Yearly Traffic Safety Analysis

481 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Sioux County, there were 481 traffic crashes in 2022, a 1.8% decrease from the 490 crashes recorded in 2021. Despite the slight drop in total incidents, the number of fatalities increased significantly, rising from 3 in 2021 to 8 in 2022. This represents a 166.7% increase in persons killed year-over-year.

481

▼ -1.8%was 490

Total Crash Events

8

▲ 166.7%was 3

Persons Killed

211

▲ 2.9%was 205

Persons Injured

6

▲ 100.0%was 3

Fatal Crash Events

Note: "Persons Killed" (8) counts individual fatalities across all crash events. "Fatal" in the severity table below (6) 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 Sioux County remained relatively stable, decreasing by 9 incidents from 490 in 2021 to 481 in 2022. However, the severity of these crashes worsened, with total injuries increasing from 205 to 211 and fatalities growing from 3 to 8. This indicates a trend towards fewer total crashes but more severe outcomes.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 1↓-100.0%

8

Motorists Killed

Prior: 2↑300.0%

0

Other Killed

Prior: 0→0.0%

2

Pedestrians Injured

Prior: 2→0.0%

3

Cyclists Injured

Prior: 0→%

205

Motorists Injured

Prior: 203↑1.0%

1

Other Injured

Prior: 0→%

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. The peak day for crashes moved from Friday (91 crashes) in 2021 to Monday (91 crashes) in 2022. The peak hour for collisions, however, remained unchanged, with 3 p.m. being the most frequent time for crashes in both years, recording 45 incidents in each period.

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 increased from 2021 to 2022. The number of fatal crashes doubled from 3 to 6, and the corresponding fatal crash rate rose from 0.61% to 1.25% of all crashes. Crashes resulting in serious injuries also increased from 14 to 17. Consequently, the proportion of crashes with no injuries decreased from 68.8% in 2021 to 67.8% in 2022.

Severity is per crash event (most severe injury). 6 fatal crash events resulted in 8 persons killed.

Outcome by Severity (Crash Events)

Fatal6fatal crashes1.2%
↑100.0%prior 3
Serious Injury17serious injury crashes3.5%
↑21.4%prior 14
Minor Injury78minor injury crashes16.2%
↑6.8%prior 73
Possible Injury54possible injury crashes11.2%
↓-14.3%prior 63
No Injury326no injury crashes67.8%
↓-3.3%prior 337

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 animals remained the leading contributing factor in both years, with 97 incidents in 2022 compared to 100 in 2021. The most significant change was in crashes attributed to 'Driving too fast for conditions,' which doubled in count from 28 in 2021 to 56 in 2022, a 100% increase. Conversely, incidents involving 'Followed too close' decreased by 26.9%, from 78 crashes in 2021 to 57 in 2022, though it remained the second-most cited factor in the current period.

Officer-Reported Primary Contributing Cause

Animal97 (20.2%)↓-3.0%prior 100
Followed too close57 (11.9%)↓-26.9%prior 78
Driving too fast for conditions56 (11.6%)↑100.0%prior 28
FTYROW: From stop sign43 (8.9%)↑2.4%prior 42
Lost Control28 (5.8%)↑27.3%prior 22
Ran off road - left23 (4.8%)↓-14.8%prior 27
Ran Stop Sign20 (4.2%)↑33.3%prior 15
Ran off road - straight18 (3.7%)↓-5.3%prior 19
Other (explain in narrative): Other18 (3.7%)↑38.5%prior 13
FTYROW: Making left turn14 (2.9%)↑27.3%prior 11

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

Road & Environmental Conditions

While clear weather and dry road surfaces were the most common conditions during crashes in both years, there was a notable increase in crashes under adverse winter conditions. In 2022, crashes on snowy roads increased from 29 to 43, and those on icy or frosty surfaces rose from 29 to 46 compared to 2021. Similarly, crashes during snowfall nearly doubled from 11 to 21, and incidents in blowing snow increased from 7 to 19.

Weather

Clear241 (60.3%)
↓-9.4%prior 266
Cloudy78 (19.5%)
↓-8.2%prior 85
Snow21 (5.3%)
↑90.9%prior 11
Blowing Snow19 (4.8%)
↑171.4%prior 7
Rain18 (4.5%)
↑28.6%prior 14
Freezing rain/drizzle12 (3.0%)
Fog, smoke, smog5 (1.3%)
Severe Winds4 (1.0%)
Blowing sand, soil, dirt2 (0.5%)

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

Lighting

Daylight279 (69.4%)
↓-2.1%prior 285
Dark - roadway not lighted59 (14.7%)
↑9.3%prior 54
Dark - roadway lighted37 (9.2%)
↑2.8%prior 36
Dawn13 (3.2%)
↑18.2%prior 11
Dusk13 (3.2%)
↑44.4%prior 9
Dark - unknown roadway lighting1 (0.2%)

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

Road Surface

Dry256 (63.8%)
↓-11.1%prior 288
Ice/frost46 (11.5%)
↑58.6%prior 29
Snow43 (10.7%)
↑48.3%prior 29
Wet35 (8.7%)
↑40.0%prior 25
Gravel14 (3.5%)
↓-30.0%prior 20
Slush6 (1.5%)
↑20.0%prior 5
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 top vehicle makes involved in crashes remained consistent, with Ford and Chevrolet vehicles being the most frequent in both 2021 and 2022. An analysis of persons involved shows the 16-20 age group was the largest demographic in both years, with 214 individuals in 2022 and 215 in 2021. The number of individuals aged 0-15 involved in crashes saw a notable increase from 31 in 2021 to 51 in 2022, while the 55-64 age group also saw a rise from 94 to 122 persons involved.

Top Vehicle Makes (768 vehicles)

1
FORD152 (19.8%)
↑0.7%prior 151
2
CHEV137 (17.8%)
↑24.5%prior 110
3
CHEVROLET47 (6.1%)
↓-42.0%prior 81
4
JEEP32 (4.2%)
↑33.3%prior 24
5
GMC29 (3.8%)
↓-17.1%prior 35
6
BUIC28 (3.6%)
↑33.3%prior 21
7
TOYT27 (3.5%)
↑58.8%prior 17
8
DODG23 (3%)
↓-25.8%prior 31
9
PONT19 (2.5%)
↑26.7%prior 15
10
RAM18 (2.3%)
↑200.0%prior 6

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

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

Sex Distribution (730 persons with recorded sex)

Male421 (57.7%)
↑12.9%prior 373
Female309 (42.3%)
↑19.8%prior 258

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: 481
  • Total persons involved: 1,096
  • Total vehicles involved: 768

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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