Yearly Traffic Safety Analysis

1,002 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Cerro Gordo County, total crashes remained stable year-over-year, with 1,002 incidents in 2024 compared to 1,004 in 2023, a decrease of less than 1%. Despite this stability in crash volume, the number of fatalities increased significantly from one in the prior period to seven in the current period. Total injuries, however, saw a decrease of 14.4% from 292 to 250.

1,002

▼ -0.2%was 1,004

Total Crash Events

7

▲ 600.0%was 1

Persons Killed

250

▼ -14.4%was 292

Persons Injured

7

▲ 600.0%was 1

Fatal Crash Events

Note: "Persons Killed" (7) counts individual fatalities across all crash events. "Fatal" in the severity table below (7) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall crash trend in Cerro Gordo County was stable, with total crashes decreasing by a negligible 0.2% from 1,004 in 2023 to 1,002 in 2024. This stability in volume masks a significant shift in outcomes, as fatalities rose from one to seven, while total injuries decreased by 14.4%.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 1↑400.0%

3

Pedestrians Injured

Prior: 4↓-25.0%

7

Cyclists Injured

Prior: 6↑16.7%

240

Motorists Injured

Prior: 281↓-14.6%

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · 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 year-over-year shifts. While Friday remained the peak day for crashes in both periods, the count on Fridays increased from 161 to 185. The peak hour for collisions shifted slightly earlier, from the 4 p.m. hour in 2023 (85 crashes) to the 3 p.m. hour in 2024 (80 crashes). Crash distribution across weekdays also changed, with Tuesday becoming the second-busiest day in the current period with 159 crashes, compared to Thursday in the prior year.

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes were stable, their severity distribution changed significantly year-over-year. The number of fatal crashes increased from one (0.1% of total) in 2023 to seven (0.7% of total) in 2024. Conversely, crashes resulting in injuries decreased across all reported levels: serious injury crashes fell from 23 to 16, and minor injury crashes dropped from 85 to 72. Consequently, the proportion of crashes with no reported injuries rose from 76.3% to 78.0%.

Outcome by Severity (Crash Events)

Fatal7fatal crashes0.7%
↑600.0%prior 1
Serious Injury16serious injury crashes1.6%
↓-30.4%prior 23
Minor Injury72minor injury crashes7.2%
↓-15.3%prior 85
Possible Injury125possible injury crashes12.5%
↓-3.1%prior 129
No Injury782no injury crashes78%
↑2.1%prior 766

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the leading contributing factor, increasing from 175 incidents in 2023 to 208 in 2024, an 18.9% rise in count. 'Followed too close' also saw an increase in count from 71 to 84 crashes. A notable change was the 60.7% increase in crashes attributed to 'Made improper turn,' which rose from 28 to 45 incidents. Conversely, the broad category of 'Other' factors decreased from 83 to 57 crashes, altering its rank among the top causes.

Officer-Reported Primary Contributing Cause

Animal208 (20.8%)↑18.9%prior 175
Followed too close84 (8.4%)↑18.3%prior 71
Ran off road - left62 (6.2%)↑8.8%prior 57
Other (explain in narrative): Other57 (5.7%)↓-31.3%prior 83
FTYROW: From stop sign55 (5.5%)↑12.2%prior 49
Driver Distraction: Other interior distraction45 (4.5%)↓-10.0%prior 50
Made improper turn45 (4.5%)↑60.7%prior 28
Driving too fast for conditions41 (4.1%)↑17.1%prior 35
FTYROW: Making left turn29 (2.9%)↓-3.3%prior 30
Ran Stop Sign29 (2.9%)↓-17.1%prior 35

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in daylight decreased slightly from 61.7% in 2023 to 59.8% in 2024. Regarding road surface, there was an increase in the count of crashes on icy or frosty roads, from 40 to 53 incidents. Conversely, crashes on snowy surfaces declined from 54 to 39. This corresponds with a decrease in crashes reported during snowy weather, which fell from 40 to 17 year-over-year.

Weather

Clear582 (71.1%)
↑0.7%prior 578
Cloudy155 (18.9%)
↓-18.0%prior 189
Rain27 (3.3%)
↓-12.9%prior 31
Snow17 (2.1%)
↓-57.5%prior 40
Freezing rain/drizzle15 (1.8%)
↑66.7%prior 9
Fog, smoke, smog13 (1.6%)
↑160.0%prior 5
Blowing Snow5 (0.6%)
Sleet, hail2 (0.2%)
Severe Winds1 (0.1%)
Other (explain in narrative)1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight599 (72.3%)
↓-3.4%prior 620
Dark - roadway lighted95 (11.5%)
↓-12.0%prior 108
Dark - roadway not lighted84 (10.1%)
↓-13.4%prior 97
Dusk22 (2.7%)
→0.0%prior 22
Dawn20 (2.4%)
↑122.2%prior 9
Dark - unknown roadway lighting9 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry637 (77.7%)
↓-4.5%prior 667
Wet73 (8.9%)
↓-8.8%prior 80
Ice/frost53 (6.5%)
↑32.5%prior 40
Snow39 (4.8%)
↓-27.8%prior 54
Gravel11 (1.3%)
↓-8.3%prior 12
Slush6 (0.7%)
↓-14.3%prior 7
Mud, dirt1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes shifted, with Ford (293 vehicles) overtaking Chevrolet (272 vehicles) for the top spot in 2024; in the prior year, Chevrolet led with 308 vehicles to Ford's 298. The total number of persons involved in crashes decreased from 2,268 to 1,700. In terms of age distribution, the 16-20 age group saw its proportional involvement increase from 11.3% to 12.4% of all persons in crashes, while the 65+ age group's share slightly decreased from 16.1% to 15.4%.

Top Vehicle Makes (1,652 vehicles)

1
FORD293 (17.7%)
↓-1.7%prior 298
2
CHEV272 (16.5%)
↓-11.7%prior 308
3
TOYT98 (5.9%)
↑55.6%prior 63
4
DODG72 (4.4%)
↓-15.3%prior 85
5
JEEP71 (4.3%)
↓-13.4%prior 82
6
CHEVROLET68 (4.1%)
↑33.3%prior 51
7
GMC68 (4.1%)
↑4.6%prior 65
8
HOND64 (3.9%)
↑10.3%prior 58
9
NISS63 (3.8%)
↓-6.0%prior 67
10
BUIC43 (2.6%)
↓-24.6%prior 57

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (1,028 persons with recorded sex)

Male563 (54.8%)
↓-33.8%prior 850
Female465 (45.2%)
↓-31.6%prior 680

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 1,002
  • Total persons involved: 1,700
  • Total vehicles involved: 1,652

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: 2024." Published September 9, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2024-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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