Yearly Traffic Safety Analysis

655 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Lee County recorded 655 total crashes, a 6.3% decrease from the 699 crashes reported in 2024. The most notable year-over-year change was a 50% reduction in total fatalities, which fell from 4 in the prior period to 2 in the current period. Overall crash injuries also declined by 10.8%.

655

▼ -6.3%was 699

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

149

▼ -10.8%was 167

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

Overall, traffic crashes in Lee County showed a downward trend year-over-year. Total crashes decreased by 6.3%, from 699 in 2024 to 655 in 2025. This decline was also reflected in crash outcomes, with total injuries falling by 10.8% from 167 to 149, and fatalities decreasing by 50% from 4 to 2.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

0

Other Killed

Prior: 0→0.0%

2

Pedestrians Injured

Prior: 2→0.0%

5

Cyclists Injured

Prior: 3↑66.7%

141

Motorists Injured

Prior: 161↓-12.4%

1

Other Injured

Prior: 1→0.0%

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 saw some shifts between the two periods. While the peak hour for crashes remained consistent around 5 p.m. in both 2024 (52 crashes) and 2025 (51 crashes), the peak day changed. In 2024, Friday was the busiest day with 120 crashes, whereas in 2025, Wednesday became the peak day with 114 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

Crash severity improved year-over-year, with fatal crashes decreasing from 4 in 2024 to 2 in 2025, halving their share of total crashes from 0.6% to 0.3%. The proportion of crashes resulting in possible injuries saw a notable decrease from 11.4% to 7.0% of all incidents. Conversely, the share of minor injury crashes increased from 7.3% to 9.2%, and crashes with no injuries rose from 78.1% to 80.9% of the total.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.3%
↓-50.0%prior 4
Serious Injury17serious injury crashes2.6%
↓-5.6%prior 18
Minor Injury60minor injury crashes9.2%
↑17.6%prior 51
Possible Injury46possible injury crashes7%
↓-42.5%prior 80
No Injury530no injury crashes80.9%
↓-2.9%prior 546

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 in both periods, though the count of such incidents decreased from 275 in 2024 to 245 in 2025. The second most cited factor in the prior year, 'Lost Control,' also saw a decrease in count from 40 to 36 incidents. Notably, crashes attributed to 'Failure to Yield Right of Way from a stop sign' increased from 31 to 36, elevating this factor's rank from fourth to a tie for second place in 2025. The count for 'Followed too close' remained relatively stable, decreasing slightly from 33 to 32.

Officer-Reported Primary Contributing Cause

Animal245 (37.4%)↓-10.9%prior 275
FTYROW: From stop sign36 (5.5%)↑16.1%prior 31
Lost Control36 (5.5%)↓-10.0%prior 40
Followed too close32 (4.9%)↓-3.0%prior 33
Ran off road - left31 (4.7%)↑3.3%prior 30
Other (explain in narrative): Other31 (4.7%)↓-6.1%prior 33
Ran Stop Sign26 (4%)→0.0%prior 26
Driver Distraction: Other interior distraction20 (3.1%)↓-20.0%prior 25
Driving too fast for conditions18 (2.7%)↑12.5%prior 16
FTYROW: Making left turn14 (2.1%)↓-12.5%prior 16

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 conditions under which crashes occurred remained broadly similar year-over-year, with the majority happening in clear weather on dry roads. The proportion of crashes occurring during daylight increased from 40.8% of all crashes in 2024 to 44.9% in 2025. Similarly, crashes on dry road surfaces accounted for 52.5% of the total in 2025, up from 48.1% in the previous year. The number of crashes in clear weather was stable, with 320 in 2025 compared to 314 in 2024.

Weather

Clear320 (73.7%)
↑1.9%prior 314
Cloudy65 (15.0%)
↑6.6%prior 61
Snow18 (4.1%)
↑20.0%prior 15
Rain18 (4.1%)
↑12.5%prior 16
Fog, smoke, smog6 (1.4%)
↓-33.3%prior 9
Freezing rain/drizzle3 (0.7%)
↓-72.7%prior 11
Blowing Snow2 (0.5%)
Severe Winds1 (0.2%)
Other (explain in narrative)1 (0.2%)

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

Lighting

Daylight294 (65.6%)
↑3.2%prior 285
Dark - roadway not lighted62 (13.8%)
↓-7.5%prior 67
Dark - roadway lighted52 (11.6%)
↓-3.7%prior 54
Dark - unknown roadway lighting16 (3.6%)
↓-23.8%prior 21
Dusk14 (3.1%)
↑7.7%prior 13
Dawn10 (2.2%)
↓-16.7%prior 12

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

Road Surface

Dry344 (79.3%)
↑2.4%prior 336
Wet37 (8.5%)
↑15.6%prior 32
Snow25 (5.8%)
↑8.7%prior 23
Ice/frost10 (2.3%)
↓-64.3%prior 28
Gravel10 (2.3%)
→0.0%prior 10
Slush6 (1.4%)
↑20.0%prior 5
Other (explain in narrative)2 (0.5%)

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 remained consistent, with Ford (168 vehicles) and Chevrolet (192 vehicles, combining 'CHEV' and 'CHEVROLET') leading in 2025, similar to the prior year. The count of Fords involved decreased from 189, while Chevrolet's combined involvement saw a smaller dip from 200. The age distribution of persons involved in crashes showed little variation, though there was a slight increase in individuals aged 65 and older, from 142 in 2024 to 150 in 2025.

Top Vehicle Makes (945 vehicles)

1
FORD168 (17.8%)
↓-11.1%prior 189
2
CHEV139 (14.7%)
↑10.3%prior 126
3
GMC55 (5.8%)
↑7.8%prior 51
4
CHEVROLET53 (5.6%)
↓-28.4%prior 74
5
DODG51 (5.4%)
↑8.5%prior 47
6
JEEP45 (4.8%)
↓-6.3%prior 48
7
KIA33 (3.5%)
↓-23.3%prior 43
8
CHRY29 (3.1%)
↑16.0%prior 25
9
HOND28 (3%)
→0.0%prior 28
10
NISS26 (2.8%)
↑18.2%prior 22

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

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

Sex Distribution (495 persons with recorded sex)

Male274 (55.4%)
↑3.4%prior 265
Female221 (44.6%)
↑2.3%prior 216

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: 655
  • Total persons involved: 981
  • Total vehicles involved: 945

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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