Yearly Traffic Safety Analysis

1,004 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In Cerro Gordo County, there were 1,004 total vehicle crashes in 2023, a slight increase of 0.9% from the 995 crashes recorded in 2022. While overall crash volume remained stable, the most notable year-over-year shift was a significant decrease in traffic fatalities, which fell from four in the prior period to one in the current period. Conversely, the total number of injuries rose from 272 to 292.

1,004

▲ 0.9%was 995

Total Crash Events

1

▼ -75.0%was 4

Persons Killed

292

▲ 7.4%was 272

Persons Injured

1

▼ -66.7%was 3

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends in Cerro Gordo County were relatively stable year-over-year, with total collisions increasing by just nine incidents from 995 to 1,004. However, outcomes shifted, with total injuries increasing by 7.4% (from 272 to 292) while total fatalities decreased by 75% (from four to one).

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 4↓-75.0%

0

Other Killed

Prior: 0→0.0%

4

Pedestrians Injured

Prior: 4→0.0%

6

Cyclists Injured

Prior: 12↓-50.0%

281

Motorists Injured

Prior: 256↑9.8%

1

Other Injured

Prior: 0→%

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · 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 consistency between the two periods. Friday remained the peak day for crashes in both 2023 (161 crashes) and 2022 (169 crashes). There was a minor shift in the peak hour, moving from the 3 p.m. hour in the prior period (83 crashes) to the 4 p.m. hour in the current period (85 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased year-over-year, with the fatal crash rate dropping from 0.3% to 0.1% of all crashes. The number of fatal crashes declined from three to one. While the share of crashes resulting in possible injuries decreased from 14.7% to 12.8%, the proportion of both serious injury crashes (2.0% to 2.3%) and minor injury crashes (6.1% to 8.5%) saw an increase.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.1%
↓-66.7%prior 3
Serious Injury23serious injury crashes2.3%
↑15.0%prior 20
Minor Injury85minor injury crashes8.5%
↑39.3%prior 61
Possible Injury129possible injury crashes12.8%
↓-11.6%prior 146
No Injury766no injury crashes76.3%
↑0.1%prior 765

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors remained consistent year-over-year, with collisions involving an animal being the top cited cause in both 2023 (175 crashes) and 2022 (173 crashes). The count for crashes attributed to 'Followed too close' decreased from 84 to 71. Notably, crashes involving 'Driving too fast for conditions' fell by 41.7%, from 60 incidents to 35. In contrast, crashes citing 'Driver Distraction: Other interior distraction' as a factor increased from 29 to 50, a 72.4% rise in count.

Officer-Reported Primary Contributing Cause

Animal175 (17.4%)↑1.2%prior 173
Other (explain in narrative): Other83 (8.3%)↑2.5%prior 81
Followed too close71 (7.1%)↓-15.5%prior 84
Ran off road - left57 (5.7%)↓-10.9%prior 64
Driver Distraction: Other interior distraction50 (5%)↑72.4%prior 29
FTYROW: From stop sign49 (4.9%)↓-10.9%prior 55
Lost Control37 (3.7%)↑12.1%prior 33
Driving too fast for conditions35 (3.5%)↓-41.7%prior 60
Ran Stop Sign35 (3.5%)↑40.0%prior 25
FTYROW: Making left turn30 (3%)↓-25.0%prior 40

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions were broadly similar across both periods, with most incidents occurring in daylight and on clear days. The most significant change was observed in road surface conditions. Crashes on dry roads increased from 608 to 667, while collisions on snow or ice-covered surfaces decreased substantially, from a combined 159 incidents in 2022 to 94 in 2023.

Weather

Clear578 (67.4%)
↑4.7%prior 552
Cloudy189 (22.1%)
↑1.6%prior 186
Snow40 (4.7%)
↓-28.6%prior 56
Rain31 (3.6%)
↑63.2%prior 19
Freezing rain/drizzle9 (1.1%)
↓-18.2%prior 11
Fog, smoke, smog5 (0.6%)
Blowing Snow3 (0.4%)
↓-84.2%prior 19
Other (explain in narrative)1 (0.1%)
Severe Winds1 (0.1%)
↓-88.9%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight620 (72.1%)
↓-0.5%prior 623
Dark - roadway lighted108 (12.6%)
↑0.9%prior 107
Dark - roadway not lighted97 (11.3%)
↑6.6%prior 91
Dusk22 (2.6%)
↑10.0%prior 20
Dawn9 (1.0%)
↓-18.2%prior 11
Dark - unknown roadway lighting4 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry667 (77.5%)
↑9.7%prior 608
Wet80 (9.3%)
↑14.3%prior 70
Snow54 (6.3%)
↓-45.5%prior 99
Ice/frost40 (4.6%)
↓-33.3%prior 60
Gravel12 (1.4%)
→0.0%prior 12
Slush7 (0.8%)
→0.0%prior 7
Other (explain in narrative)1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Chevrolet (308 vehicles) and Ford (298 vehicles) being the most common in 2023, similar to the prior year's counts of 300 for each. Analysis of the age of persons involved in crashes shows a notable demographic shift. The number of individuals in the 65+ age group increased from 286 to 366, while the number of people in the 26-34 age group decreased from 366 to 310.

Top Vehicle Makes (1,699 vehicles)

1
CHEV308 (18.1%)
↑2.7%prior 300
2
FORD298 (17.5%)
↓-0.7%prior 300
3
DODG85 (5%)
↑21.4%prior 70
4
JEEP82 (4.8%)
↓-11.8%prior 93
5
NISS67 (3.9%)
↑13.6%prior 59
6
GMC65 (3.8%)
↓-17.7%prior 79
7
TOYT63 (3.7%)
↓-11.3%prior 71
8
HOND58 (3.4%)
↑7.4%prior 54
9
BUIC57 (3.4%)
↑42.5%prior 40
10
CHRY55 (3.2%)
↑44.7%prior 38

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (1,530 persons with recorded sex)

Male850 (55.6%)
↑4.0%prior 817
Female680 (44.4%)
↓-1.3%prior 689

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 1,004
  • Total persons involved: 2,268
  • Total vehicles involved: 1,699

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: 2023." Published September 9, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2023-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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