 in 2022.

Top Vehicle Makes (225 vehicles)

1
FORD44 (19.6%)
↓-4.3%prior 46
2
CHEV39 (17.3%)
↑30.0%prior 30
3
GMC18 (8%)
↑100.0%prior 9
4
CHEVROLET14 (6.2%)
↓-53.3%prior 30
5
JEEP12 (5.3%)
↑100.0%prior 6
6
DODG8 (3.6%)
↑14.3%prior 7
7
CHRY7 (3.1%)
8
TOYT6 (2.7%)
→0.0%prior 6
9
RAM5 (2.2%)
10
TOYO5 (2.2%)

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 (204 persons with recorded sex)

Male125 (61.3%)
↑31.6%prior 95
Female79 (38.7%)
↑9.7%prior 72

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: 140
  • Total persons involved: 320
  • Total vehicles involved: 225

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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