Yearly Traffic Safety Analysis

1,665 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Story County recorded 1,665 total vehicle crashes, a 12.3% increase from the 1,483 crashes documented in 2024. While the number of fatalities decreased from 4 to 2, total injuries rose by 14.9% from 329 to 378. A notable shift was the 50% increase in crashes involving driving under the influence, which grew from 22 to 33 incidents year-over-year.

1,665

▲ 12.3%was 1,483

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

378

▲ 14.9%was 329

Persons Injured

2

▼ -50.0%was 4

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 data for Story County indicates a rising trend in collisions year-over-year. Total crashes increased by 12.3%, from 1,483 in 2024 to 1,665 in 2025. This increase was accompanied by a 14.9% rise in total injuries, although fatalities decreased by 50% from 4 to 2.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 4↓-75.0%

0

Other Killed

Prior: 0→0.0%

24

Pedestrians Injured

Prior: 17↑41.2%

19

Cyclists Injured

Prior: 11↑72.7%

331

Motorists Injured

Prior: 296↑11.8%

4

Other Injured

Prior: 5↓-20.0%

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 year-over-year. The 5 PM hour was the peak time for crashes in both 2025 (155 crashes) and 2024 (143 crashes). The peak day for crashes shifted from Friday in 2024 (278 crashes) to Thursday in 2025 (280 crashes), with late-weekdays consistently showing the highest volumes. Crash occurrences were most frequent in the final quarter of the year for both periods.

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 total crashes increased, the severity of those crashes shifted toward less severe outcomes. Fatal crashes decreased from 4 in 2024 to 2 in 2025, and serious injury crashes fell from 25 to 19. Conversely, crashes resulting in minor injuries rose from 97 to 116, and possible injury crashes increased from 168 to 194. The proportion of non-injury crashes remained stable at approximately 80% of all incidents in both years.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.1%
↓-50.0%prior 4
Serious Injury19serious injury crashes1.1%
↓-24.0%prior 25
Minor Injury116minor injury crashes7%
↑19.6%prior 97
Possible Injury194possible injury crashes11.7%
↑15.5%prior 168
No Injury1,334no injury crashes80.1%
↑12.2%prior 1,189

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 ranking of top contributing factors shifted between 2024 and 2025. 'Followed too close' became the leading cause in 2025 with 230 incidents, an increase of 38 crashes from the prior year's 192. Crashes involving animals, the top factor in 2024 with 231 incidents, decreased to 207 incidents. Notably, crashes attributed to 'Driving too fast for conditions' increased by 38 incidents, from 101 to 139, while 'Failure to yield on a left turn' decreased from 106 to 88 incidents.

Officer-Reported Primary Contributing Cause

Followed too close230 (13.8%)↑19.8%prior 192
Animal207 (12.4%)↓-10.4%prior 231
Driving too fast for conditions139 (8.3%)↑37.6%prior 101
FTYROW: Making left turn88 (5.3%)↓-17.0%prior 106
Other (explain in narrative): Other77 (4.6%)↓-4.9%prior 81
Ran off road - left73 (4.4%)↑10.6%prior 66
Improper or erratic lane changing72 (4.3%)↑60.0%prior 45
FTYROW: From stop sign68 (4.1%)↓-16.0%prior 81
Ran Traffic Signal65 (3.9%)↑14.0%prior 57
Driver Distraction: Other interior distraction54 (3.2%)↑50.0%prior 36

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 majority of crashes in both periods occurred in clear weather and on dry roads. In 2025, 62.8% of crashes happened in clear weather and 66.5% on dry surfaces, proportions similar to 2024's figures of 62.4% and 64.1%, respectively. However, there was a notable increase in the number of crashes occurring on snowy roads, rising from 85 in 2024 to 132 in 2025. Crashes in daylight conditions constituted a slightly larger share of the total in 2025 (65.6%) compared to the prior year (61.8%).

Weather

Clear1,046 (70.5%)
↑13.1%prior 925
Cloudy253 (17.1%)
↑46.2%prior 173
Snow80 (5.4%)
↑77.8%prior 45
Rain59 (4.0%)
↓-30.6%prior 85
Blowing Snow18 (1.2%)
↑80.0%prior 10
Freezing rain/drizzle10 (0.7%)
↓-9.1%prior 11
Fog, smoke, smog9 (0.6%)
↓-30.8%prior 13
Severe Winds6 (0.4%)
Other (explain in narrative)1 (0.1%)
Sleet, hail1 (0.1%)

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

Lighting

Daylight1,092 (73.0%)
↑19.1%prior 917
Dark - roadway lighted208 (13.9%)
↑7.8%prior 193
Dark - roadway not lighted116 (7.8%)
↑20.8%prior 96
Dawn31 (2.1%)
↑6.9%prior 29
Dusk30 (2.0%)
↑3.4%prior 29
Dark - unknown roadway lighting18 (1.2%)
↑80.0%prior 10

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

Road Surface

Dry1,108 (74.5%)
↑16.6%prior 950
Wet134 (9.0%)
↓-9.5%prior 148
Snow132 (8.9%)
↑55.3%prior 85
Ice/frost70 (4.7%)
↑34.6%prior 52
Gravel20 (1.3%)
→0.0%prior 20
Slush19 (1.3%)
↑35.7%prior 14
Other (explain in narrative)4 (0.3%)
Mud, dirt1 (0.1%)

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

Vehicles & Demographics

The demographics of persons involved in crashes and the top vehicle makes remained relatively stable year-over-year. Individuals in the 16-25 age group continued to be the most frequently involved demographic in both periods. The top three vehicle makes involved in crashes were consistent: Ford (450 incidents in 2025 vs. 385 in 2024), Chevrolet (381 vs. 347), and Toyota (271 vs. 215). While the top makes saw an increase in crash counts, their rank order was largely unchanged.

Top Vehicle Makes (2,994 vehicles)

1
FORD450 (15%)
↑16.9%prior 385
2
CHEV381 (12.7%)
↑9.8%prior 347
3
TOYT271 (9.1%)
↑26.0%prior 215
4
HOND195 (6.5%)
↑23.4%prior 158
5
NISS143 (4.8%)
↑25.4%prior 114
6
JEEP125 (4.2%)
↓-3.8%prior 130
7
CHEVROLET108 (3.6%)
↓-7.7%prior 117
8
GMC83 (2.8%)
↑7.8%prior 77
9
DODG83 (2.8%)
↑25.8%prior 66
10
KIA79 (2.6%)
↑49.1%prior 53

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

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

Sex Distribution (2,185 persons with recorded sex)

Male1,246 (57.0%)
↑22.4%prior 1,018
Female939 (43.0%)
↑13.8%prior 825

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: 1,665
  • Total persons involved: 3,090
  • Total vehicles involved: 2,994

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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