Yearly Traffic Safety Analysis

718 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Webster County, a total of 718 vehicle crashes were recorded in 2025, a 2.6% increase from the 700 crashes documented in 2024. While total collisions saw a minor rise, the most significant year-over-year change was a 24.2% increase in the number of people injured, which grew from 149 to 185. Meanwhile, the number of fatalities decreased from four in the prior period to three in the current period.

718

▲ 2.6%was 700

Total Crash Events

3

▼ -25.0%was 4

Persons Killed

185

▲ 24.2%was 149

Persons Injured

3

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 · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crash trends in Webster County show a slight increase year-over-year. The total number of crashes rose from 700 in 2024 to 718 in 2025, an increase of 18 incidents. This rise in crashes was accompanied by a 24.2% increase in total injuries (from 149 to 185), even as fatalities fell from 4 to 3.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 3→0.0%

4

Pedestrians Injured

Prior: 9↓-55.6%

3

Cyclists Injured

Prior: 2↑50.0%

178

Motorists Injured

Prior: 136↑30.9%

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 timing of crashes shifted between the two periods. In 2025, Friday was the peak day for crashes with 130 incidents, a change from the prior year when Monday was the peak day with 117 crashes. The most frequent time for collisions also shifted, moving from the 3 p.m. hour (61 crashes) in 2024 to the 5 p.m. hour (65 crashes) in 2025.

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

While the number of fatal crashes remained constant at three in both 2024 and 2025, the overall severity of crashes increased. The proportion of collisions resulting in any type of injury rose from 22.5% of all crashes in the prior period to 25.9% in the current period. This was driven by increases in both serious injury crashes, which went from 13 to 17, and possible injury crashes, which rose from 90 to 112.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.4%
→0.0%prior 3
Serious Injury17serious injury crashes2.4%
↑30.8%prior 13
Minor Injury57minor injury crashes7.9%
↑5.6%prior 54
Possible Injury112possible injury crashes15.6%
↑24.4%prior 90
No Injury529no injury crashes73.7%
↓-2.0%prior 540

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

Collisions involving animals remained a leading contributing factor in both periods, though the count decreased from 96 to 85. The most notable shift was an 83% increase in crashes attributed to 'Other interior distraction,' which rose from 29 incidents in 2024 to 53 in 2025, becoming the third-most cited factor. Conversely, crashes due to 'Failure to yield from a stop sign' decreased in count from 55 to 42.

Officer-Reported Primary Contributing Cause

Animal85 (11.8%)↓-11.5%prior 96
Other (explain in narrative): Other58 (8.1%)↓-4.9%prior 61
Driver Distraction: Other interior distraction53 (7.4%)↑82.8%prior 29
Followed too close47 (6.5%)↓-7.8%prior 51
FTYROW: From stop sign42 (5.8%)↓-23.6%prior 55
Driving too fast for conditions39 (5.4%)↑5.4%prior 37
Ran off road - left36 (5%)↑5.9%prior 34
Ran Stop Sign30 (4.2%)↑66.7%prior 18
FTYROW: Making left turn27 (3.8%)↑12.5%prior 24
Operating vehicle in an reckless, erratic, careless, negligent manner21 (2.9%)↑50.0%prior 14

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

Road & Environmental Conditions

The analysis of environmental conditions reveals a significant shift in the role of road surface conditions year-over-year. While crashes in clear weather and daylight remained dominant and relatively stable, incidents on snowy roads increased from 30 to 84. Similarly, crashes on icy or frosty surfaces rose from 37 to 44, indicating a greater prevalence of winter-related road condition crashes in the current period.

Weather

Clear428 (67.3%)
→0.0%prior 428
Cloudy131 (20.6%)
↑3.1%prior 127
Snow32 (5.0%)
↑39.1%prior 23
Rain18 (2.8%)
↓-5.3%prior 19
Blowing Snow15 (2.4%)
↑150.0%prior 6
Freezing rain/drizzle5 (0.8%)
Fog, smoke, smog4 (0.6%)
Severe Winds2 (0.3%)
Sleet, hail1 (0.2%)

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

Lighting

Daylight447 (70.1%)
↑5.7%prior 423
Dark - roadway lighted95 (14.9%)
→0.0%prior 95
Dark - roadway not lighted60 (9.4%)
↓-6.3%prior 64
Dawn18 (2.8%)
↑38.5%prior 13
Dusk16 (2.5%)
→0.0%prior 16
Dark - unknown roadway lighting2 (0.3%)
↓-81.8%prior 11

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

Road Surface

Dry444 (69.4%)
↓-7.7%prior 481
Snow84 (13.1%)
↑180.0%prior 30
Wet51 (8.0%)
→0.0%prior 51
Ice/frost44 (6.9%)
↑18.9%prior 37
Slush9 (1.4%)
Gravel7 (1.1%)
↓-30.0%prior 10
Other (explain in narrative)1 (0.2%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes were consistent, with Chevrolet (217) and Ford (183) leading in the current period, similar to the prior year. However, the number of Jeeps involved in collisions saw a notable increase from 49 to 82, and Toyotas rose from 61 to 86. The age distribution of people involved in crashes showed that the 26-34 age group grew from 141 individuals in 2024 to 185 in 2025, becoming the second-largest group after the 16-20 age group (186 people).

Top Vehicle Makes (1,249 vehicles)

1
CHEV217 (17.4%)
→0.0%prior 217
2
FORD183 (14.7%)
↓-6.6%prior 196
3
TOYO86 (6.9%)
↑41.0%prior 61
4
JEEP82 (6.6%)
↑67.3%prior 49
5
DODG71 (5.7%)
↑22.4%prior 58
6
GMC68 (5.4%)
↑51.1%prior 45
7
CHEVROLET46 (3.7%)
↑9.5%prior 42
8
CHRY45 (3.6%)
↑15.4%prior 39
9
NR42 (3.4%)
→0.0%prior 42
10
BUIC32 (2.6%)
↓-33.3%prior 48

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

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

Sex Distribution (803 persons with recorded sex)

Male445 (55.4%)
↑11.0%prior 401
Female358 (44.6%)
↓-3.2%prior 370

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: 718
  • Total persons involved: 1,273
  • Total vehicles involved: 1,249

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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