Yearly Traffic Safety Analysis

109 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In Worth County, the total number of traffic crashes remained unchanged year-over-year, with 109 incidents reported in both 2022 and 2021. While total crashes were stable, the number of people injured increased by 42.1%, from 19 to 27. The most notable shift was a 400% increase in crashes involving a driver under the influence of alcohol, which rose from 1 crash in 2021 to 5 crashes in 2022.

109

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

27

▲ 42.1%was 19

Persons Injured

2

▼ -33.3%was 3

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

Overall, the trend in Worth County shows a stable number of total crashes year-over-year, holding at 109 incidents. However, the severity of outcomes worsened, as the total number of injuries rose from 19 in 2021 to 27 in 2022, representing a 42.1% increase. Conversely, the number of fatalities resulting from these crashes decreased from 3 to 2 over the same period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

1

Pedestrians Injured

Prior: 0→%

26

Motorists Injured

Prior: 19↑36.8%

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 Friday with 20 incidents, a change from 2021 when Wednesday was the peak day with 19 crashes. The peak hour for collisions also shifted significantly later, moving from the 3 p.m. hour (11 crashes) in 2021 to the 9 p.m. hour (10 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

The severity of crashes showed a mixed trend year-over-year. The number of fatal crashes decreased from 3 in 2021 to 2 in 2022, and the corresponding fatal crash rate dropped from 2.75 to 1.83 per 100 crashes. While the count of minor injury crashes fell from 9 to 6, crashes resulting in serious injuries increased from 2 to 3, and those with possible injuries rose from 10 to 19. Overall, 80.7% of crashes in 2022 resulted in no injury, a slight decrease from 81.7% in the prior year.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.8%
↓-33.3%prior 3
Serious Injury3serious injury crashes2.8%
↑50.0%prior 2
Minor Injury6minor injury crashes5.5%
↓-33.3%prior 9
Possible Injury10possible injury crashes9.2%
↑66.7%prior 6
No Injury88no injury crashes80.7%
↓-1.1%prior 89

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 primary contributing factors remained consistent, with 'Animal' being the leading cause in both periods, though its count decreased slightly from 40 in 2021 to 38 in 2022. 'Lost Control' also remained a top factor, increasing from 12 to 13 incidents. Notably, crashes attributed to 'Followed too close' saw a significant increase in count, rising from 1 incident in 2021 to 4 in 2022. Conversely, incidents where a driver 'Ran Stop Sign' decreased from 5 to 3.

Officer-Reported Primary Contributing Cause

Animal38 (34.9%)↓-5.0%prior 40
Lost Control13 (11.9%)↑8.3%prior 12
Other (explain in narrative): Other8 (7.3%)↑33.3%prior 6
Driving too fast for conditions6 (5.5%)↑20.0%prior 5
Ran off road - left6 (5.5%)
Ran off road - straight5 (4.6%)↓-37.5%prior 8
FTYROW: From stop sign4 (3.7%)
Made improper turn4 (3.7%)
Followed too close4 (3.7%)
Driver Distraction: Other interior distraction3 (2.8%)

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 noticeable shift in crash conditions year-over-year. Crashes occurring in daylight decreased from 56 to 42, while those in 'Dark - roadway not lighted' conditions increased slightly from 21 to 23. Collisions during adverse weather became more frequent; crashes in 'Freezing rain/drizzle' more than tripled from 3 to 10, and crashes on snowy road surfaces doubled from 5 to 10. Correspondingly, crashes on dry roads decreased from 57 to 51.

Weather

Clear43 (53.8%)
↓-21.8%prior 55
Cloudy13 (16.3%)
↑30.0%prior 10
Freezing rain/drizzle10 (12.5%)
Snow4 (5.0%)
↓-20.0%prior 5
Blowing Snow4 (5.0%)
Severe Winds3 (3.8%)
Fog, smoke, smog2 (2.5%)
Rain1 (1.3%)

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

Lighting

Daylight42 (52.5%)
↓-25.0%prior 56
Dark - roadway not lighted23 (28.7%)
↑9.5%prior 21
Dark - roadway lighted6 (7.5%)
Dawn5 (6.3%)
Dusk3 (3.8%)
Dark - unknown roadway lighting1 (1.3%)

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

Road Surface

Dry51 (63.7%)
↓-10.5%prior 57
Ice/frost13 (16.3%)
↑8.3%prior 12
Snow10 (12.5%)
↑100.0%prior 5
Gravel3 (3.8%)
Wet3 (3.8%)
↓-50.0%prior 6

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 shifted, with Chevrolet (29 vehicles, combining 'CHEV' and 'CHEVROLET') surpassing Ford (16 vehicles) in 2022, reversing the order from 2021 when Ford led with 29 vehicles. A significant demographic change occurred in the age of persons involved in crashes; the 21-25 age group saw its involvement nearly triple from 12 individuals in 2021 to 34 in 2022. The number of people aged 65 and older involved in crashes also increased from 20 to 27.

Top Vehicle Makes (152 vehicles)

1
CHEV17 (11.2%)
↑54.5%prior 11
2
FORD16 (10.5%)
↓-44.8%prior 29
3
CHEVROLET12 (7.9%)
↓-7.7%prior 13
4
TOYO9 (5.9%)
5
DODGE8 (5.3%)
→0.0%prior 8
6
FREIGHTLINER7 (4.6%)
7
HONDA7 (4.6%)
8
JEEP7 (4.6%)
9
GMC5 (3.3%)
10
NISS5 (3.3%)
↓-16.7%prior 6

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

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

Sex Distribution (146 persons with recorded sex)

Male91 (62.3%)
↑16.7%prior 78
Female55 (37.7%)
↑27.9%prior 43

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: 109
  • Total persons involved: 209
  • Total vehicles involved: 152

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