Yearly Traffic Safety Analysis

201 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Louisa County, total vehicle crashes decreased by 9.5% from 222 in the prior period to 201 in the current period. This overall reduction in crashes was accompanied by a decrease in total injuries from 53 to 43. The most notable year-over-year shift was the registration of one fatal crash in the current period, whereas none were recorded in the previous year.

201

▼ -9.5%was 222

Total Crash Events

1

Persons Killed

43

▼ -18.9%was 53

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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

The overall trend in Louisa County shows a decrease in traffic collisions and resulting injuries year-over-year. Total crashes fell by 21 incidents from 222 to 201, and the number of people injured declined from 53 to 43. However, this downward trend in volume was contrasted by the occurrence of one fatality in the current period, compared to zero in the prior period.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 0→%

43

Motorists Injured

Prior: 52↓-17.3%

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 peak day for crashes shifted from Thursday (44 crashes) in the prior period to Friday (40 crashes) in the current period. The peak hour for collisions, however, remained consistent at 5 a.m. for both years. Despite the hour being the same, the number of crashes during this peak time decreased from 28 to 19.

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 saw a mixed change year-over-year. The current period recorded one fatal crash, accounting for 0.5% of all incidents, an increase from zero fatal crashes in the prior period. Conversely, the proportion of crashes resulting in serious injuries decreased, with serious injury crashes falling from 3 in the prior year to 1 in the current year.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.5%
Serious Injury1serious injury crashes0.5%
↓-66.7%prior 3
Minor Injury17minor injury crashes8.5%
↑13.3%prior 15
Possible Injury14possible injury crashes7%
↓-33.3%prior 21
No Injury168no injury crashes83.6%
↓-8.2%prior 183

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 decreased from 127 to 122. This represented a 3.9% drop in count but an increase in share from 57.2% to 60.7% of all crashes. "Lost Control" held its rank as the second most common factor, with its incident count falling from 15 to 11. Crashes attributed to "Ran off road - left" saw a significant reduction, dropping from 14 incidents to 5.

Officer-Reported Primary Contributing Cause

Animal122 (60.7%)↓-3.9%prior 127
Lost Control11 (5.5%)↓-26.7%prior 15
Exceeded authorized speed5 (2.5%)
FTYROW: From driveway5 (2.5%)
Other (explain in narrative): Other5 (2.5%)↓-37.5%prior 8
FTYROW: From stop sign5 (2.5%)→0.0%prior 5
Operating vehicle in an reckless, erratic, careless, negligent manner5 (2.5%)
Driving too fast for conditions5 (2.5%)
Ran off road - left5 (2.5%)↓-64.3%prior 14
Ran off road - straight4 (2%)↓-33.3%prior 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 were more likely to occur under favorable conditions in the current period compared to the prior year. The proportion of crashes in clear weather rose from 71.7% to 78.8%, while the share of incidents on dry road surfaces increased from 74.8% to 84.2%. Correspondingly, crashes occurring in darkness on unlit roadways decreased as a percentage of total lighting-condition crashes, from 35.1% to 26.0%.

Weather

Clear78 (78.8%)
↑2.6%prior 76
Cloudy14 (14.1%)
→0.0%prior 14
Snow4 (4.0%)
↓-20.0%prior 5
Rain3 (3.0%)
↓-50.0%prior 6

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

Lighting

Daylight57 (54.8%)
↑5.6%prior 54
Dark - roadway not lighted27 (26.0%)
↓-30.8%prior 39
Dark - roadway lighted7 (6.7%)
↑16.7%prior 6
Dark - unknown roadway lighting7 (6.7%)
Dusk3 (2.9%)
↓-40.0%prior 5
Dawn3 (2.9%)

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

Road Surface

Dry85 (84.2%)
↑2.4%prior 83
Snow6 (5.9%)
↓-14.3%prior 7
Wet5 (5.0%)
↓-58.3%prior 12
Gravel4 (4.0%)
Ice/frost1 (1.0%)
↓-80.0%prior 5

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

Vehicles & Demographics

The top makes of vehicles involved in crashes shifted, with Ford (42 vehicles) becoming the most frequent, up from a tie with Chevrolet at 37 vehicles each in the prior year. In the current period, Chevrolet-involved crashes dropped to 31. An analysis of persons involved in crashes shows a notable increase in the 65+ age group, which grew from representing 10.4% of persons to 13.4% year-over-year.

Top Vehicle Makes (245 vehicles)

1
FORD42 (17.1%)
↑13.5%prior 37
2
CHEV31 (12.7%)
↓-16.2%prior 37
3
DODG22 (9%)
↑57.1%prior 14
4
TOYT16 (6.5%)
↑60.0%prior 10
5
CHRY12 (4.9%)
↑50.0%prior 8
6
GMC10 (4.1%)
→0.0%prior 10
7
TOYO9 (3.7%)
↑80.0%prior 5
8
HOND8 (3.3%)
↓-42.9%prior 14
9
HYUN7 (2.9%)
→0.0%prior 7
10
KIA7 (2.9%)
↓-30.0%prior 10

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 (100 persons with recorded sex)

Male68 (68.0%)
→0.0%prior 68
Female32 (32.0%)
↑3.2%prior 31

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: 201
  • Total persons involved: 257
  • Total vehicles involved: 245

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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