Yearly Traffic Safety Analysis

275 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Benton County, total traffic crashes decreased by 5.2%, from 290 in 2023 to 275 in 2024. Despite this overall reduction in collisions, the number of fatalities doubled from 2 to 4 year-over-year. The most significant contributing factor in both periods remained collisions involving animals, though these incidents decreased from 92 to 76.

275

▼ -5.2%was 290

Total Crash Events

4

▲ 100.0%was 2

Persons Killed

78

▼ -17.9%was 95

Persons Injured

4

▲ 100.0%was 2

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 data for Benton County indicates a downward trend in the total number of crashes and injuries, but a negative trend for fatalities. Total crashes fell by 5.2% from 290 to 275, and total injuries decreased by 17.9% from 95 to 78. Conversely, traffic fatalities increased from 2 in 2023 to 4 in 2024.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 1↓-100.0%

4

Motorists Killed

Prior: 1↑300.0%

1

Cyclists Injured

Prior: 0→%

77

Motorists Injured

Prior: 95↓-18.9%

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. While the peak hour for crashes remained 5 p.m. in both 2023 (26 crashes) and 2024 (30 crashes), the peak day for crashes moved from Friday (50 crashes) in the prior year to Saturday (57 crashes) in the current year. Crashes on Saturdays saw a notable increase from 37 to 57 year-over-year.

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

While total crashes declined, the severity of outcomes worsened year-over-year. The number of fatal crashes doubled from 2 to 4, causing the fatal crash rate to increase from 0.69 to 1.45 per 100 crashes. The count of serious injury crashes also rose from 8 to 10. Consequently, the share of crashes involving no injuries decreased from 73.8% in 2023 to 73.1% in 2024.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.5%
↑100.0%prior 2
Serious Injury10serious injury crashes3.6%
↑25.0%prior 8
Minor Injury33minor injury crashes12%
↓-5.7%prior 35
Possible Injury27possible injury crashes9.8%
↓-12.9%prior 31
No Injury201no injury crashes73.1%
↓-6.1%prior 214

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 were the leading contributing factor in both 2023 and 2024, though the count of these incidents decreased from 92 to 76. The second-ranked factor, 'Lost Control,' also saw a decrease in count from 36 to 30 incidents. Conversely, crashes attributed to 'Driving too fast for conditions' increased in count from 17 in 2023 to 22 in 2024, moving it from the fourth to the third most common factor.

Officer-Reported Primary Contributing Cause

Animal76 (27.6%)↓-17.4%prior 92
Lost Control30 (10.9%)↓-16.7%prior 36
Driving too fast for conditions22 (8%)↑29.4%prior 17
Ran off road - left21 (7.6%)↑40.0%prior 15
Driver Distraction: Other interior distraction12 (4.4%)↑71.4%prior 7
FTYROW: From stop sign11 (4%)↑10.0%prior 10
Ran off road - straight10 (3.6%)↓-47.4%prior 19
Followed too close10 (3.6%)↓-16.7%prior 12
Driver Distraction: Inattentive/lost in thought7 (2.5%)
Other (explain in narrative): Other7 (2.5%)↓-12.5%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

Crash conditions remained broadly consistent year-over-year, with most incidents in both periods occurring in clear weather (143 in 2024 vs. 150 in 2023) and on dry roads (153 vs. 156). Crashes during daylight hours were most frequent in both years, increasing from 132 to 140. There was no significant shift in the proportion of crashes occurring under adverse weather, lighting, or road surface conditions.

Weather

Clear143 (67.1%)
↓-4.7%prior 150
Cloudy39 (18.3%)
↑11.4%prior 35
Snow9 (4.2%)
↑28.6%prior 7
Freezing rain/drizzle7 (3.3%)
→0.0%prior 7
Rain7 (3.3%)
Fog, smoke, smog6 (2.8%)
→0.0%prior 6
Blowing Snow2 (0.9%)

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

Lighting

Daylight140 (64.5%)
↑6.1%prior 132
Dark - roadway not lighted43 (19.8%)
↓-18.9%prior 53
Dark - roadway lighted13 (6.0%)
↓-13.3%prior 15
Dawn10 (4.6%)
↑25.0%prior 8
Dark - unknown roadway lighting6 (2.8%)
Dusk5 (2.3%)

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

Road Surface

Dry153 (71.8%)
↓-1.9%prior 156
Ice/frost22 (10.3%)
↑15.8%prior 19
Wet15 (7.0%)
↑25.0%prior 12
Gravel12 (5.6%)
↑9.1%prior 11
Snow10 (4.7%)
↓-16.7%prior 12
Slush1 (0.5%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes were consistent, with Ford (70 crashes) and Chevrolet (72 crashes including 'CHEV' and 'CHEVROLET') leading in 2024, similar to 2023 (85 Ford, 82 Chevrolet). The age distribution of persons involved in crashes showed a notable shift; the 26-34 age group was the largest in 2024 with 69 individuals, whereas the 35-44 age group was largest in 2023 with 102 individuals. The number of people aged 35-44 and 65+ involved in crashes saw significant decreases from 102 to 59 and 87 to 39, respectively.

Top Vehicle Makes (387 vehicles)

1
FORD70 (18.1%)
↓-17.6%prior 85
2
CHEV61 (15.8%)
↓-11.6%prior 69
3
DODG18 (4.7%)
↓-14.3%prior 21
4
JEEP18 (4.7%)
↑28.6%prior 14
5
GMC15 (3.9%)
↑50.0%prior 10
6
HOND15 (3.9%)
↑7.1%prior 14
7
TOYT13 (3.4%)
↓-55.2%prior 29
8
NISS13 (3.4%)
↑8.3%prior 12
9
CHRY12 (3.1%)
↑33.3%prior 9
10
KIA11 (2.8%)
↑57.1%prior 7

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 (216 persons with recorded sex)

Male142 (65.7%)
↓-41.8%prior 244
Female74 (34.3%)
↓-53.5%prior 159

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: 275
  • Total persons involved: 398
  • Total vehicles involved: 387

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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