Yearly Traffic Safety Analysis

129 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Guthrie County, the total number of crashes remained unchanged at 129 in 2024 compared to 2023. Despite the stable crash volume, the number of fatalities doubled, increasing from one in 2023 to two in 2024. Total injuries saw a slight decrease from 38 to 36.

129

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

36

▼ -5.3%was 38

Persons Injured

2

▲ 100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

Year-over-year, the total volume of traffic crashes in Guthrie County was stable, with 129 incidents recorded in both 2024 and 2023. While the overall number of crashes did not change, the number of fatalities increased from one to two, and total injuries decreased slightly from 38 to 36.

Vulnerable Road User Casualties

1

Cyclists Killed

Prior: 0→%

1

Motorists Killed

Prior: 1→0.0%

0

Cyclists Injured

Prior: 1↓-100.0%

36

Motorists Injured

Prior: 36→0.0%

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 showed some shifts between the two periods. The peak day for crashes moved from Wednesday (26 crashes) in 2023 to Friday (26 crashes) in 2024. The peak hour for collisions remained consistent at 6 p.m. for both years, with 14 crashes in 2023 and 13 in 2024.

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 in 2024 compared to the prior year. The number of fatal crashes doubled from one to two, representing 1.6% of all crashes in 2024 versus 0.8% in 2023. The number of crashes involving serious injuries also rose from four to six, while the proportion of crashes with no reported injuries decreased from 77.5% in 2023 to 72.9% in 2024.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.6%
↑100.0%prior 1
Serious Injury6serious injury crashes4.7%
↑50.0%prior 4
Minor Injury11minor injury crashes8.5%
↑10.0%prior 10
Possible Injury16possible injury crashes12.4%
↑14.3%prior 14
No Injury94no injury crashes72.9%
↓-6.0%prior 100

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

The primary contributing factors for crashes in Guthrie County remained consistent year-over-year. Collisions involving an 'Animal' were the leading cause in both 2024 (38 crashes) and 2023 (40 crashes). 'Lost Control' (18 crashes in 2024, down from 19) and 'Ran off road - straight' (9 crashes in 2024, down from 12) also remained top factors, though their counts decreased. Notably, crashes attributed to 'Ran Stop Sign' increased from one in 2023 to four in 2024.

Officer-Reported Primary Contributing Cause

Animal38 (29.5%)↓-5.0%prior 40
Lost Control18 (14%)↓-5.3%prior 19
Ran off road - straight9 (7%)↓-25.0%prior 12
Driving too fast for conditions6 (4.7%)↓-14.3%prior 7
Ran off road - left6 (4.7%)
Other (explain in narrative): Other6 (4.7%)↓-40.0%prior 10
FTYROW: From stop sign4 (3.1%)
Ran Stop Sign4 (3.1%)
Improper Backing3 (2.3%)
Exceeded authorized speed3 (2.3%)

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 distribution of crashes across most conditions remained relatively stable year-over-year. Crashes on dry roads were identical at 66 in both periods. However, there was a notable increase in crashes on gravel roads, which rose from 2 in 2023 to 14 in 2024. Conversely, crashes in daylight conditions increased from 59 to 70, while those occurring on dark, unlit roadways decreased from 26 to 17.

Weather

Clear68 (71.6%)
↑6.3%prior 64
Cloudy15 (15.8%)
↓-6.3%prior 16
Snow3 (3.2%)
↓-57.1%prior 7
Freezing rain/drizzle3 (3.2%)
Blowing Snow2 (2.1%)
Fog, smoke, smog2 (2.1%)
Rain1 (1.1%)
Other (explain in narrative)1 (1.1%)

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

Lighting

Daylight70 (72.2%)
↑18.6%prior 59
Dark - roadway not lighted17 (17.5%)
↓-34.6%prior 26
Dark - roadway lighted4 (4.1%)
Dawn2 (2.1%)
↓-60.0%prior 5
Dusk2 (2.1%)
Dark - unknown roadway lighting2 (2.1%)

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

Road Surface

Dry66 (68.8%)
→0.0%prior 66
Gravel14 (14.6%)
Snow7 (7.3%)
↓-36.4%prior 11
Wet6 (6.3%)
→0.0%prior 6
Ice/frost3 (3.1%)
↓-62.5%prior 8

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 showed a shift in 2024. While Ford and Chevrolet were tied with 41 vehicles each in 2023, Chevrolet-branded vehicles (42) were more frequently involved than Ford (28) in 2024. The total number of people involved in crashes decreased from 253 to 174, with notable reductions in the number of individuals from the 26-34 and 35-44 age groups.

Top Vehicle Makes (168 vehicles)

1
CHEV31 (18.5%)
↓-24.4%prior 41
2
FORD28 (16.7%)
↓-31.7%prior 41
3
CHEVROLET11 (6.5%)
↑37.5%prior 8
4
GMC8 (4.8%)
↑14.3%prior 7
5
DODG8 (4.8%)
↑33.3%prior 6
6
JEEP7 (4.2%)
↓-50.0%prior 14
7
CHRY6 (3.6%)
8
KIA5 (3%)
9
HONDA5 (3%)
10
RAM5 (3%)

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

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

Sex Distribution (80 persons with recorded sex)

Male58 (72.5%)
↓-42.6%prior 101
Female22 (27.5%)
↓-64.5%prior 62

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: 129
  • Total persons involved: 174
  • Total vehicles involved: 168

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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