Yearly Traffic Safety Analysis

1,799 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Pottawattamie County, total vehicle crashes decreased by 4.4% from 1,882 in 2024 to 1,799 in 2025. During this same period, the number of fatalities remained unchanged at 9, while total injuries fell by 4.3% from 646 to 618. One of the most notable shifts was an increase in crashes attributed to 'driving too fast for conditions,' which rose from 68 incidents in the prior period to 84 in the current period.

1,799

▼ -4.4%was 1,882

Total Crash Events

9

Persons Killed

618

▼ -4.3%was 646

Persons Injured

8

▼ -11.1%was 9

Fatal Crash Events

Note: "Persons Killed" (9) counts individual fatalities across all crash events. "Fatal" in the severity table below (8) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic safety trends in Pottawattamie County showed a modest improvement year-over-year. The total number of crashes declined from 1,882 to 1,799, representing a 4.4% decrease. Similarly, the number of people injured in these incidents dropped by 4.3% from 646 to 618, while fatalities held steady at 9 for both periods.

Vulnerable Road User Casualties

4

Pedestrians Killed

Prior: 4→0.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 5→0.0%

0

Other Killed

Prior: 0→0.0%

12

Pedestrians Injured

Prior: 18↓-33.3%

19

Cyclists Injured

Prior: 20↓-5.0%

585

Motorists Injured

Prior: 603↓-3.0%

2

Other Injured

Prior: 5↓-60.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained largely consistent between the two periods. Friday was the peak day for crashes in both 2025 (301 crashes) and 2024 (359 crashes), though the total count on Fridays decreased. The peak hour for collisions shifted slightly later in the day, from the 3 p.m. hour in 2024 (141 crashes) to the 4 p.m. hour in 2025 (161 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes saw minor changes year-over-year. The fatal crash rate decreased slightly from 0.48% to 0.44%, with 8 fatal crashes in 2025 compared to 9 in the prior year. The number of crashes resulting in minor injuries decreased from 180 to 161. Conversely, the share of crashes involving possible injuries increased from 19.4% in 2024 to 20.2% in 2025, even as the absolute count remained stable (365 vs. 364).

Severity is per crash event (most severe injury). 8 fatal crash events resulted in 9 persons killed.

Outcome by Severity (Crash Events)

Fatal8fatal crashes0.4%
↓-11.1%prior 9
Serious Injury39serious injury crashes2.2%
↓-2.5%prior 40
Minor Injury161minor injury crashes8.9%
↓-10.6%prior 180
Possible Injury364possible injury crashes20.2%
↓-0.3%prior 365
No Injury1,227no injury crashes68.2%
↓-4.7%prior 1,288

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes were consistent across both periods, with 'Followed too close' ranking first in both 2025 (251 incidents) and 2024 (250 incidents). The count of crashes where drivers 'Ran off road - left' decreased from 166 to 143, and incidents of 'Lost Control' fell from 113 to 96. Notably, crashes attributed to 'Driving too fast for conditions' increased by 23.5%, from 68 incidents to 84, while crashes from 'Ran Traffic Signal' decreased from 89 to 68.

Officer-Reported Primary Contributing Cause

Followed too close251 (14%)↑0.4%prior 250
Ran off road - left143 (7.9%)↓-13.9%prior 166
Lost Control96 (5.3%)↓-15.0%prior 113
FTYROW: From stop sign91 (5.1%)↓-7.1%prior 98
Animal88 (4.9%)↓-4.3%prior 92
Driving too fast for conditions84 (4.7%)↑23.5%prior 68
Ran off road - straight84 (4.7%)↓-5.6%prior 89
Ran Stop Sign81 (4.5%)↑17.4%prior 69
Made improper turn74 (4.1%)↑17.5%prior 63
Ran Traffic Signal68 (3.8%)↓-23.6%prior 89

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

In both 2025 and 2024, the vast majority of crashes occurred in favorable conditions: during daylight, on dry roads, and in clear weather. For instance, crashes on dry roads accounted for 77.8% of the total in 2025, up slightly from 74.6% in 2024. There was a notable decrease in crashes occurring on adverse road surfaces, with incidents on icy or frosty roads falling from 96 in the prior period to 41 in the current period.

Weather

Clear1,287 (75.0%)
↓-3.3%prior 1,331
Cloudy243 (14.2%)
↓-11.0%prior 273
Rain83 (4.8%)
↓-11.7%prior 94
Snow37 (2.2%)
↑12.1%prior 33
Blowing Snow22 (1.3%)
↑120.0%prior 10
Freezing rain/drizzle20 (1.2%)
↓-25.9%prior 27
Severe Winds11 (0.6%)
↑22.2%prior 9
Fog, smoke, smog7 (0.4%)
↓-53.3%prior 15
Sleet, hail6 (0.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight1,156 (66.9%)
↓-3.2%prior 1,194
Dark - roadway lighted313 (18.1%)
↓-7.1%prior 337
Dark - roadway not lighted165 (9.5%)
↓-10.3%prior 184
Dusk53 (3.1%)
↑17.8%prior 45
Dawn31 (1.8%)
↓-20.5%prior 39
Dark - unknown roadway lighting11 (0.6%)
↓-15.4%prior 13

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry1,399 (81.2%)
↓-0.4%prior 1,404
Wet181 (10.5%)
↓-13.0%prior 208
Snow72 (4.2%)
↑18.0%prior 61
Ice/frost41 (2.4%)
↓-57.3%prior 96
Slush15 (0.9%)
↑50.0%prior 10
Gravel12 (0.7%)
↓-25.0%prior 16
Sand2 (0.1%)
Other (explain in narrative)1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent year-over-year, with Ford (450 vehicles) and Chevrolet (517 vehicles, combining 'CHEV' and 'CHEVROLET') being the most common in 2025, similar to their top rankings in 2024 (480 and 524 vehicles, respectively). Demographically, the number of people involved in crashes decreased across most age groups, aligning with the overall reduction in collisions. For example, involvement for the 16-20 age group dropped from 404 to 376 individuals.

Top Vehicle Makes (3,165 vehicles)

1
FORD450 (14.2%)
↓-6.3%prior 480
2
CHEV282 (8.9%)
↓-8.1%prior 307
3
CHEVROLET235 (7.4%)
↑8.3%prior 217
4
JEEP162 (5.1%)
↑1.3%prior 160
5
NR120 (3.8%)
↑3.4%prior 116
6
KIA119 (3.8%)
↓-16.2%prior 142
7
NISS112 (3.5%)
↓-8.2%prior 122
8
TOYT109 (3.4%)
↑45.3%prior 75
9
GMC104 (3.3%)
↓-10.3%prior 116
10
HONDA101 (3.2%)
↑5.2%prior 96

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

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

Sex Distribution (2,049 persons with recorded sex)

Male1,253 (61.2%)
↓-2.6%prior 1,286
Female796 (38.8%)
↓-9.8%prior 882

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 1,799
  • Total persons involved: 3,313
  • Total vehicles involved: 3,165

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: 2025." Published September 9, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2025-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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