Yearly Traffic Safety Analysis

378 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Plymouth County, total vehicle crashes decreased by 7.4% in 2024, falling from 408 in the prior year to 378. Despite this overall reduction in crashes, the most notable year-over-year shift was an increase in traffic fatalities, which rose from 5 to 6. The number of fatal crashes also increased from 4 to 5 during the same period.

378

▼ -7.4%was 408

Total Crash Events

6

▲ 20.0%was 5

Persons Killed

115

▼ -4.2%was 120

Persons Injured

5

▲ 25.0%was 4

Fatal Crash Events

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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

The overall trend in Plymouth County shows a decrease in traffic incidents, with total crashes falling from 408 to 378 year-over-year. Total injuries also saw a slight decline from 120 to 115. However, this downward trend did not extend to the most severe outcomes, as the number of fatalities increased from 5 to 6.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

6

Motorists Killed

Prior: 5↑20.0%

1

Pedestrians Injured

Prior: 2↓-50.0%

3

Cyclists Injured

Prior: 2↑50.0%

111

Motorists Injured

Prior: 114↓-2.6%

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 shifted between the two periods. In 2024, the peak day for crashes was Friday, with 78 incidents, a change from the prior year when Wednesday was the peak day with 68 crashes. The peak hour also shifted slightly earlier, moving from the 5 PM hour (38 crashes) in 2023 to the 4 PM hour (35 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

While total crashes declined, the severity of crashes increased year-over-year. The number of fatal crashes rose from 4 to 5, and the fatal crash rate increased from 0.98% to 1.32% of all crashes. The number of serious injury crashes remained unchanged at 11. Crashes resulting in minor or possible injuries saw a slight decrease in absolute numbers.

Severity is per crash event (most severe injury). 5 fatal crash events resulted in 6 persons killed.

Outcome by Severity (Crash Events)

Fatal5fatal crashes1.3%
↑25.0%prior 4
Serious Injury11serious injury crashes2.9%
→0.0%prior 11
Minor Injury43minor injury crashes11.4%
↓-10.4%prior 48
Possible Injury37possible injury crashes9.8%
↓-2.6%prior 38
No Injury282no injury crashes74.6%
↓-8.1%prior 307

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, with the count of such incidents increasing from 80 to 90. A significant change was observed in crashes attributed to 'Lost Control,' which decreased in count by 42%, from 38 incidents in 2023 to 22 in 2024. 'Followed too close' became the second most common factor in the current period with 25 crashes, up from 22 in the prior year.

Officer-Reported Primary Contributing Cause

Animal90 (23.8%)↑12.5%prior 80
Followed too close25 (6.6%)↑13.6%prior 22
Driving too fast for conditions23 (6.1%)↓-14.8%prior 27
FTYROW: From stop sign23 (6.1%)↑15.0%prior 20
Lost Control22 (5.8%)↓-42.1%prior 38
Ran off road - straight20 (5.3%)↓-13.0%prior 23
Ran Stop Sign17 (4.5%)↑6.3%prior 16
Ran off road - left15 (4%)↓-21.1%prior 19
Other (explain in narrative): Other14 (3.7%)↓-41.7%prior 24
Driver Distraction: Other interior distraction12 (3.2%)↑71.4%prior 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

There was a notable shift in the conditions under which crashes occurred, particularly regarding road surface. Crashes on icy or frosty roads decreased significantly, from 47 incidents in the prior year to 25 in 2024. Similarly, crashes on wet surfaces declined from 29 to 19. Crashes in clear weather and on dry roads remained the most common scenario in both periods but saw a decrease in absolute numbers, consistent with the overall trend.

Weather

Clear215 (73.1%)
↓-9.3%prior 237
Cloudy41 (13.9%)
↓-18.0%prior 50
Snow10 (3.4%)
↓-23.1%prior 13
Freezing rain/drizzle7 (2.4%)
Rain6 (2.0%)
↓-62.5%prior 16
Fog, smoke, smog3 (1.0%)
↓-70.0%prior 10
Blowing Snow3 (1.0%)
↓-72.7%prior 11
Other (explain in narrative)3 (1.0%)
Severe Winds3 (1.0%)
Blowing sand, soil, dirt2 (0.7%)

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

Lighting

Daylight208 (70.0%)
↓-12.2%prior 237
Dark - roadway not lighted53 (17.8%)
↓-18.5%prior 65
Dark - roadway lighted16 (5.4%)
↓-20.0%prior 20
Dawn9 (3.0%)
↓-18.2%prior 11
Dusk8 (2.7%)
↓-20.0%prior 10
Dark - unknown roadway lighting3 (1.0%)
↓-40.0%prior 5

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

Road Surface

Dry212 (71.9%)
↓-10.5%prior 237
Ice/frost25 (8.5%)
↓-46.8%prior 47
Snow21 (7.1%)
↑10.5%prior 19
Wet19 (6.4%)
↓-34.5%prior 29
Gravel10 (3.4%)
↑25.0%prior 8
Slush4 (1.4%)
Mud, dirt3 (1.0%)
Sand1 (0.3%)

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 showed some shifts year-over-year. While Ford was the top make in the prior period with 113 vehicles, its involvement decreased to 109. Chevrolet vehicles (combining 'CHEV' and 'CHEVROLET' entries) saw an increase from 108 to 121, making it the most frequently involved make in the current period. Notably, the number of GMC vehicles in crashes dropped from 50 to 29.

Top Vehicle Makes (583 vehicles)

1
FORD109 (18.7%)
↓-3.5%prior 113
2
CHEV88 (15.1%)
↑10.0%prior 80
3
CHEVROLET33 (5.7%)
↑17.9%prior 28
4
GMC29 (5%)
↓-42.0%prior 50
5
JEEP25 (4.3%)
↓-7.4%prior 27
6
DODG24 (4.1%)
↑41.2%prior 17
7
TOYO20 (3.4%)
↑66.7%prior 12
8
NISS15 (2.6%)
↑36.4%prior 11
9
RAM14 (2.4%)
→0.0%prior 14
10
HOND13 (2.2%)
↓-7.1%prior 14

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

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

Sex Distribution (357 persons with recorded sex)

Male207 (58.0%)
↓-44.5%prior 373
Female150 (42.0%)
↓-31.8%prior 220

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: 378
  • Total persons involved: 604
  • Total vehicles involved: 583

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

ThatCarHitMe.com · An Injuria.ai Company