Yearly Traffic Safety Analysis

359 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In 2024, Hamilton County recorded 359 total crashes, a 5.3% increase from the 341 crashes reported in 2023. While the overall crash count and the number of fatalities rose, from 2 to 3, the number of crashes resulting in serious injuries decreased significantly. These serious injury crashes fell from 11 in the prior period to 4 in the current period.

359

▲ 5.3%was 341

Total Crash Events

3

▲ 50.0%was 2

Persons Killed

84

▼ -5.6%was 89

Persons Injured

3

▲ 50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 Hamilton County show a slight increase year-over-year, with total collisions rising by 5.3% from 341 in 2023 to 359 in 2024. Despite this increase in total incidents, the number of people injured fell by 5.6% from 89 to 84. The number of fatalities rose from 2 to 3.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 2↑50.0%

2

Cyclists Injured

Prior: 0→%

82

Motorists Injured

Prior: 89↓-7.9%

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 2023 and 2024. Thursday remained the peak day for crashes in both periods, with 58 and 65 crashes respectively. However, the peak hour for crashes shifted from the 3 p.m. hour in 2023 (27 crashes) to the 4 p.m. hour in 2024 (30 crashes). A notable change occurred on weekends, with Sunday crashes increasing from 37 in the prior year to 60 in the current year, while Saturday crashes decreased from 55 to 35.

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

In 2024, Hamilton County saw an increase in fatal crashes, rising to 3 from 2 in the previous year, with the fatal crash rate increasing from 0.59 to 0.84 per 100 crashes. Conversely, crashes resulting in serious injuries saw a marked decrease, falling from 11 in 2023 to 4 in 2024. The share of serious injury crashes dropped from 3.2% to 1.1% of all incidents. Crashes involving minor or possible injuries increased in both count and proportion, while the share of no-injury crashes decreased slightly from 79.2% to 77.7%.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.8%
↑50.0%prior 2
Serious Injury4serious injury crashes1.1%
↓-63.6%prior 11
Minor Injury30minor injury crashes8.4%
↑15.4%prior 26
Possible Injury43possible injury crashes12%
↑34.4%prior 32
No Injury279no injury crashes77.7%
↑3.3%prior 270

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 87 incidents in 2023 to 81 in 2024. The most significant change was the drop in crashes attributed to 'Driving too fast for conditions,' which fell from 31 incidents in the prior year to 16 in the current year. 'Lost Control' also saw a minor decrease from 32 to 30 incidents. Meanwhile, crashes with the factor 'Other (explain in narrative): Other' increased from 26 to 37, becoming the second-most cited factor in 2024.

Officer-Reported Primary Contributing Cause

Animal81 (22.6%)↓-6.9%prior 87
Other (explain in narrative): Other37 (10.3%)↑42.3%prior 26
Lost Control30 (8.4%)↓-6.3%prior 32
Ran off road - straight23 (6.4%)↓-17.9%prior 28
Ran off road - left18 (5%)↑38.5%prior 13
Driving too fast for conditions16 (4.5%)↓-48.4%prior 31
FTYROW: From stop sign16 (4.5%)→0.0%prior 16
Followed too close10 (2.8%)↓-16.7%prior 12
Driver Distraction: Other interior distraction9 (2.5%)↓-10.0%prior 10
Operating vehicle in an reckless, erratic, careless, negligent manner8 (2.2%)↑60.0%prior 5

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

Road & Environmental Conditions

The majority of crashes in both periods occurred in clear weather and on dry roads. In 2024, the number of crashes on dry road surfaces increased from 174 to 211, and their share of all crashes grew from 51.0% to 58.8%. Similarly, crashes in clear weather rose from 169 to 199. The proportion of crashes occurring in darkness remained stable, with incidents on unlighted dark roadways accounting for approximately 18.7% of crashes in both years.

Weather

Clear199 (66.3%)
↑17.8%prior 169
Cloudy46 (15.3%)
→0.0%prior 46
Snow18 (6.0%)
↑12.5%prior 16
Rain10 (3.3%)
→0.0%prior 10
Blowing Snow8 (2.7%)
↓-27.3%prior 11
Fog, smoke, smog8 (2.7%)
Freezing rain/drizzle7 (2.3%)
↓-41.7%prior 12
Severe Winds3 (1.0%)
Other (explain in narrative)1 (0.3%)

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

Lighting

Daylight188 (61.4%)
↑10.6%prior 170
Dark - roadway not lighted67 (21.9%)
↑4.7%prior 64
Dark - roadway lighted28 (9.2%)
↑55.6%prior 18
Dawn10 (3.3%)
↓-23.1%prior 13
Dusk8 (2.6%)
↑60.0%prior 5
Dark - unknown roadway lighting5 (1.6%)

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

Road Surface

Dry211 (70.6%)
↑21.3%prior 174
Ice/frost34 (11.4%)
↓-12.8%prior 39
Wet25 (8.4%)
↑13.6%prior 22
Snow24 (8.0%)
↑20.0%prior 20
Gravel3 (1.0%)
↓-66.7%prior 9
Slush2 (0.7%)

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 year-over-year, with Chevrolet and Ford vehicles leading in both periods. In 2024, Chevrolet-branded vehicles (listed as 'CHEV' and 'CHEVROLET') were involved in a combined 114 incidents, compared to a combined 105 in 2023. Ford vehicles were involved in 62 crashes in 2024, a decrease from 74 in the prior year. The representation of large trucks like Freightliner was also steady, with 17 vehicles involved in the current period and 19 in the prior period.

Top Vehicle Makes (532 vehicles)

1
CHEV73 (13.7%)
↓-3.9%prior 76
2
FORD62 (11.7%)
↓-16.2%prior 74
3
CHEVROLET41 (7.7%)
↑41.4%prior 29
4
GMC29 (5.5%)
↑20.8%prior 24
5
TOYT24 (4.5%)
↑50.0%prior 16
6
DODG20 (3.8%)
→0.0%prior 20
7
JEEP17 (3.2%)
↑6.3%prior 16
8
FREIGHTLINER17 (3.2%)
↓-10.5%prior 19
9
DODGE16 (3%)
↑14.3%prior 14
10
HOND14 (2.6%)
↑27.3%prior 11

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

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

Sex Distribution (325 persons with recorded sex)

Male217 (66.8%)
↓-23.9%prior 285
Female108 (33.2%)
↓-24.5%prior 143

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: 359
  • Total persons involved: 546
  • Total vehicles involved: 532

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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