Yearly Traffic Safety Analysis

252 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Floyd County, traffic crashes remained nearly stable, with 252 incidents in 2024 compared to 253 in the prior year, a decrease of less than 1%. While the overall crash volume was consistent, the number of fatalities saw a significant decrease from 5 in 2023 to 1 in 2024. Total injuries increased slightly from 62 to 64.

252

-0.4%was 253

Total Crash Events

1

-80.0%was 5

Persons Killed

64

3.2%was 62

Persons Injured

1

-75.0%was 4

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 trend in traffic crashes in Floyd County was stable year-over-year, with a total of 252 crashes in 2024, just one fewer than the 253 recorded in 2023. Despite the stable crash volume, there was a notable improvement in fatal outcomes, with fatalities dropping from 5 to 1. Conversely, the total number of people injured saw a minor increase from 62 to 64.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 00.0%

1

Motorists Killed

Prior: 5-80.0%

1

Cyclists Injured

Prior: 0%

63

Motorists Injured

Prior: 5612.5%

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 a shift in the most common day for incidents, moving from Wednesday (45 crashes) in 2023 to Friday (49 crashes) in 2024. The peak hour for crashes remained the 5 p.m. hour in both periods, though the number of crashes during this hour increased from 23 to 30. Crashes during the morning commute hours of 6 a.m. to 8 a.m. decreased from 33 in 2023 to 26 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

The severity of crashes shifted year-over-year, with a significant decrease in fatal incidents. In 2024, there was 1 fatal crash, down from 4 in the prior year, reducing the fatal crash rate from 1.6% to 0.4% of all crashes. However, the number of crashes resulting in serious injuries doubled from 4 to 8. The proportion of crashes with no injuries increased slightly to 81% from 79.4% in the previous year.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
-75.0%prior 4
Serious Injury8serious injury crashes3.2%
100.0%prior 4
Minor Injury21minor injury crashes8.3%
40.0%prior 15
Possible Injury18possible injury crashes7.1%
-37.9%prior 29
No Injury204no injury crashes81%
1.5%prior 201

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 an animal remained the top contributing factor in both periods, accounting for 105 crashes in 2024, a slight decrease from 109 in 2023. The number of crashes attributed to a vehicle running off the road to the left increased significantly, from 6 in 2023 to 16 in 2024, becoming the second most frequent factor. Crashes involving failure to yield right of way from a stop sign also saw an increase in count from 10 to 14. Conversely, crashes due to driving too fast for conditions decreased from 14 to 11.

Officer-Reported Primary Contributing Cause

Animal105 (41.7%)-3.7%prior 109
Ran off road - left16 (6.3%)166.7%prior 6
FTYROW: From stop sign14 (5.6%)40.0%prior 10
Lost Control11 (4.4%)10.0%prior 10
Driving too fast for conditions11 (4.4%)-21.4%prior 14
Ran off road - straight9 (3.6%)50.0%prior 6
Driver Distraction: Other interior distraction9 (3.6%)80.0%prior 5
FTYROW: At uncontrolled intersection8 (3.2%)-20.0%prior 10
Made improper turn8 (3.2%)
Other (explain in narrative): Other8 (3.2%)33.3%prior 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

In both 2024 and 2023, the majority of crashes occurred during daylight hours (104 and 106, respectively) and on dry roads (100 and 112, respectively). There was a notable increase in crashes occurring in dark, unlighted conditions, which rose from 15 incidents in 2023 to 22 in 2024. While crashes in clear weather remained the most common, incidents during rainy conditions increased from 6 to 11, and crashes on snow-covered roads decreased from 13 to 11.

Weather

Clear92 (60.9%)
-8.0%prior 100
Cloudy33 (21.9%)
6.5%prior 31
Rain11 (7.3%)
83.3%prior 6
Snow5 (3.3%)
-54.5%prior 11
Freezing rain/drizzle4 (2.6%)
Blowing Snow4 (2.6%)
Sleet, hail1 (0.7%)
Fog, smoke, smog1 (0.7%)

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

Lighting

Daylight104 (67.5%)
-1.9%prior 106
Dark - roadway not lighted22 (14.3%)
46.7%prior 15
Dark - roadway lighted14 (9.1%)
-6.7%prior 15
Dawn6 (3.9%)
-50.0%prior 12
Dusk4 (2.6%)
-20.0%prior 5
Dark - unknown roadway lighting4 (2.6%)

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

Road Surface

Dry100 (65.8%)
-10.7%prior 112
Wet14 (9.2%)
7.7%prior 13
Ice/frost11 (7.2%)
10.0%prior 10
Snow11 (7.2%)
-15.4%prior 13
Gravel11 (7.2%)
Slush5 (3.3%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Ford (63 vehicles) and Chevrolet (59 vehicles) leading in 2024, similar to the prior year's counts of 64 and 56, respectively. A significant change was observed in the demographics of persons involved, with the total count dropping from 538 to 361. Within these groups, the proportion of persons aged 35-44 decreased from a 23.0% share to a 13.8% share of all persons involved, while the share of those aged 65 and older increased from 12.1% to 17.5%.

Top Vehicle Makes (351 vehicles)

1
FORD63 (17.9%)
-1.6%prior 64
2
CHEV59 (16.8%)
5.4%prior 56
3
DODG19 (5.4%)
11.8%prior 17
4
JEEP17 (4.8%)
-10.5%prior 19
5
GMC17 (4.8%)
88.9%prior 9
6
TOYT11 (3.1%)
-8.3%prior 12
7
TOYO11 (3.1%)
10.0%prior 10
8
NISS11 (3.1%)
10.0%prior 10
9
CHRY9 (2.6%)
-40.0%prior 15
10
FREIGHTLINER8 (2.3%)
33.3%prior 6

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

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

Sex Distribution (158 persons with recorded sex)

Male102 (64.6%)
-49.3%prior 201
Female56 (35.4%)
-61.6%prior 146

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: 252
  • Total persons involved: 361
  • Total vehicles involved: 351

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

ThatCarHitMe.com · An Injuria.ai Company