Yearly Traffic Safety Analysis

146 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In 2024, Kossuth County recorded 146 total crashes, an 18.7% increase from the 123 crashes reported in 2023. The most significant year-over-year change was the number of traffic fatalities, which rose from zero in the prior period to three in 2024. The total number of injuries also increased by 13.7%, from 51 to 58.

146

▲ 18.7%was 123

Total Crash Events

3

Persons Killed

58

▲ 13.7%was 51

Persons Injured

3

Fatal Crash Events

Note: "Persons Killed" (3) 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 · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety trends in Kossuth County worsened year-over-year, with total crashes increasing by 18.7% from 123 in 2023 to 146 in 2024. This rise was reflected across multiple severity metrics, including a 13.7% increase in total injuries (from 51 to 58) and an increase in fatalities from zero to three.

Vulnerable Road User Casualties

3

Motorists Killed

Prior: 0→%

58

Motorists Injured

Prior: 50↑16.0%

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 temporal patterns of crashes shifted between the two periods. In 2024, the peak day for crashes was Tuesday with 24 incidents, a change from 2023 when Friday saw the most crashes at 27. The peak hour for collisions also shifted two hours earlier, from 3 p.m. in 2023 (16 crashes) to 1 p.m. in 2024 (16 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 increased significantly in 2024, marked by the occurrence of 3 fatal crashes, whereas none were recorded in 2023. These fatal incidents accounted for 2.1% of all crashes in the current period. The number of serious injury crashes increased from 6 to 7, while crashes resulting in possible injury decreased from 15 to 12. The count of non-injury crashes grew from 84 in 2023 to 105 in 2024.

Outcome by Severity (Crash Events)

Fatal3fatal crashes2.1%
Serious Injury7serious injury crashes4.8%
↑16.7%prior 6
Minor Injury19minor injury crashes13%
↑5.6%prior 18
Possible Injury12possible injury crashes8.2%
↓-20.0%prior 15
No Injury105no injury crashes71.9%
↑25.0%prior 84

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

While "Lost Control" remained a leading contributing factor with 14 crashes in both 2023 and 2024, there were notable shifts in other categories. Crashes attributed to "Ran off road - left" doubled in count from 6 to 12. Incidents of "Followed too close" also increased from 5 to 9. Conversely, the count of crashes from "Failure to yield from a stop sign" decreased from 12 to 10, and crashes involving "Ran Stop Sign" fell from 8 to 6.

Officer-Reported Primary Contributing Cause

Lost Control14 (9.6%)→0.0%prior 14
Ran off road - left12 (8.2%)↑100.0%prior 6
FTYROW: From stop sign10 (6.8%)↓-16.7%prior 12
Other (explain in narrative): Other9 (6.2%)↑50.0%prior 6
Followed too close9 (6.2%)↑80.0%prior 5
Driving too fast for conditions7 (4.8%)↓-12.5%prior 8
Ran off road - straight7 (4.8%)
Improper Backing7 (4.8%)↑40.0%prior 5
FTYROW: At uncontrolled intersection6 (4.1%)
Ran Stop Sign6 (4.1%)↓-25.0%prior 8

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

Road & Environmental Conditions

In both periods, most crashes occurred in clear weather, during daylight, and on dry roads. The proportion of crashes under these ideal conditions was higher in 2024 compared to 2023. Crashes in clear weather accounted for 76.0% of all incidents in 2024, up from 64.2% in the prior year. Similarly, the share of crashes on dry surfaces increased from 69.9% to 74.7%, indicating a lower proportion of crashes were associated with adverse conditions in the current period.

Weather

Clear111 (77.1%)
↑40.5%prior 79
Cloudy20 (13.9%)
↓-4.8%prior 21
Fog, smoke, smog5 (3.5%)
→0.0%prior 5
Blowing Snow3 (2.1%)
Snow3 (2.1%)
Rain1 (0.7%)
↓-80.0%prior 5
Freezing rain/drizzle1 (0.7%)

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

Lighting

Daylight97 (67.4%)
↑24.4%prior 78
Dark - roadway not lighted20 (13.9%)
↑5.3%prior 19
Dark - roadway lighted14 (9.7%)
↓-12.5%prior 16
Dusk7 (4.9%)
Dawn6 (4.2%)

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

Road Surface

Dry109 (75.7%)
↑26.7%prior 86
Ice/frost15 (10.4%)
↑15.4%prior 13
Wet8 (5.6%)
↓-20.0%prior 10
Snow6 (4.2%)
↓-14.3%prior 7
Gravel5 (3.5%)
Slush1 (0.7%)

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

Vehicles & Demographics

An analysis of vehicle makes involved in crashes shows Chevrolet (61 vehicles, combining variations) surpassed Ford (47 vehicles) as the most frequent make in 2024, a reversal from 2023 when Ford led with 54 vehicles to Chevrolet's 46. The age demographics of people involved also shifted; the 35-44 age group became the largest cohort with 47 individuals, up from 42. Notably, involvement of persons aged 65 and older decreased from 50 to 35, and for the 16-20 age group, it fell from 41 to 26.

Top Vehicle Makes (245 vehicles)

1
CHEV50 (20.4%)
↑31.6%prior 38
2
FORD47 (19.2%)
↓-13.0%prior 54
3
GMC14 (5.7%)
↑100.0%prior 7
4
BUIC13 (5.3%)
↑160.0%prior 5
5
CHEVROLET11 (4.5%)
↑37.5%prior 8
6
JEEP10 (4.1%)
↑25.0%prior 8
7
TOYO10 (4.1%)
↑42.9%prior 7
8
DODG6 (2.4%)
↓-50.0%prior 12
9
NISS5 (2%)
10
HOND5 (2%)

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

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

Sex Distribution (170 persons with recorded sex)

Male112 (65.9%)
↓-0.9%prior 113
Female58 (34.1%)
↓-24.7%prior 77

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: 146
  • Total persons involved: 259
  • Total vehicles involved: 245

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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