Yearly Traffic Safety Analysis

350 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Winneshiek County, total traffic crashes increased slightly from 343 in the prior year to 350 in the current period, a change of approximately 2%. Despite the rise in total incidents, the number of fatalities saw a significant decrease, falling from 5 to 2. The most notable shift in crash causation was a 34% increase in the count of collisions involving animals, which rose from 119 to 160 incidents year-over-year.

350

▲ 2.0%was 343

Total Crash Events

2

▼ -60.0%was 5

Persons Killed

97

Persons Injured

2

▼ -33.3%was 3

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 · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crashes in Winneshiek County showed a slight upward trend, increasing by 2% from 343 incidents in 2023 to 350 in 2024. However, the outcomes of these crashes improved, with total fatalities decreasing from 5 to 2. The total number of injuries remained unchanged at 97 for both periods.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 4↓-50.0%

1

Cyclists Injured

Prior: 1→0.0%

96

Motorists Injured

Prior: 95↑1.1%

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · 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. Friday was the peak day for crashes in both the current period (61 crashes) and the prior period (58 crashes). Similarly, the 5 p.m. hour was the peak time for collisions in both years, accounting for 31 crashes in the current year and 39 in the prior year. A minor shift occurred in the second-busiest crash day, which moved from Monday (55 crashes) in the prior year to Thursday (55 crashes) in the current year.

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes rose, crash severity outcomes improved year-over-year. The number of fatal crashes decreased from 3 to 2, and the fatal crash rate fell from 0.87 to 0.57. The proportion of crashes resulting in any level of injury was stable, accounting for 22.7% of crashes in the prior year and 22.9% in the current year. The share of crashes involving serious injuries increased slightly from 2.3% (8 crashes) to 3.1% (11 crashes).

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.6%
↓-33.3%prior 3
Serious Injury11serious injury crashes3.1%
↑37.5%prior 8
Minor Injury42minor injury crashes12%
↑16.7%prior 36
Possible Injury27possible injury crashes7.7%
↓-20.6%prior 34
No Injury268no injury crashes76.6%
↑2.3%prior 262

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals were the leading contributing factor in both periods, with the count of such incidents increasing by 34% from 119 in the prior year to 160 in the current year. This factor's share of all crashes grew from 34.7% to 45.7%. Conversely, crashes attributed to a driver losing control decreased from 35 to 24. Incidents where driving too fast for conditions was a factor saw a small increase in count, from 21 to 25.

Officer-Reported Primary Contributing Cause

Animal160 (45.7%)↑34.5%prior 119
Driving too fast for conditions25 (7.1%)↑19.0%prior 21
Lost Control24 (6.9%)↓-31.4%prior 35
Followed too close17 (4.9%)↑30.8%prior 13
Ran off road - left14 (4%)↓-17.6%prior 17
Ran off road - straight12 (3.4%)↓-33.3%prior 18
Other (explain in narrative): Other9 (2.6%)↓-52.6%prior 19
FTYROW: Making left turn7 (2%)
Ran Stop Sign7 (2%)↓-22.2%prior 9
FTYROW: From stop sign7 (2%)↓-50.0%prior 14

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The majority of crashes in both years occurred during daylight hours in clear weather on dry roads. The count of crashes on roads affected by snow, ice, or slush decreased from 39 incidents in the prior period to 27 in the current period. Crashes in daylight conditions decreased from 138 to 123, and those on dry roads fell from 167 to 153, suggesting a reduction in crashes under ideal conditions.

Weather

Clear134 (65.0%)
↓-16.3%prior 160
Cloudy47 (22.8%)
↑11.9%prior 42
Rain6 (2.9%)
Fog, smoke, smog6 (2.9%)
→0.0%prior 6
Snow6 (2.9%)
↓-40.0%prior 10
Other (explain in narrative)3 (1.5%)
Blowing Snow3 (1.5%)
↓-50.0%prior 6
Freezing rain/drizzle1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight123 (57.2%)
↓-10.9%prior 138
Dark - roadway not lighted50 (23.3%)
↓-13.8%prior 58
Dark - roadway lighted18 (8.4%)
↓-21.7%prior 23
Dawn9 (4.2%)
→0.0%prior 9
Dusk8 (3.7%)
↓-38.5%prior 13
Dark - unknown roadway lighting7 (3.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry153 (73.6%)
↓-8.4%prior 167
Wet15 (7.2%)
↓-6.3%prior 16
Snow12 (5.8%)
↓-42.9%prior 21
Gravel12 (5.8%)
↓-29.4%prior 17
Ice/frost10 (4.8%)
↓-37.5%prior 16
Slush5 (2.4%)
Mud, dirt1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

The primary vehicle makes involved in crashes remained consistent year-over-year. Ford and Chevrolet vehicles were the top two most frequently involved makes in both periods, with counts remaining relatively stable. Ford-made vehicles were involved in 101 crashes in the current period compared to 91 in the prior, while Chevrolet vehicles were involved in 109 crashes compared to 112 previously. The number of Jeeps in crashes increased from 21 to 27, while GMC involvement decreased from 31 to 24.

Top Vehicle Makes (455 vehicles)

1
FORD101 (22.2%)
↑11.0%prior 91
2
CHEV80 (17.6%)
↑6.7%prior 75
3
CHEVROLET29 (6.4%)
↓-21.6%prior 37
4
JEEP27 (5.9%)
↑28.6%prior 21
5
GMC24 (5.3%)
↓-22.6%prior 31
6
SUBA15 (3.3%)
↑114.3%prior 7
7
BUIC12 (2.6%)
↓-36.8%prior 19
8
DODG12 (2.6%)
↓-42.9%prior 21
9
TOYO11 (2.4%)
→0.0%prior 11
10
NISS10 (2.2%)
↓-23.1%prior 13

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (188 persons with recorded sex)

Male98 (52.1%)
↓-62.5%prior 261
Female90 (47.9%)
↓-49.2%prior 177

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 350
  • Total persons involved: 474
  • Total vehicles involved: 455

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: 2024." Published September 9, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2024-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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