Yearly Traffic Safety Analysis

82 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In 2024, Osceola County recorded 82 total traffic crashes, a 3.5% decrease from the 85 crashes reported in 2023. The most significant year-over-year change was the reduction in crash fatalities, which dropped from two in the prior period to zero in the current period. Total injuries also decreased from 38 to 28.

82

▼ -3.5%was 85

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

28

▼ -26.3%was 38

Persons Injured

0

▼ -100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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

Overall, Osceola County experienced a downward trend in traffic collisions in 2024 compared to the previous year. Total crashes decreased by 3.5%, from 85 to 82. This trend extended to crash outcomes, with total injuries declining by 26.3% (from 38 to 28) and fatalities dropping from two to zero.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 2↓-100.0%

0

Other Killed

Prior: 0→0.0%

27

Motorists Injured

Prior: 38↓-28.9%

1

Other Injured

Prior: 0→%

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 timing of crashes in Osceola County showed notable shifts between the two periods. In 2024, the peak day for crashes was Wednesday with 15 incidents, a change from 2023 when Sunday and Monday were the most frequent days with 15 crashes each. The peak hour also changed significantly, moving from 3 p.m. in 2023 (13 crashes) to 7 a.m. in 2024 (12 crashes).

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

Crash severity in Osceola County improved in 2024 compared to the prior year. There were no fatal crashes recorded in 2024, down from one in 2023. The number of crashes resulting in serious injuries also decreased, falling from 5 in 2023 to 2 in 2024. While the number of minor injury crashes remained stable (10 in 2024 vs. 11 in 2023), crashes with possible injuries increased from 9 to 11.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes2.4%
↓-60.0%prior 5
Minor Injury10minor injury crashes12.2%
↓-9.1%prior 11
Possible Injury11possible injury crashes13.4%
↑22.2%prior 9
No Injury59no injury crashes72%
→0.0%prior 59

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 top contributing factor in Osceola County for both periods, though the count decreased from 25 crashes in 2023 to 22 in 2024. 'Lost Control' was the second-most cited factor in both years, with an unchanged count of 8 crashes. Notably, crashes attributed to 'Failure to Yield Right of Way from a stop sign' increased from 4 incidents in 2023 to 7 in 2024. Similarly, incidents of 'Driving too fast for conditions' and 'Ran off road - left' both saw a slight increase, from 5 to 6 crashes each.

Officer-Reported Primary Contributing Cause

Animal22 (26.8%)↓-12.0%prior 25
Lost Control8 (9.8%)→0.0%prior 8
FTYROW: From stop sign7 (8.5%)
Ran off road - left6 (7.3%)↑20.0%prior 5
Driving too fast for conditions6 (7.3%)↑20.0%prior 5
Ran off road - straight5 (6.1%)
Operating vehicle in an reckless, erratic, careless, negligent manner3 (3.7%)
Followed too close3 (3.7%)
FTYROW: At uncontrolled intersection3 (3.7%)
Other (explain in narrative): Other2 (2.4%)

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

Road & Environmental Conditions

Crashes on dry roads increased from 38 in 2023 to 45 in 2024, while collisions on wet surfaces saw a significant drop from 10 to 2. Crashes occurring during daylight hours decreased from 47 to 44, whereas incidents in dark, unlit conditions rose from 13 to 16. In terms of weather, crashes during clear conditions increased from 41 to 47, while those involving snow or freezing rain decreased from 9 in 2023 to 5 in 2024.

Weather

Clear47 (72.3%)
↑14.6%prior 41
Cloudy7 (10.8%)
↑16.7%prior 6
Fog, smoke, smog4 (6.2%)
Snow3 (4.6%)
Freezing rain/drizzle2 (3.1%)
Other (explain in narrative)1 (1.5%)
Rain1 (1.5%)

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

Lighting

Daylight44 (65.7%)
↓-6.4%prior 47
Dark - roadway not lighted16 (23.9%)
↑23.1%prior 13
Dusk3 (4.5%)
Dark - unknown roadway lighting2 (3.0%)
Dawn2 (3.0%)

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

Road Surface

Dry45 (69.2%)
↑18.4%prior 38
Ice/frost9 (13.8%)
Snow5 (7.7%)
↓-37.5%prior 8
Gravel4 (6.2%)
Wet2 (3.1%)
↓-80.0%prior 10

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Ford being the most common in both 2024 and 2023, involved in 25 crashes each year. Chevrolet (listed as 'Chev' and 'Chevrolet') also ranked highly in both periods. The number of people involved in crashes decreased from 188 to 121, with reductions seen across all age brackets. The most significant drop was in the 45-54 age group, which saw its count fall from 36 individuals in 2023 to 15 in 2024.

Top Vehicle Makes (119 vehicles)

1
FORD25 (21%)
→0.0%prior 25
2
CHEV15 (12.6%)
↑15.4%prior 13
3
DODG6 (5%)
→0.0%prior 6
4
PETERBILT5 (4.2%)
5
CHEVROLET5 (4.2%)
↓-37.5%prior 8
6
HONDA4 (3.4%)
7
DODGE4 (3.4%)
8
GMC4 (3.4%)
↓-33.3%prior 6
9
JEEP3 (2.5%)
↓-40.0%prior 5
10
TOYT3 (2.5%)

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

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

Sex Distribution (53 persons with recorded sex)

Male32 (60.4%)
↓-56.8%prior 74
Female21 (39.6%)
↓-46.2%prior 39

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 10, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 82
  • Total persons involved: 121
  • Total vehicles involved: 119

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 10, 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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