Yearly Traffic Safety Analysis

370 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In Bremer County, total crashes remained nearly stable, increasing from 368 in 2024 to 370 in 2025. Despite the minimal change in overall crash volume, the severity of incidents worsened, with fatalities rising from zero in the prior period to two in the current period. Total injuries also saw a significant increase of 33.3%, rising from 72 to 96 year-over-year.

370

▲ 0.5%was 368

Total Crash Events

2

Persons Killed

96

▲ 33.3%was 72

Persons Injured

2

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

Year-over-year, the total number of crashes in Bremer County remained relatively stable, with a slight increase of 0.5% from 368 in 2024 to 370 in 2025. However, the severity of these crashes worsened, as total injuries increased by 33.3% from 72 to 96, and the county recorded two fatalities compared to none in the previous year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 0→%

2

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

93

Motorists Injured

Prior: 72↑29.2%

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 showed some shifts between the two periods. The peak day for crashes moved from Friday (67 incidents) in 2024 to Saturday (58 incidents) in 2025. The peak hour for collisions also shifted slightly earlier, from 5 p.m. in the prior period (34 crashes) to 4 p.m. in the current period (32 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 increased in 2025 compared to the previous year. The county recorded two fatal crashes, resulting in two deaths, whereas there were no fatal crashes in 2024. The proportion of crashes involving any level of injury rose from 14.2% of all incidents in 2024 to 19.2% in 2025. This was driven by increases in both serious injury crashes (from 6 to 10) and minor injury crashes (from 23 to 35).

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.5%
Serious Injury10serious injury crashes2.7%
↑66.7%prior 6
Minor Injury35minor injury crashes9.5%
↑52.2%prior 23
Possible Injury26possible injury crashes7%
↑13.0%prior 23
No Injury297no injury crashes80.3%
↓-6.0%prior 316

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 top contributing factor in both periods, though the count decreased by 5.1% from 177 in 2024 to 168 in 2025. Crashes attributed to 'Followed too close' increased by 50%, rising from 20 to 30 incidents and maintaining its rank as the second-leading cause. Incidents where a driver 'Ran off road - left' also increased from 14 to 18, while crashes from 'Driving too fast for conditions' decreased from 19 to 12.

Officer-Reported Primary Contributing Cause

Animal168 (45.4%)↓-5.1%prior 177
Followed too close30 (8.1%)↑50.0%prior 20
Ran off road - left18 (4.9%)↑28.6%prior 14
FTYROW: Making left turn17 (4.6%)↓-5.6%prior 18
FTYROW: From stop sign13 (3.5%)↑18.2%prior 11
Driving too fast for conditions12 (3.2%)↓-36.8%prior 19
Lost Control12 (3.2%)↑50.0%prior 8
Ran Stop Sign11 (3%)↑37.5%prior 8
Other (explain in narrative): Other9 (2.4%)↓-40.0%prior 15
Ran off road - straight9 (2.4%)

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 distribution of crashes across most weather and road surface conditions remained similar year-over-year, with the majority of incidents in both periods occurring on dry roads under clear skies. There was, however, a significant shift in lighting conditions. While daylight crashes were stable (158 vs. 156), incidents occurring in the dark on unlighted roadways increased by 58.3%, from 24 in 2024 to 38 in 2025.

Weather

Clear156 (71.6%)
↑8.3%prior 144
Cloudy44 (20.2%)
↑33.3%prior 33
Snow8 (3.7%)
↓-27.3%prior 11
Blowing Snow4 (1.8%)
Rain3 (1.4%)
↓-50.0%prior 6
Freezing rain/drizzle1 (0.5%)
Severe Winds1 (0.5%)
Fog, smoke, smog1 (0.5%)

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

Lighting

Daylight158 (71.8%)
↑1.3%prior 156
Dark - roadway not lighted38 (17.3%)
↑58.3%prior 24
Dark - roadway lighted12 (5.5%)
↓-14.3%prior 14
Dawn7 (3.2%)
↑16.7%prior 6
Dusk4 (1.8%)
↓-20.0%prior 5
Dark - unknown roadway lighting1 (0.5%)

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

Road Surface

Dry171 (78.1%)
↑15.5%prior 148
Snow16 (7.3%)
↑23.1%prior 13
Ice/frost12 (5.5%)
↑33.3%prior 9
Gravel9 (4.1%)
↓-30.8%prior 13
Wet9 (4.1%)
↓-40.0%prior 15
Slush2 (0.9%)
↓-60.0%prior 5

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford and Chevrolet vehicles leading in both years. The number of Ford vehicles involved increased from 87 to 105, and Chevrolet vehicles rose from 102 to 121. An analysis of persons involved in crashes reveals notable demographic shifts, as involvement of individuals aged 21-25 grew by 34.7% (from 49 to 66) and those aged 45-54 increased by 50.9% (from 57 to 86).

Top Vehicle Makes (521 vehicles)

1
FORD105 (20.2%)
↑20.7%prior 87
2
CHEV98 (18.8%)
↑22.5%prior 80
3
TOYT41 (7.9%)
↓-4.7%prior 43
4
JEEP27 (5.2%)
↑35.0%prior 20
5
CHEVROLET23 (4.4%)
↑4.5%prior 22
6
NISS20 (3.8%)
↓-20.0%prior 25
7
DODG19 (3.6%)
↓-20.8%prior 24
8
RAM18 (3.5%)
↑38.5%prior 13
9
CHRY16 (3.1%)
↓-20.0%prior 20
10
GMC14 (2.7%)
↓-17.6%prior 17

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

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

Sex Distribution (256 persons with recorded sex)

Male146 (57.0%)
↑3.5%prior 141
Female110 (43.0%)
↓-5.2%prior 116

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: 370
  • Total persons involved: 549
  • Total vehicles involved: 521

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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