Yearly Traffic Safety Analysis

995 CRASHES IN
IOWA, IA
2022

All metrics benchmarked against2021

In 2022, Cerro Gordo County recorded 995 total crashes, a slight decrease of 1.0% from the 1,005 crashes reported in 2021. Despite the relatively stable overall crash volume, the county saw a significant reduction in crash severity. The number of fatalities fell by 50%, from 8 in 2021 to 4 in 2022.

995

▼ -1.0%was 1,005

Total Crash Events

4

▼ -50.0%was 8

Persons Killed

272

▼ -13.1%was 313

Persons Injured

3

▼ -62.5%was 8

Fatal Crash Events

Note: "Persons Killed" (4) 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 crash trends in Cerro Gordo County showed a slight decline from 2021 to 2022. Total crashes decreased by 1.0%, from 1,005 to 995. This downward trend was more pronounced in crash outcomes, with total injuries falling by 13.1% from 313 to 272, and fatalities decreasing by 50% from 8 to 4.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 2↓-100.0%

4

Motorists Killed

Prior: 5↓-20.0%

4

Pedestrians Injured

Prior: 7↓-42.9%

12

Cyclists Injured

Prior: 11↑9.1%

256

Motorists Injured

Prior: 294↓-12.9%

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 in Cerro Gordo County remained broadly consistent year-over-year. Friday was the peak day for crashes in both 2022 (169 crashes) and 2021 (161 crashes). However, the peak hour for collisions shifted earlier, from the 5 p.m. hour in 2021 (80 crashes) to the 3 p.m. hour in 2022 (83 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 saw a notable improvement in 2022 compared to the prior year. The number of fatal crashes dropped from 8 in 2021 to 3 in 2022, a 62.5% decrease. While the count of serious injury crashes increased from 13 to 20, the count of minor injury crashes fell from 85 to 61. Overall, the percentage of crashes resulting in no injuries increased from a 74.6% share in 2021 to a 76.9% share in 2022.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.3%
↓-62.5%prior 8
Serious Injury20serious injury crashes2%
↑53.8%prior 13
Minor Injury61minor injury crashes6.1%
↓-28.2%prior 85
Possible Injury146possible injury crashes14.7%
↓-2.0%prior 149
No Injury765no injury crashes76.9%
↑2.0%prior 750

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 for crashes in Cerro Gordo County were consistent across both periods, with 'Animal' being the top cited cause in both 2022 (173 crashes) and 2021 (168 crashes). While 'Followed too close' remained a top factor, its count decreased by 15.2% from 99 to 84 incidents. Notably, crashes attributed to 'Ran off road - left' increased by 60% in count, from 40 incidents in 2021 to 64 in 2022, and 'Driving too fast for conditions' rose by 22.4% from 49 to 60 crashes.

Officer-Reported Primary Contributing Cause

Animal173 (17.4%)↑3.0%prior 168
Followed too close84 (8.4%)↓-15.2%prior 99
Other (explain in narrative): Other81 (8.1%)↓-4.7%prior 85
Ran off road - left64 (6.4%)↑60.0%prior 40
Driving too fast for conditions60 (6%)↑22.4%prior 49
FTYROW: From stop sign55 (5.5%)↓-17.9%prior 67
FTYROW: Making left turn40 (4%)↑29.0%prior 31
Lost Control33 (3.3%)→0.0%prior 33
Driver Distraction: Other interior distraction29 (2.9%)↓-6.5%prior 31
Ran Traffic Signal27 (2.7%)↓-10.0%prior 30

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 conditions under which crashes occurred showed some shifts between 2021 and 2022. While clear weather and daylight remained the most common conditions in both periods, there was a notable increase in crashes occurring during adverse winter conditions. The number of crashes in snowy weather more than doubled from 24 to 56, and incidents on snow-covered road surfaces increased by 94.1% from 51 to 99. Conversely, the share of crashes on dry road surfaces decreased from 64.9% in 2021 to 61.1% in 2022.

Weather

Clear552 (64.3%)
↓-6.3%prior 589
Cloudy186 (21.7%)
↑2.2%prior 182
Snow56 (6.5%)
↑133.3%prior 24
Blowing Snow19 (2.2%)
↑111.1%prior 9
Rain19 (2.2%)
↓-20.8%prior 24
Freezing rain/drizzle11 (1.3%)
↓-50.0%prior 22
Severe Winds9 (1.0%)
Fog, smoke, smog4 (0.5%)
↓-20.0%prior 5
Sleet, hail2 (0.2%)

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

Lighting

Daylight623 (72.8%)
↑3.3%prior 603
Dark - roadway lighted107 (12.5%)
↓-12.3%prior 122
Dark - roadway not lighted91 (10.6%)
↓-15.7%prior 108
Dusk20 (2.3%)
↑33.3%prior 15
Dawn11 (1.3%)
↓-21.4%prior 14
Dark - unknown roadway lighting4 (0.5%)

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

Road Surface

Dry608 (70.8%)
↓-6.7%prior 652
Snow99 (11.5%)
↑94.1%prior 51
Wet70 (8.1%)
↓-15.7%prior 83
Ice/frost60 (7.0%)
↓-4.8%prior 63
Gravel12 (1.4%)
↑33.3%prior 9
Slush7 (0.8%)
↑40.0%prior 5
Other (explain in narrative)3 (0.3%)

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

Vehicles & Demographics

An analysis of vehicles and persons involved shows shifts in both make and age demographics. In 2022, Ford and Chev were the top two vehicle makes involved in crashes, each with 300 vehicles, a change from 2021 when Ford led with 324 vehicles. The number of persons in the 26-34 age group involved in crashes increased from 272 to 366, making it the largest group in 2022. Conversely, involvement for the 55-64 age group decreased from 300 persons in 2021 to 259 in 2022.

Top Vehicle Makes (1,679 vehicles)

1
FORD300 (17.9%)
↓-7.4%prior 324
2
CHEV300 (17.9%)
↑42.2%prior 211
3
JEEP93 (5.5%)
↑34.8%prior 69
4
GMC79 (4.7%)
↑29.5%prior 61
5
TOYT71 (4.2%)
↓-1.4%prior 72
6
DODG70 (4.2%)
↑29.6%prior 54
7
CHEVROLET62 (3.7%)
↓-57.2%prior 145
8
NISS59 (3.5%)
↑22.9%prior 48
9
HOND54 (3.2%)
↑10.2%prior 49
10
NR45 (2.7%)
↓-18.2%prior 55

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

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

Sex Distribution (1,506 persons with recorded sex)

Male817 (54.2%)
↑7.6%prior 759
Female689 (45.8%)
↑12.6%prior 612

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: 995
  • Total persons involved: 2,226
  • Total vehicles involved: 1,679

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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