Yearly Traffic Safety Analysis

397 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Iowa County recorded 397 traffic crashes, a 2.6% increase from the 387 crashes documented in 2021. While total fatalities remained unchanged at two, and total injuries decreased by 10.7% from 103 to 92, the most notable shift was a 53.6% increase in the count of crashes attributed to vehicles running off a straight road.

397

▲ 2.6%was 387

Total Crash Events

2

Persons Killed

92

▼ -10.7%was 103

Persons Injured

2

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, traffic crashes in Iowa County saw a slight increase in 2022, rising by 2.6% to 397 incidents from 387 the previous year. Despite the rise in total crashes, the number of people injured decreased by 10.7% from 103 to 92. Fatalities remained stable, with two individuals killed in traffic incidents in both 2022 and 2021.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 2→0.0%

1

Cyclists Injured

Prior: 0→%

91

Motorists Injured

Prior: 103↓-11.7%

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 2021 and 2022. While Friday remained the peak day for crashes in both years (72 in 2021, 77 in 2022), the peak hour for incidents moved from the 3 p.m. hour (31 crashes) in 2021 to the 8 a.m. hour (26 crashes) in 2022. Crashes on Tuesdays saw a notable increase from 53 to 67, while Sunday crashes decreased from 55 to 29.

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 shifted slightly toward less severe outcomes in 2022 compared to 2021. While the number of fatal crashes remained constant at two, the proportion of crashes resulting in no injuries increased from 79.1% to 81.1% of all incidents. Correspondingly, the share of crashes involving minor injuries decreased from 9.0% to 8.1%, and possible injury crashes fell from 9.8% to 8.6%. The count of serious injury crashes increased by one, from 6 in 2021 to 7 in 2022.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.5%
→0.0%prior 2
Serious Injury7serious injury crashes1.8%
↑16.7%prior 6
Minor Injury32minor injury crashes8.1%
↓-8.6%prior 35
Possible Injury34possible injury crashes8.6%
↓-10.5%prior 38
No Injury322no injury crashes81.1%
↑5.2%prior 306

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 with animals remained the leading contributing factor in both periods, with a slight increase from 113 incidents in 2021 to 115 in 2022. The most significant change was in crashes attributed to 'Ran off road - straight,' which increased in count by 53.6% from 28 to 43, moving from the fourth to the second most common factor. Conversely, crashes due to 'Lost Control' decreased from 36 to 28, falling from the second to the fourth-ranked cause. The count for 'Driving too fast for conditions' rose from 28 to 32 incidents.

Officer-Reported Primary Contributing Cause

Animal115 (29%)↑1.8%prior 113
Ran off road - straight43 (10.8%)↑53.6%prior 28
Driving too fast for conditions32 (8.1%)↑14.3%prior 28
Lost Control28 (7.1%)↓-22.2%prior 36
Ran off road - left23 (5.8%)↑27.8%prior 18
Followed too close18 (4.5%)↓-21.7%prior 23
Other (explain in narrative): Other13 (3.3%)↓-23.5%prior 17
Operating vehicle in an reckless, erratic, careless, negligent manner12 (3%)↑33.3%prior 9
Driver Distraction: Other interior distraction11 (2.8%)↑37.5%prior 8
FTYROW: From stop sign10 (2.5%)↓-41.2%prior 17

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 proportion of crashes occurring on dry roads and in clear weather remained relatively stable year-over-year. However, there was a notable shift in crashes related to winter conditions; incidents on roads with ice or frost more than doubled from 17 to 38, and crashes during blowing snow increased from 5 to 24. In terms of lighting, the share of crashes in daylight decreased from 47.8% to 43.3%, while the count of crashes in unlit dark conditions increased from 78 to 89.

Weather

Clear162 (54.4%)
↑2.5%prior 158
Cloudy66 (22.1%)
↓-13.2%prior 76
Blowing Snow24 (8.1%)
↑380.0%prior 5
Rain16 (5.4%)
↑14.3%prior 14
Snow15 (5.0%)
↓-42.3%prior 26
Severe Winds7 (2.3%)
Freezing rain/drizzle6 (2.0%)
Fog, smoke, smog1 (0.3%)
Sleet, hail1 (0.3%)

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

Lighting

Daylight172 (58.1%)
↓-7.0%prior 185
Dark - roadway not lighted89 (30.1%)
↑14.1%prior 78
Dark - roadway lighted20 (6.8%)
↑100.0%prior 10
Dusk9 (3.0%)
↑28.6%prior 7
Dawn5 (1.7%)
↓-37.5%prior 8
Dark - unknown roadway lighting1 (0.3%)

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

Road Surface

Dry191 (64.1%)
↓-1.0%prior 193
Ice/frost38 (12.8%)
↑123.5%prior 17
Snow25 (8.4%)
↓-28.6%prior 35
Wet24 (8.1%)
↓-11.1%prior 27
Gravel17 (5.7%)
↑70.0%prior 10
Slush2 (0.7%)
↓-71.4%prior 7
Mud, dirt1 (0.3%)

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

Vehicles & Demographics

Ford and Chevrolet vehicles remained the most frequently involved in crashes in both 2021 and 2022, though the count for Ford-made vehicles decreased from 114 to 102. Freightliner trucks saw increased involvement, rising from 20 to 26 vehicles. Analysis of persons involved shows a shift in age demographics; the proportion of individuals aged 16-20 dropped from 10.3% of all persons in 2021 to 5.7% in 2022. Conversely, the share of persons in the 35-44 age group increased from 15.3% to 18.5%.

Top Vehicle Makes (554 vehicles)

1
FORD102 (18.4%)
↓-10.5%prior 114
2
CHEV70 (12.6%)
↑4.5%prior 67
3
FREIGHTLINER26 (4.7%)
↑30.0%prior 20
4
TOYO25 (4.5%)
↑56.3%prior 16
5
DODG25 (4.5%)
↑13.6%prior 22
6
CHEVROLET23 (4.2%)
↓-45.2%prior 42
7
HOND20 (3.6%)
↑33.3%prior 15
8
GMC19 (3.4%)
↓-9.5%prior 21
9
NR17 (3.1%)
↑54.5%prior 11
10
JEEP16 (2.9%)
↑60.0%prior 10

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

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

Sex Distribution (511 persons with recorded sex)

Male339 (66.3%)
↑17.7%prior 288
Female172 (33.7%)
↓-1.7%prior 175

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: 397
  • Total persons involved: 783
  • Total vehicles involved: 554

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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