Yearly Traffic Safety Analysis

78 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Shelby County recorded 78 total crashes, a 20.4% decrease from the 98 crashes reported in 2024. The most significant year-over-year change was the reduction in crash fatalities, which dropped from two in the prior period to zero in the current period. Total injuries also decreased from 31 to 23.

78

▼ -20.4%was 98

Total Crash Events

0

▼ -100.0%was 2

Persons Killed

23

▼ -25.8%was 31

Persons Injured

0

▼ -100.0%was 2

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

Overall, Shelby County experienced a downward trend in traffic crashes and their severity between 2024 and 2025. The total number of crashes fell by 20.4%, from 98 to 78. Notably, there were no fatalities in 2025, compared to two in the previous year, and the number of injuries decreased by 25.8% from 31 to 23.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 2↓-100.0%

23

Motorists Injured

Prior: 29↓-20.7%

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 in Shelby County shifted between the two periods. The peak day for collisions moved from Friday (20 crashes) in 2024 to Saturday (16 crashes) in 2025. The peak hour for crashes also shifted later in the afternoon, from 2 p.m. (9 crashes) in the prior year to 3 p.m. (13 crashes) in the current year. The month with the most crashes changed from March (14 crashes) in 2024 to November (19 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

Crash severity improved significantly in 2025 compared to 2024. There were no fatal crashes in the current period, down from two fatal crashes (2.0% of all crashes) in the prior year. The number of serious injury crashes also decreased, falling from six (6.1% of total) to two (2.6% of total). While the number of crashes involving minor or possible injuries remained constant at 17, their share of total crashes rose from 17.3% to 21.8% due to the overall reduction in total incidents.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes2.6%
↓-66.7%prior 6
Minor Injury11minor injury crashes14.1%
→0.0%prior 11
Possible Injury6possible injury crashes7.7%
→0.0%prior 6
No Injury59no injury crashes75.6%
↓-19.2%prior 73

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 an animal remained the leading contributing factor in both periods, with 10 crashes recorded each year. There were notable shifts in other factors; crashes attributed to 'Ran Stop Sign' doubled from 3 to 6, and 'Operating vehicle in a reckless, erratic, careless, negligent manner' increased from 1 to 6 incidents. Conversely, crashes due to 'Followed too close' decreased significantly from 7 in 2024 to 1 in 2025. 'FTYROW: From stop sign' also increased in count from 6 to 8, becoming the second-most common factor in the current period.

Officer-Reported Primary Contributing Cause

Animal10 (12.8%)→0.0%prior 10
FTYROW: From stop sign8 (10.3%)↑33.3%prior 6
Lost Control6 (7.7%)↓-25.0%prior 8
Ran Stop Sign6 (7.7%)
Operating vehicle in an reckless, erratic, careless, negligent manner6 (7.7%)
Made improper turn5 (6.4%)
Ran off road - straight4 (5.1%)↓-20.0%prior 5
Driving too fast for conditions3 (3.8%)↓-50.0%prior 6
FTYROW: Making left turn3 (3.8%)↓-40.0%prior 5
FTYROW: Other (explain in narrative)2 (2.6%)

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 in 2025 were more likely to occur in clear and dry conditions compared to 2024. In the current period, 76.9% of crashes happened on dry roads, up from 66.3% in the prior period. Correspondingly, the share of crashes on adverse surfaces like snow, ice, or wet roads fell from 19.4% to 10.3%. A similar trend was observed for weather, with clear-weather crashes accounting for 73.1% of the total in 2025 versus 68.4% in 2024. The proportion of crashes occurring in daylight also increased from 61.2% to 70.5%.

Weather

Clear57 (81.4%)
↓-14.9%prior 67
Cloudy9 (12.9%)
↑12.5%prior 8
Snow2 (2.9%)
Freezing rain/drizzle1 (1.4%)
Severe Winds1 (1.4%)

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

Lighting

Daylight55 (77.5%)
↓-8.3%prior 60
Dark - roadway not lighted9 (12.7%)
↓-43.8%prior 16
Dark - roadway lighted3 (4.2%)
↓-50.0%prior 6
Dusk2 (2.8%)
Dawn1 (1.4%)
Dark - unknown roadway lighting1 (1.4%)

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

Road Surface

Dry60 (85.7%)
↓-7.7%prior 65
Snow5 (7.1%)
Gravel2 (2.9%)
Ice/frost2 (2.9%)
Wet1 (1.4%)
↓-87.5%prior 8

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 saw a notable shift. Ford became the most common make in 2025 with 19 vehicles, despite its count decreasing from 28 in the prior year. The number of Chevrolet vehicles (listed as 'CHEV' and 'CHEVROLET') decreased substantially from a combined 49 in 2024 to 18 in 2025. Regarding person demographics, the 65+ age group remained one of the most represented groups in both years, with 25 individuals involved in 2025 compared to 29 in 2024. The 45-54 age group's involvement was nearly halved, dropping from 29 persons in 2024 to 15 in 2025.

Top Vehicle Makes (131 vehicles)

1
FORD19 (14.5%)
↓-32.1%prior 28
2
CHEV16 (12.2%)
↓-51.5%prior 33
3
JEEP8 (6.1%)
↑60.0%prior 5
4
GMC8 (6.1%)
↓-11.1%prior 9
5
DODG7 (5.3%)
↑40.0%prior 5
6
TOYT6 (4.6%)
7
HOND5 (3.8%)
8
HYUN4 (3.1%)
9
CHRY4 (3.1%)
10
TOYO4 (3.1%)

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 (89 persons with recorded sex)

Male53 (59.6%)
↓-14.5%prior 62
Female36 (40.4%)
↓-7.7%prior 39

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: 78
  • Total persons involved: 132
  • Total vehicles involved: 131

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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