Yearly Traffic Safety Analysis

311 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In 2024, Clayton County recorded 311 total crashes, an 8.7% increase from the 286 crashes documented in 2023. While total crashes and injuries rose, the number of fatalities decreased from 3 to 2. A notable trend was the 19% year-over-year increase in crashes attributed to animals, which remained the leading contributing factor in both periods.

311

▲ 8.7%was 286

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

64

▲ 4.9%was 61

Persons Injured

2

▼ -33.3%was 3

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

Crash volumes in Clayton County trended upward year-over-year, with total incidents increasing by 8.7% from 286 in 2023 to 311 in 2024. This rise in crashes was accompanied by a slight increase in total injuries from 61 to 64. However, the number of fatalities resulting from these crashes decreased from 3 in the prior period to 2 in the current period.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 3↓-33.3%

0

Other Killed

Prior: 0→0.0%

63

Motorists Injured

Prior: 60↑5.0%

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 temporal patterns of crashes shifted between the two periods. In 2024, Friday became the peak day for crashes with 55 incidents, a change from 2023 when Sunday was the peak day with 50 crashes. The peak hour for collisions remained consistent at 5 p.m. in both years, although the crash volume during this hour increased from 25 to 30.

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

The severity of crashes showed a slight de-escalation year-over-year, with a lower proportion of injury-related incidents. The number of fatal crashes dropped from 3 to 2, and the fatal crash rate per 100 crashes decreased from 1.05 to 0.64. Correspondingly, the share of no-injury crashes increased from 79.4% of all incidents in 2023 to 82.0% in 2024.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.6%
↓-33.3%prior 3
Serious Injury6serious injury crashes1.9%
↓-14.3%prior 7
Minor Injury26minor injury crashes8.4%
↓-3.7%prior 27
Possible Injury22possible injury crashes7.1%
→0.0%prior 22
No Injury255no injury crashes82%
↑12.3%prior 227

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 top contributing factors remained consistent, though their counts shifted. Collisions involving an animal remained the leading factor, increasing by 19% from 126 incidents in 2023 to 150 in 2024. In contrast, crashes attributed to a driver losing control decreased from 29 to 26, and incidents where a vehicle ran off the road to the left saw a significant drop from 25 to 14.

Officer-Reported Primary Contributing Cause

Animal150 (48.2%)↑19.0%prior 126
Lost Control26 (8.4%)↓-10.3%prior 29
Ran off road - left14 (4.5%)↓-44.0%prior 25
Other (explain in narrative): Other14 (4.5%)↓-6.7%prior 15
Driving too fast for conditions10 (3.2%)↓-23.1%prior 13
Followed too close9 (2.9%)
Driver Distraction: Other interior distraction8 (2.6%)↑33.3%prior 6
FTYROW: From stop sign7 (2.3%)
Swerving/Evasive Action5 (1.6%)
Operating vehicle in an reckless, erratic, careless, negligent manner5 (1.6%)

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

Road & Environmental Conditions

Environmental conditions during crashes remained largely stable year-over-year. Crashes on dry roads increased from 113 to 124, and incidents in daylight were nearly unchanged, with 115 in 2024 versus 118 in 2023. There was a notable decrease in crashes occurring during snowy weather, which fell from 12 incidents in 2023 to 6 in 2024.

Weather

Clear119 (70.0%)
↑3.5%prior 115
Cloudy24 (14.1%)
↓-4.0%prior 25
Rain8 (4.7%)
↑60.0%prior 5
Fog, smoke, smog7 (4.1%)
Snow6 (3.5%)
↓-50.0%prior 12
Severe Winds2 (1.2%)
Blowing Snow1 (0.6%)
Freezing rain/drizzle1 (0.6%)
Sleet, hail1 (0.6%)
Blowing sand, soil, dirt1 (0.6%)

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

Lighting

Daylight115 (65.7%)
↓-2.5%prior 118
Dark - roadway not lighted39 (22.3%)
↑11.4%prior 35
Dark - roadway lighted9 (5.1%)
↓-10.0%prior 10
Dark - unknown roadway lighting5 (2.9%)
Dusk4 (2.3%)
Dawn3 (1.7%)

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

Road Surface

Dry124 (72.5%)
↑9.7%prior 113
Wet16 (9.4%)
↑23.1%prior 13
Gravel12 (7.0%)
↓-25.0%prior 16
Snow11 (6.4%)
↓-15.4%prior 13
Ice/frost4 (2.3%)
↓-63.6%prior 11
Slush3 (1.8%)
Other (explain in narrative)1 (0.6%)

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

Vehicles & Demographics

An analysis of vehicles involved shows a shift in the top makes, as Ford-made vehicles increased from 61 to 94, surpassing Chevrolet models, which decreased from 99 to 89. The age demographics of persons involved in crashes also changed, with decreases in the number of individuals in the 16-20 age group (from 66 to 52) and the 55-64 age group (from 89 to 61).

Top Vehicle Makes (400 vehicles)

1
FORD94 (23.5%)
↑54.1%prior 61
2
CHEV68 (17%)
→0.0%prior 68
3
CHEVROLET21 (5.3%)
↓-32.3%prior 31
4
JEEP20 (5%)
↑17.6%prior 17
5
DODG16 (4%)
↓-23.8%prior 21
6
RAM12 (3%)
↑140.0%prior 5
7
DODGE12 (3%)
↑33.3%prior 9
8
TOYT11 (2.8%)
→0.0%prior 11
9
BUIC11 (2.8%)
10
GMC11 (2.8%)
↓-38.9%prior 18

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

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

Sex Distribution (172 persons with recorded sex)

Male100 (58.1%)
↓-48.7%prior 195
Female72 (41.9%)
↓-46.7%prior 135

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: 311
  • Total persons involved: 412
  • Total vehicles involved: 400

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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