Yearly Traffic Safety Analysis

142 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In O'Brien County, total traffic crashes remained relatively stable, increasing slightly from 139 in 2021 to 142 in 2022, a change of 2.2%. However, the severity of these incidents worsened considerably. The most notable year-over-year shift was the emergence of fatalities, with 5 deaths recorded in 2022 compared to zero in the prior year.

142

▲ 2.2%was 139

Total Crash Events

5

Persons Killed

72

▲ 30.9%was 55

Persons Injured

5

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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 O'Brien County worsened from 2021 to 2022. While the total number of crashes saw a minor increase of 2.2% (from 139 to 142), the number of people injured rose by 30.9% (from 55 to 72), and the number of fatalities increased from 0 to 5.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 0→%

2

Pedestrians Injured

Prior: 0→%

70

Motorists Injured

Prior: 54↑29.6%

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 Monday with 26 incidents, a change from Thursday (27 incidents) in 2021. The peak hour for crashes also saw a significant change, moving from the 4 p.m. hour (16 crashes) in 2021 to the 7 a.m. hour (14 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

Crash severity increased significantly in 2022 compared to 2021. The number of fatal crashes rose from zero to 5, accounting for 3.5% of all crashes in 2022. The count of serious injury crashes also grew from 4 to 7. Consequently, the proportion of crashes resulting in no injuries decreased from 66.9% in 2021 to 64.8% in 2022.

Outcome by Severity (Crash Events)

Fatal5fatal crashes3.5%
Serious Injury7serious injury crashes4.9%
↑75.0%prior 4
Minor Injury20minor injury crashes14.1%
↓-9.1%prior 22
Possible Injury18possible injury crashes12.7%
↓-10.0%prior 20
No Injury92no injury crashes64.8%
↓-1.1%prior 93

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 showed some consistency and some notable shifts year-over-year. Failure to yield from a stop sign was the top factor in both periods, with an identical count of 19 crashes. Following too closely increased from 15 crashes in 2021 to 17 in 2022. The most significant change was in crashes attributed to 'Lost Control,' which saw its count increase by 71.4%, from 7 incidents in 2021 to 12 in 2022.

Officer-Reported Primary Contributing Cause

FTYROW: From stop sign19 (13.4%)→0.0%prior 19
Followed too close17 (12%)↑13.3%prior 15
Lost Control12 (8.5%)↑71.4%prior 7
Driving too fast for conditions12 (8.5%)↓-20.0%prior 15
Ran off road - left7 (4.9%)↓-41.7%prior 12
Animal7 (4.9%)↑16.7%prior 6
Ran Stop Sign7 (4.9%)→0.0%prior 7
FTYROW: From yield sign5 (3.5%)
Improper Backing4 (2.8%)
Driver Distraction: Other interior distraction4 (2.8%)↓-33.3%prior 6

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 were largely comparable between 2021 and 2022, with most incidents in both years occurring in daylight and clear weather. There was a slight shift in road surface conditions; the proportion of crashes on dry roads increased from 67.6% in 2021 to 75.4% in 2022. Conversely, crashes on snow-covered roads decreased from 19 to 15 incidents, and crashes on wet roads dropped from 6 to 3.

Weather

Clear90 (65.2%)
↑7.1%prior 84
Cloudy31 (22.5%)
↓-3.1%prior 32
Snow6 (4.3%)
↓-14.3%prior 7
Fog, smoke, smog3 (2.2%)
Freezing rain/drizzle2 (1.4%)
Blowing sand, soil, dirt2 (1.4%)
Severe Winds2 (1.4%)
Blowing Snow2 (1.4%)

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

Lighting

Daylight101 (73.2%)
↑5.2%prior 96
Dark - roadway not lighted19 (13.8%)
↑26.7%prior 15
Dark - roadway lighted14 (10.1%)
↑7.7%prior 13
Dawn3 (2.2%)
Dusk1 (0.7%)
↓-83.3%prior 6

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

Road Surface

Dry107 (77.5%)
↑13.8%prior 94
Snow15 (10.9%)
↓-21.1%prior 19
Ice/frost9 (6.5%)
↑50.0%prior 6
Mud, dirt3 (2.2%)
Wet3 (2.2%)
↓-50.0%prior 6
Gravel1 (0.7%)

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 remained consistent, with Ford and Chevrolet accounting for the highest number of vehicles in both 2021 and 2022. Ford vehicles involved increased from 42 to 44, while Chevrolet vehicles (combining 'CHEV' and 'CHEVROLET') remained stable at 59 and 57 respectively. Analysis of persons involved shows a notable increase in the 21-25 age group (from 19 to 34 individuals) and the 35-44 age group (from 36 to 56 individuals).

Top Vehicle Makes (238 vehicles)

1
FORD44 (18.5%)
↑4.8%prior 42
2
CHEV44 (18.5%)
↑29.4%prior 34
3
GMC18 (7.6%)
↑50.0%prior 12
4
CHEVROLET13 (5.5%)
↓-48.0%prior 25
5
DODG12 (5%)
↓-7.7%prior 13
6
BUIC9 (3.8%)
↑80.0%prior 5
7
DODGE6 (2.5%)
→0.0%prior 6
8
PETERBILT6 (2.5%)
9
JEEP5 (2.1%)
↓-28.6%prior 7
10
CHRY5 (2.1%)

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

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

Sex Distribution (225 persons with recorded sex)

Male146 (64.9%)
↑25.9%prior 116
Female79 (35.1%)
↓-7.1%prior 85

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: 142
  • Total persons involved: 329
  • Total vehicles involved: 238

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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