Yearly Traffic Safety Analysis

58 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Taylor County recorded 58 total crashes, representing a 16% increase from the 50 crashes reported in 2024. While overall crashes rose, the number of people injured decreased from 20 to 14, and no fatalities were reported in either period. The most notable year-over-year shift was a significant increase in crashes involving animals, which rose from 14 incidents in 2024 to 25 in 2025.

58

▲ 16.0%was 50

Total Crash Events

0

Persons Killed

14

▼ -30.0%was 20

Persons Injured

0

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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

Crash totals in Taylor County increased by 16% in 2025, rising from 50 to 58 incidents compared to the previous year. Despite the rise in overall crashes, the number of people injured fell by 30%, from 20 in 2024 to 14 in 2025. No traffic fatalities were recorded in either period.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

14

Motorists Injured

Prior: 17↓-17.6%

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 shifted between the two periods. In 2025, the peak day for crashes was Wednesday with 12 incidents, a change from Monday which saw the most crashes (12) in 2024. The peak hour also moved earlier in the day, from 8 p.m. (5 crashes) in the prior year to 5 p.m. (7 crashes) in the current year.

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

There were no fatal crashes recorded in either 2025 or 2024. The proportion of crashes resulting in an injury decreased, with incidents involving a serious, minor, or possible injury accounting for 19% of all crashes in 2025, down from a 36% share in 2024. Correspondingly, the share of no-injury crashes rose from 64% (32 incidents) in the prior year to 81% (47 incidents) in the current year.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes3.4%
→0.0%prior 2
Minor Injury4minor injury crashes6.9%
↓-55.6%prior 9
Possible Injury5possible injury crashes8.6%
↓-28.6%prior 7
No Injury47no injury crashes81%
↑46.9%prior 32

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 with animals remained the leading contributing factor and grew significantly, with the count of such incidents increasing by 79% from 14 in 2024 to 25 in 2025. As a result, the share of crashes attributed to animals rose from 28% in the prior year to 43.1% in the current year. The number of crashes due to 'Followed too close' increased from 1 to 3, and incidents of 'Ran off road - straight' also increased from 1 to 3.

Officer-Reported Primary Contributing Cause

Animal25 (43.1%)↑78.6%prior 14
Other (explain in narrative): Other4 (6.9%)
Lost Control3 (5.2%)
Followed too close3 (5.2%)
Ran off road - straight3 (5.2%)
Ran Stop Sign2 (3.4%)
Driver Distraction: Other interior distraction2 (3.4%)
Exceeded authorized speed2 (3.4%)
Driver Distraction: Inattentive/lost in thought2 (3.4%)
Operator inexperience1 (1.7%)

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

Road & Environmental Conditions

Crashes were more likely to occur in clear weather and daylight conditions in 2025 compared to the prior year. The number of daylight crashes increased from 14 to 23, while crashes in dark, unlit conditions decreased from 15 to 10. Similarly, crashes on dry road surfaces increased from 25 to 31, whereas incidents on wet roads fell from 6 to 1.

Weather

Clear31 (86.1%)
↑10.7%prior 28
Cloudy2 (5.6%)
↓-60.0%prior 5
Rain1 (2.8%)
Severe Winds1 (2.8%)
Snow1 (2.8%)

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

Lighting

Daylight23 (62.2%)
↑64.3%prior 14
Dark - roadway not lighted10 (27.0%)
↓-33.3%prior 15
Dawn3 (8.1%)
Dark - roadway lighted1 (2.7%)
↓-80.0%prior 5

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

Road Surface

Dry31 (86.1%)
↑24.0%prior 25
Gravel2 (5.6%)
Ice/frost1 (2.8%)
Snow1 (2.8%)
Wet1 (2.8%)
↓-83.3%prior 6

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

Vehicles & Demographics

Ford and Chevrolet vehicles were the most frequently involved makes in both periods, with their combined count remaining stable year-over-year. An analysis of persons involved in crashes shows a notable shift in age demographics; the number of individuals in the 16-20 age group decreased from 21 in 2024 to 12 in 2025. Conversely, involvement of the 35-44 age group increased from 10 to 14, and the 65+ age group more than doubled from 3 to 7.

Top Vehicle Makes (82 vehicles)

1
FORD15 (18.3%)
↑15.4%prior 13
2
CHEV15 (18.3%)
↓-16.7%prior 18
3
CHEVROLET9 (11%)
↑50.0%prior 6
4
JEEP7 (8.5%)
5
DODG4 (4.9%)
6
HYUN3 (3.7%)
7
TOYO2 (2.4%)
8
TOYT2 (2.4%)
9
KIA2 (2.4%)
10
DODGE2 (2.4%)

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

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

Sex Distribution (36 persons with recorded sex)

Male23 (63.9%)
↑9.5%prior 21
Female13 (36.1%)
↓-7.1%prior 14

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: 58
  • Total persons involved: 83
  • Total vehicles involved: 82

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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