Yearly Traffic Safety Analysis

499 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Sioux County, a total of 499 traffic crashes were recorded in 2024, a 1.4% decrease from the 506 crashes in 2023. While overall crashes remained relatively stable, the most notable year-over-year shift was a 50% reduction in fatalities, which fell from 6 to 3. This decrease in fatalities occurred alongside a 26.7% increase in the number of people injured, which rose from 187 to 237.

499

▼ -1.4%was 506

Total Crash Events

3

▼ -50.0%was 6

Persons Killed

237

▲ 26.7%was 187

Persons Injured

2

▼ -66.7%was 6

Fatal Crash Events

Note: "Persons Killed" (3) 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 trends in Sioux County show a slight overall decrease in volume, with total incidents falling by 1.4% from 506 in 2023 to 499 in 2024. However, the severity of outcomes shifted, as the number of individuals injured increased by 26.7% (from 187 to 237), while the number of fatalities was halved, dropping from 6 to 3.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 5↓-40.0%

0

Other Killed

Prior: 0→0.0%

2

Pedestrians Injured

Prior: 1↑100.0%

1

Cyclists Injured

Prior: 2↓-50.0%

233

Motorists Injured

Prior: 181↑28.7%

1

Other Injured

Prior: 3↓-66.7%

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 temporal pattern of crashes shifted between the two periods. The peak day for crashes moved from Friday (86 incidents) in 2023 to Monday (95 incidents) in 2024. The peak hour for collisions remained 5 p.m. across both years, although the number of crashes occurring during this hour decreased from 47 in 2023 to 39 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

There was a notable decrease in crash severity year-over-year. The number of fatal crashes fell from 6 (1.2% of all crashes) in 2023 to 2 (0.4%) in 2024. Concurrently, the proportion of crashes resulting in no injuries decreased from 72.7% to 67.1%. Crashes resulting in possible injuries saw the largest proportional increase, rising from 10.3% of all incidents (52 crashes) to 15.2% (76 crashes).

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.4%
↓-66.7%prior 6
Serious Injury13serious injury crashes2.6%
↑8.3%prior 12
Minor Injury73minor injury crashes14.6%
↑7.4%prior 68
Possible Injury76possible injury crashes15.2%
↑46.2%prior 52
No Injury335no injury crashes67.1%
↓-9.0%prior 368

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 changed year-over-year. In 2023, collisions involving an animal were the leading cause with 106 incidents. In 2024, this factor's count decreased to 63 crashes, and "Followed too close" became the top factor, with its count increasing from 51 to 70 crashes. The number of crashes attributed to "Driving too fast for conditions" also saw a significant reduction, falling from 51 in the prior period to 37 in the current period.

Officer-Reported Primary Contributing Cause

Followed too close70 (14%)↑37.3%prior 51
Animal63 (12.6%)↓-40.6%prior 106
Driving too fast for conditions37 (7.4%)↓-27.5%prior 51
FTYROW: From stop sign33 (6.6%)↓-2.9%prior 34
Ran off road - left32 (6.4%)↑28.0%prior 25
Lost Control28 (5.6%)↓-15.2%prior 33
FTYROW: At uncontrolled intersection23 (4.6%)↑155.6%prior 9
Ran Stop Sign20 (4%)↑66.7%prior 12
FTYROW: Making left turn15 (3%)↑25.0%prior 12
Made improper turn15 (3%)↑87.5%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

Compared to the prior period, a larger proportion of crashes in the current period occurred under favorable conditions. Crashes in clear weather increased from a 54.2% share to 59.7%, and those on dry roads rose from 54.9% to 62.5% of all incidents. Correspondingly, the share of crashes on roads with ice or frost decreased from 10.3% in 2023 to 7.4% in 2024.

Weather

Clear298 (67.0%)
↑8.8%prior 274
Cloudy89 (20.0%)
↓-5.3%prior 94
Snow18 (4.0%)
↓-10.0%prior 20
Rain14 (3.1%)
↑40.0%prior 10
Fog, smoke, smog11 (2.5%)
↑10.0%prior 10
Freezing rain/drizzle6 (1.3%)
Blowing Snow4 (0.9%)
↓-50.0%prior 8
Severe Winds4 (0.9%)
Other (explain in narrative)1 (0.2%)

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

Lighting

Daylight316 (70.7%)
↑11.3%prior 284
Dark - roadway not lighted72 (16.1%)
↓-2.7%prior 74
Dark - roadway lighted35 (7.8%)
↓-16.7%prior 42
Dusk12 (2.7%)
→0.0%prior 12
Dawn11 (2.5%)
↑22.2%prior 9
Dark - unknown roadway lighting1 (0.2%)

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

Road Surface

Dry312 (70.1%)
↑12.2%prior 278
Snow40 (9.0%)
↑8.1%prior 37
Ice/frost37 (8.3%)
↓-28.8%prior 52
Wet35 (7.9%)
↑29.6%prior 27
Gravel17 (3.8%)
↓-15.0%prior 20
Slush3 (0.7%)
↓-57.1%prior 7
Mud, dirt1 (0.2%)

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

Vehicles & Demographics

Chevrolet and Ford vehicles were the most frequently involved in crashes during both periods. The number of involved Chevrolet vehicles decreased from 207 to 194, while the count for Ford vehicles increased from 140 to 170. Analysis of persons involved reveals a demographic shift; the proportion of individuals aged 65 and older increased from 9.2% to 13.2% of all persons involved, while the share for the 16-20 age group decreased from 19.9% to 17.9%.

Top Vehicle Makes (838 vehicles)

1
FORD170 (20.3%)
↑21.4%prior 140
2
CHEV136 (16.2%)
↓-5.6%prior 144
3
CHEVROLET58 (6.9%)
↓-7.9%prior 63
4
JEEP40 (4.8%)
↑42.9%prior 28
5
GMC37 (4.4%)
→0.0%prior 37
6
BUIC31 (3.7%)
↑34.8%prior 23
7
HOND27 (3.2%)
↑68.8%prior 16
8
DODG24 (2.9%)
↑26.3%prior 19
9
KIA18 (2.1%)
↑100.0%prior 9
10
RAM18 (2.1%)
↑63.6%prior 11

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

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

Sex Distribution (552 persons with recorded sex)

Male329 (59.6%)
↓-26.7%prior 449
Female223 (40.4%)
↓-19.8%prior 278

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: 499
  • Total persons involved: 906
  • Total vehicles involved: 838

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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