Yearly Traffic Safety Analysis

516 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Wapello County, total traffic crashes decreased from 562 in 2021 to 516 in 2022, representing an 8.2% reduction. This downward trend was also observed in related outcomes, with total injuries falling by 25.1% from 227 to 170. The most notable year-over-year shift was the significant increase in crashes attributed to 'Ran Stop Sign', which rose in count from 22 to 36 incidents.

516

▼ -8.2%was 562

Total Crash Events

3

▼ -40.0%was 5

Persons Killed

170

▼ -25.1%was 227

Persons Injured

3

▼ -40.0%was 5

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 traffic safety trends in Wapello County improved from 2021 to 2022. The total number of crashes declined by 8.2%, from 562 to 516. This was accompanied by a 40% decrease in fatalities, from 5 to 3, and a 25.1% decrease in injuries, from 227 to 170.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 5↓-60.0%

4

Pedestrians Injured

Prior: 4→0.0%

1

Cyclists Injured

Prior: 5↓-80.0%

165

Motorists Injured

Prior: 218↓-24.3%

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. While Friday remained the peak day for crashes in both 2021 (91 crashes) and 2022 (86 crashes), the peak hour for collisions moved earlier, from 5 p.m. in 2021 (45 crashes) to 3 p.m. in 2022 (41 crashes). The distribution of crashes throughout the week also changed, with Monday being the second-busiest day in 2021 (90 crashes) and Saturday becoming the second-busiest in 2022 (81 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 decreased from 2021 to 2022. The number of fatal crashes fell from 5 to 3, and their share of all crashes dropped from 0.9% to 0.6%. The proportion of crashes resulting in any injury also declined, from 31.3% (176 crashes) in 2021 to 26.9% (139 crashes) in 2022. Notably, serious injury crashes were cut by more than half, falling from 21 incidents to 10.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.6%
↓-40.0%prior 5
Serious Injury10serious injury crashes1.9%
↓-52.4%prior 21
Minor Injury46minor injury crashes8.9%
↓-31.3%prior 67
Possible Injury83possible injury crashes16.1%
↓-5.7%prior 88
No Injury374no injury crashes72.5%
↓-1.8%prior 381

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 factor in both years was 'Animal', with counts decreasing from 100 in 2021 to 92 in 2022. 'FTYROW: From stop sign' also remained a top factor, with its count declining from 52 to 44. A significant change was observed in 'Ran Stop Sign' incidents, which increased in count by 63.6% from 22 to 36, moving it from the 10th-ranked factor in 2021 to a tie for 3rd in 2022. Conversely, crashes attributed to 'Driving too fast for conditions' decreased from 24 to 13.

Officer-Reported Primary Contributing Cause

Animal92 (17.8%)↓-8.0%prior 100
FTYROW: From stop sign44 (8.5%)↓-15.4%prior 52
Lost Control36 (7%)↓-14.3%prior 42
Ran Stop Sign36 (7%)↑63.6%prior 22
Followed too close36 (7%)↓-16.3%prior 43
Ran off road - left30 (5.8%)↑7.1%prior 28
Other (explain in narrative): Other30 (5.8%)↓-9.1%prior 33
FTYROW: Making left turn18 (3.5%)↓-21.7%prior 23
Operating vehicle in an reckless, erratic, careless, negligent manner17 (3.3%)↑30.8%prior 13
Ran Traffic Signal16 (3.1%)↑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 environmental conditions under which crashes occurred remained remarkably stable year-over-year. In both 2021 and 2022, approximately 66% of crashes happened in 'Clear' weather and about 57% occurred during 'Daylight'. Similarly, the proportion of crashes on 'Dry' road surfaces was consistent, at 67.6% in 2021 and 69.4% in 2022, indicating no major shift in crash patterns related to adverse conditions.

Weather

Clear340 (76.1%)
↓-8.1%prior 370
Cloudy67 (15.0%)
↑15.5%prior 58
Rain18 (4.0%)
↓-14.3%prior 21
Snow14 (3.1%)
↓-26.3%prior 19
Freezing rain/drizzle6 (1.3%)
↑20.0%prior 5
Fog, smoke, smog1 (0.2%)
Severe Winds1 (0.2%)

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

Lighting

Daylight292 (65.6%)
↓-8.5%prior 319
Dark - roadway lighted76 (17.1%)
↓-9.5%prior 84
Dark - roadway not lighted51 (11.5%)
↓-8.9%prior 56
Dark - unknown roadway lighting11 (2.5%)
Dusk10 (2.2%)
↓-16.7%prior 12
Dawn5 (1.1%)
↓-44.4%prior 9

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

Road Surface

Dry358 (80.1%)
↓-5.8%prior 380
Wet39 (8.7%)
↓-2.5%prior 40
Ice/frost20 (4.5%)
↓-16.7%prior 24
Snow19 (4.3%)
↓-32.1%prior 28
Gravel8 (1.8%)
↓-20.0%prior 10
Slush2 (0.4%)
Mud, dirt1 (0.2%)

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 were consistent, with Ford, Chevrolet, and Toyota models appearing most frequently in both years. The number of Ford vehicles involved was identical at 173. The demographics of persons involved in crashes showed a shift, with a decrease in the 26-34 age group (from 197 to 162 persons) and an increase in the 65+ age group (from 126 to 158 persons).

Top Vehicle Makes (858 vehicles)

1
FORD173 (20.2%)
→0.0%prior 173
2
CHEV120 (14%)
↑8.1%prior 111
3
TOYT60 (7%)
↑39.5%prior 43
4
DODG56 (6.5%)
↑40.0%prior 40
5
CHEVROLET41 (4.8%)
↓-53.4%prior 88
6
GMC34 (4%)
↓-12.8%prior 39
7
CHRY22 (2.6%)
→0.0%prior 22
8
HOND21 (2.4%)
↑10.5%prior 19
9
JEEP21 (2.4%)
↓-16.0%prior 25
10
NISS19 (2.2%)
↑90.0%prior 10

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

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

Sex Distribution (778 persons with recorded sex)

Male451 (58.0%)
↑0.9%prior 447
Female327 (42.0%)
↑1.9%prior 321

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: 516
  • Total persons involved: 1,161
  • Total vehicles involved: 858

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