Yearly Traffic Safety Analysis

111 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, IDA County recorded 111 total crashes, a 4.3% decrease from the 116 crashes reported in 2021. Despite the overall reduction in crashes, the most significant change was the emergence of traffic fatalities, with 3 deaths in 2022 compared to zero in the prior year.

111

▼ -4.3%was 116

Total Crash Events

3

Persons Killed

29

▼ -6.5%was 31

Persons Injured

3

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 crashes in IDA County saw a slight decrease in 2022, falling by 4.3% from 116 incidents in 2021 to 111. While total injuries also declined modestly from 31 to 29, the year was marked by the introduction of 3 fatal crashes, a category that had zero incidents in the previous year.

Vulnerable Road User Casualties

3

Motorists Killed

Prior: 0→%

29

Motorists Injured

Prior: 31↓-6.5%

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 shifted between the two periods. In 2022, the peak day for crashes was Thursday with 23 incidents, a change from Friday (28 incidents) in 2021. Similarly, the peak hour for crashes moved from the 8 a.m. morning hour in 2021 to 1 p.m. in 2022, which saw 9 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

Crash severity worsened significantly in 2022, with the introduction of 3 fatal crashes, which accounted for 2.7% of all incidents, compared to zero fatal crashes in 2021. The count of serious injury crashes also increased from 3 to 4. Conversely, crashes resulting in minor injuries decreased from 11 in 2021 to 6 in 2022, while the count of possible injury crashes rose from 14 to 17.

Outcome by Severity (Crash Events)

Fatal3fatal crashes2.7%
Serious Injury4serious injury crashes3.6%
↑33.3%prior 3
Minor Injury6minor injury crashes5.4%
↓-45.5%prior 11
Possible Injury17possible injury crashes15.3%
↑21.4%prior 14
No Injury81no injury crashes73%
↓-8.0%prior 88

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 top contributing factor in both years, though their count decreased by 27.3% from 44 crashes in 2021 to 32 in 2022. A significant shift occurred in crashes related to failure to yield from a stop sign, which tripled in count from 3 incidents in 2021 to 9 in 2022, becoming the second-most common factor. Meanwhile, crashes attributed to 'Lost Control' decreased from 11 to 8.

Officer-Reported Primary Contributing Cause

Animal32 (28.8%)↓-27.3%prior 44
FTYROW: From stop sign9 (8.1%)
Lost Control8 (7.2%)↓-27.3%prior 11
Driving too fast for conditions8 (7.2%)↑14.3%prior 7
Other (explain in narrative): Other7 (6.3%)→0.0%prior 7
Driver Distraction: Other interior distraction4 (3.6%)
Followed too close4 (3.6%)↓-20.0%prior 5
Ran off road - left4 (3.6%)↓-33.3%prior 6
Ran off road - straight4 (3.6%)
Ran Stop Sign3 (2.7%)

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 majority of crashes in both periods occurred in clear weather and on dry roads. In 2022, the proportion of crashes on dry surfaces increased, accounting for 55.9% of incidents (62 crashes) compared to 44.8% (52 crashes) in 2021. Correspondingly, crashes on adverse surfaces like snow, ice, or wet roads decreased from 24 incidents in 2021 to 17 in 2022. Lighting conditions remained relatively stable, with daylight crashes making up the largest share in both years.

Weather

Clear64 (81.0%)
↑14.3%prior 56
Cloudy9 (11.4%)
→0.0%prior 9
Snow2 (2.5%)
Blowing Snow2 (2.5%)
Fog, smoke, smog1 (1.3%)
Rain1 (1.3%)

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

Lighting

Daylight52 (65.8%)
↑2.0%prior 51
Dark - roadway not lighted17 (21.5%)
↓-10.5%prior 19
Dark - roadway lighted7 (8.9%)
Dusk2 (2.5%)
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

Dry62 (78.5%)
↑19.2%prior 52
Snow6 (7.6%)
↓-33.3%prior 9
Wet5 (6.3%)
Gravel3 (3.8%)
Ice/frost3 (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

An analysis of vehicles involved shows a shift in the top makes; Chevrolet took the top spot with 35 vehicles in 2022, up from 18 in 2021, while Ford dropped to second place with 32 vehicles, down from 35. Regarding the age of persons involved in crashes, there was a notable decrease in the 16-20 age group (from 40 to 27 individuals). Conversely, involvement increased for several older demographics, most prominently the 45-54 age group, which grew from 26 individuals in 2021 to 47 in 2022.

Top Vehicle Makes (164 vehicles)

1
CHEV35 (21.3%)
↑94.4%prior 18
2
FORD32 (19.5%)
↓-8.6%prior 35
3
GMC12 (7.3%)
↓-14.3%prior 14
4
JEEP11 (6.7%)
5
DODG8 (4.9%)
↑14.3%prior 7
6
CHEVROLET5 (3%)
↓-58.3%prior 12
7
CHRY5 (3%)
8
BUIC4 (2.4%)
9
KIA4 (2.4%)
↓-20.0%prior 5
10
NISS4 (2.4%)

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

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

Sex Distribution (155 persons with recorded sex)

Male94 (60.6%)
↑40.3%prior 67
Female61 (39.4%)
↑29.8%prior 47

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: 111
  • Total persons involved: 237
  • Total vehicles involved: 164

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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