Yearly Traffic Safety Analysis

54 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Pocahontas County, total vehicle crashes decreased from 78 in 2023 to 54 in 2024, a 30.8% reduction. Correspondingly, the number of people injured in these incidents fell from 30 to 25. The most significant year-over-year shift was a substantial drop in crashes attributed to losing control and running stop signs.

54

▼ -30.8%was 78

Total Crash Events

0

Persons Killed

25

▼ -16.7%was 30

Persons Injured

0

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

Traffic safety trends in Pocahontas County showed a notable improvement year-over-year. The total number of crashes declined by 30.8%, from 78 in the prior period to 54 in the current period. The number of injuries also saw a decrease of 16.7%, from 30 to 25, while fatalities remained at zero in both years.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

25

Motorists Injured

Prior: 30↓-16.7%

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 shifted between the two periods. In 2024, the peak day for crashes was Saturday, with 11 incidents, a change from the prior year's peak on Monday, which saw 17 crashes. The peak hour also moved later in the day, from 3 p.m. (11 crashes) in 2023 to 7 p.m. (7 crashes) 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 patterns remained relatively stable, with zero fatal crashes recorded in either period. While total crashes decreased, the proportion of crashes resulting in minor injuries increased from 15.4% (12 incidents) in 2023 to 24.1% (13 incidents) in 2024. Conversely, crashes involving possible injuries decreased from 10 incidents to 3, representing a drop in share from 12.8% to 5.6% of all crashes.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes3.7%
→0.0%prior 2
Minor Injury13minor injury crashes24.1%
↑8.3%prior 12
Possible Injury3possible injury crashes5.6%
↓-70.0%prior 10
No Injury36no injury crashes66.7%
↓-33.3%prior 54

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 with animals remained the leading contributing factor in both periods, though the count decreased slightly from 15 in 2023 to 13 in 2024. There were significant reductions in other key factors; crashes attributed to 'Lost Control' fell by 53.8% from 13 to 6 incidents, and 'Ran Stop Sign' crashes decreased by 66.7% from 9 to 3 incidents. As a result, 'Ran off road - left' and 'Lost Control' tied for the second-most common factor in 2024, each with 6 crashes.

Officer-Reported Primary Contributing Cause

Animal13 (24.1%)↓-13.3%prior 15
Lost Control6 (11.1%)↓-53.8%prior 13
Ran off road - left6 (11.1%)→0.0%prior 6
Driving too fast for conditions5 (9.3%)→0.0%prior 5
FTYROW: From stop sign3 (5.6%)
Ran Stop Sign3 (5.6%)↓-66.7%prior 9
Driver Distraction: Other interior distraction2 (3.7%)↓-60.0%prior 5
Operating vehicle in an reckless, erratic, careless, negligent manner2 (3.7%)
Other (explain in narrative): Improper operation2 (3.7%)
Ran off road - straight2 (3.7%)

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

Road & Environmental Conditions

Year-over-year, there was a shift in the conditions under which crashes occurred. The proportion of crashes on dry road surfaces decreased from 62.8% (49 incidents) in 2023 to 46.3% (25 incidents) in 2024. While the total number of crashes in the rain was stable at 2 incidents, their share of total crashes increased from 2.6% to 3.7%. Similarly, the proportion of crashes in clear weather decreased from 62.8% to 51.9% of all incidents.

Weather

Clear28 (68.3%)
↓-42.9%prior 49
Cloudy7 (17.1%)
↓-50.0%prior 14
Rain2 (4.9%)
Blowing Snow2 (4.9%)
Fog, smoke, smog1 (2.4%)
Freezing rain/drizzle1 (2.4%)

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

Lighting

Daylight26 (63.4%)
↓-40.9%prior 44
Dark - roadway not lighted10 (24.4%)
↓-44.4%prior 18
Dark - roadway lighted4 (9.8%)
Dark - unknown roadway lighting1 (2.4%)

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

Road Surface

Dry25 (61.0%)
↓-49.0%prior 49
Gravel5 (12.2%)
↓-28.6%prior 7
Wet5 (12.2%)
Ice/frost3 (7.3%)
↓-57.1%prior 7
Snow3 (7.3%)

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

Vehicles & Demographics

Ford and Chevrolet remained the top two vehicle makes involved in crashes in both years, though Ford's involvement decreased significantly from 32 vehicles in 2023 to 13 in 2024. The age demographics of persons involved in crashes also shifted. The number of individuals in the 16-20 age group dropped from 27 to 11, and the 65+ age group saw a decrease from 18 to 8 persons involved.

Top Vehicle Makes (74 vehicles)

1
FORD13 (17.6%)
↓-59.4%prior 32
2
CHEV12 (16.2%)
↑20.0%prior 10
3
BUIC5 (6.8%)
→0.0%prior 5
4
PETERBILT4 (5.4%)
5
DODGE4 (5.4%)
6
CHRY3 (4.1%)
↓-50.0%prior 6
7
DODG3 (4.1%)
8
RAM3 (4.1%)
9
DEER2 (2.7%)
10
TOYO2 (2.7%)

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

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

Sex Distribution (35 persons with recorded sex)

Male26 (74.3%)
↓-61.8%prior 68
Female9 (25.7%)
↓-71.9%prior 32

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: 54
  • Total persons involved: 80
  • Total vehicles involved: 74

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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