Yearly Traffic Safety Analysis

682 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Marshall County, traffic crashes increased slightly from 663 in 2023 to 682 in 2024, a change of approximately 2.9%. Despite the rise in total incidents, the number of fatalities decreased from 7 to 4, and total injuries fell from 218 to 185. The most significant year-over-year change was the 15.1% reduction in persons injured in crashes.

682

▲ 2.9%was 663

Total Crash Events

4

▼ -42.9%was 7

Persons Killed

185

▼ -15.1%was 218

Persons Injured

4

▼ -33.3%was 6

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

Overall traffic crash volume in Marshall County saw a minor increase of 2.9% from 2023 to 2024, with 19 more crashes reported in the current period. However, the severity of these crashes decreased, as evidenced by a 42.9% drop in fatalities (from 7 to 4) and a 15.1% decline in total injuries (from 218 to 185).

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 5↓-20.0%

1

Pedestrians Injured

Prior: 6↓-83.3%

4

Cyclists Injured

Prior: 5↓-20.0%

180

Motorists Injured

Prior: 206↓-12.6%

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 in Marshall County shifted between the two periods. In 2024, the peak day for crashes was Tuesday with 114 incidents, a change from Friday in the prior year which saw 124 crashes. Similarly, the peak hour moved from 3 p.m. in 2023 (67 crashes) to 5 p.m. in 2024 (48 crashes), aligning more closely with the end of the typical workday.

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 showed a mixed but generally stable pattern year-over-year. The proportion of fatal crashes decreased from 0.9% of all incidents in 2023 to 0.6% in 2024. While the share of serious injury crashes remained steady at around 3.3-3.4%, there was a shift in less severe categories: minor injury crashes decreased their share from 10.6% to 9.1%, while possible injury crashes increased from 11.6% to 13.2%.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.6%
↓-33.3%prior 6
Serious Injury23serious injury crashes3.4%
↑4.5%prior 22
Minor Injury62minor injury crashes9.1%
↓-11.4%prior 70
Possible Injury90possible injury crashes13.2%
↑16.9%prior 77
No Injury503no injury crashes73.8%
↑3.1%prior 488

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 animals remained the leading contributing factor in both periods, with counts rising from 139 to 147. 'Failure to yield from a stop sign' also remained the second-most common factor, increasing from 57 to 63 incidents. A notable shift occurred with 'Driver Distraction: Other interior distraction,' which more than doubled in count from 16 crashes in 2023 to 35 in 2024, entering the top five factors. Conversely, incidents attributed to 'Followed too close' and 'Lost Control' both decreased, each falling from 42 crashes in the prior year.

Officer-Reported Primary Contributing Cause

Animal147 (21.6%)↑5.8%prior 139
FTYROW: From stop sign63 (9.2%)↑10.5%prior 57
Lost Control36 (5.3%)↓-14.3%prior 42
Driver Distraction: Other interior distraction35 (5.1%)↑118.8%prior 16
Followed too close35 (5.1%)↓-16.7%prior 42
Other (explain in narrative): Other32 (4.7%)↓-3.0%prior 33
Driving too fast for conditions27 (4%)↓-22.9%prior 35
FTYROW: Making left turn27 (4%)↓-25.0%prior 36
Ran Stop Sign25 (3.7%)↑13.6%prior 22
Ran Traffic Signal23 (3.4%)↑4.5%prior 22

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 similar year-over-year, with the majority of incidents in both periods occurring in daylight on dry roads. The proportion of crashes in clear weather was stable, accounting for 59.7% in 2024 versus 57.6% in 2023. However, there was a noticeable decrease in crashes attributed to winter conditions; incidents on snowy roads fell from 29 to 16, and crashes on icy or frosty surfaces dropped from 30 to 18.

Weather

Clear407 (72.8%)
↑6.5%prior 382
Cloudy84 (15.0%)
↑1.2%prior 83
Rain30 (5.4%)
↑36.4%prior 22
Snow16 (2.9%)
↓-44.8%prior 29
Fog, smoke, smog9 (1.6%)
↓-25.0%prior 12
Blowing Snow5 (0.9%)
Freezing rain/drizzle5 (0.9%)
↓-54.5%prior 11
Severe Winds3 (0.5%)

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

Lighting

Daylight365 (64.1%)
↑0.3%prior 364
Dark - roadway not lighted86 (15.1%)
↑3.6%prior 83
Dark - roadway lighted72 (12.7%)
↓-6.5%prior 77
Dusk21 (3.7%)
↑162.5%prior 8
Dawn17 (3.0%)
↑88.9%prior 9
Dark - unknown roadway lighting8 (1.4%)

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

Road Surface

Dry428 (76.0%)
↑3.1%prior 415
Wet63 (11.2%)
↑28.6%prior 49
Snow33 (5.9%)
↑6.5%prior 31
Ice/frost18 (3.2%)
↓-40.0%prior 30
Gravel13 (2.3%)
↓-18.8%prior 16
Slush7 (1.2%)
Sand1 (0.2%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes saw a minor shift at the top of the rankings. Chevrolet (175 vehicles) overtook Ford (166 vehicles) as the most frequently involved make in 2024, a reversal from 2023 when Ford led with 179 vehicles. Regarding person demographics, the proportional involvement of most age groups remained stable year-over-year. However, the share of individuals aged 65 and older involved in crashes increased from 10.5% of all persons in 2023 to 12.6% in 2024.

Top Vehicle Makes (1,130 vehicles)

1
CHEV175 (15.5%)
↑0.6%prior 174
2
FORD166 (14.7%)
↓-7.3%prior 179
3
DODG62 (5.5%)
↑10.7%prior 56
4
HOND56 (5%)
↓-16.4%prior 67
5
CHEVROLET55 (4.9%)
↑61.8%prior 34
6
JEEP53 (4.7%)
→0.0%prior 53
7
TOYT52 (4.6%)
↑20.9%prior 43
8
GMC47 (4.2%)
↑30.6%prior 36
9
NISS45 (4%)
↑18.4%prior 38
10
BUIC32 (2.8%)
↑45.5%prior 22

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

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

Sex Distribution (675 persons with recorded sex)

Male406 (60.1%)
↓-27.5%prior 560
Female269 (39.9%)
↓-29.0%prior 379

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: 682
  • Total persons involved: 1,159
  • Total vehicles involved: 1,130

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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