Yearly Traffic Safety Analysis

181 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Wright County, total crashes remained stable, decreasing by just 1.1% from 183 in 2023 to 181 in 2024. Despite the consistent crash volume, the severity of outcomes saw a marked improvement. The number of people injured fell by 24.6% from 57 to 43, and fatalities dropped from three to one.

181

▼ -1.1%was 183

Total Crash Events

1

▼ -66.7%was 3

Persons Killed

43

▼ -24.6%was 57

Persons Injured

1

▼ -50.0%was 2

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

Trend Summary

Overall crash trends in Wright County show a stable volume of incidents year-over-year, with a minor decrease of two crashes from 183 to 181. However, there was a significant downward trend in the severity of these incidents. Total injuries declined by 24.6% and fatalities decreased from three to one, indicating a shift towards less severe collisions.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 3↓-66.7%

1

Cyclists Injured

Prior: 1→0.0%

42

Motorists Injured

Prior: 55↓-23.6%

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 timing of crashes shifted between the two periods. In 2024, Friday was the peak day for crashes with 35 incidents, a change from Wednesday (31 crashes) in the prior year. The peak hour also moved later in the day, from 3 p.m. in 2023 (19 crashes) to 6 p.m. in 2024 (18 crashes).

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

The severity of crashes decreased notably in 2024 compared to the previous year, with the proportion of crashes resulting in no injuries rising from 74.3% to 82.9%. Correspondingly, crashes involving serious injuries dropped significantly, from 13 incidents (7.1% of all crashes) in 2023 to 5 incidents (2.8% of all crashes) in 2024. The number of fatal crashes also decreased from two to one.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.6%
↓-50.0%prior 2
Serious Injury5serious injury crashes2.8%
↓-61.5%prior 13
Minor Injury10minor injury crashes5.5%
↓-47.4%prior 19
Possible Injury15possible injury crashes8.3%
↑15.4%prior 13
No Injury150no injury crashes82.9%
↑10.3%prior 136

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 remained the leading contributing factor in both years, with a nearly identical count of 40 in 2024 versus 41 in 2023. While the top two factors were stable, there were notable shifts in other categories. The count of crashes attributed to 'Lost Control' was more than halved, dropping from 13 to 6. Conversely, crashes involving 'Ran Stop Sign' increased from one to eight incidents, and those from 'Failure to Yield from a Stop Sign' more than doubled from four to nine.

Officer-Reported Primary Contributing Cause

Animal40 (22.1%)↓-2.4%prior 41
Ran off road - left14 (7.7%)→0.0%prior 14
Other (explain in narrative): Other10 (5.5%)↓-16.7%prior 12
FTYROW: From stop sign9 (5%)
Driving too fast for conditions8 (4.4%)↓-20.0%prior 10
Ran Stop Sign8 (4.4%)
Improper Backing8 (4.4%)↑60.0%prior 5
Made improper turn6 (3.3%)↑20.0%prior 5
Lost Control6 (3.3%)↓-53.8%prior 13
Driver Distraction: Other interior distraction6 (3.3%)→0.0%prior 6

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

Road & Environmental Conditions

There was a notable shift in crash conditions year-over-year, trending towards incidents in clearer weather on drier roads. Crashes on roads with ice or frost saw a major decrease, falling from 17 incidents in 2023 to 7 in 2024. Similarly, collisions in daylight conditions increased from 91 to 103, while those on dark, unlighted roadways decreased from 29 to 21.

Weather

Clear109 (77.9%)
↑5.8%prior 103
Cloudy12 (8.6%)
↓-47.8%prior 23
Rain7 (5.0%)
Blowing Snow4 (2.9%)
Snow4 (2.9%)
↓-50.0%prior 8
Freezing rain/drizzle2 (1.4%)
↓-60.0%prior 5
Fog, smoke, smog1 (0.7%)
Severe Winds1 (0.7%)

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

Lighting

Daylight103 (71.0%)
↑13.2%prior 91
Dark - roadway not lighted21 (14.5%)
↓-27.6%prior 29
Dark - roadway lighted16 (11.0%)
↑23.1%prior 13
Dusk4 (2.8%)
↓-50.0%prior 8
Dark - unknown roadway lighting1 (0.7%)

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

Road Surface

Dry108 (76.1%)
↑3.8%prior 104
Wet11 (7.7%)
↑83.3%prior 6
Snow8 (5.6%)
↑60.0%prior 5
Ice/frost7 (4.9%)
↓-58.8%prior 17
Gravel4 (2.8%)
↓-50.0%prior 8
Mud, dirt2 (1.4%)
Slush2 (1.4%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Chevrolet and Ford leading in both periods, although the count of Chevrolet vehicles involved decreased from 72 to 61 while Ford's count was stable at 45. Analysis of persons involved in crashes reveals a significant demographic shift. The proportion of individuals aged 65 and older more than doubled, rising from 7.8% of persons involved in 2023 to 16.7% in 2024.

Top Vehicle Makes (293 vehicles)

1
FORD45 (15.4%)
↑4.7%prior 43
2
CHEV36 (12.3%)
↓-36.8%prior 57
3
CHEVROLET25 (8.5%)
↑66.7%prior 15
4
GMC17 (5.8%)
↑142.9%prior 7
5
DODG16 (5.5%)
↑45.5%prior 11
6
TOYT14 (4.8%)
↑100.0%prior 7
7
BUIC11 (3.8%)
↑120.0%prior 5
8
JEEP9 (3.1%)
↑28.6%prior 7
9
HOND8 (2.7%)
↑60.0%prior 5
10
NISSAN8 (2.7%)

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

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

Sex Distribution (166 persons with recorded sex)

Male104 (62.7%)
↓-33.3%prior 156
Female62 (37.3%)
↓-23.5%prior 81

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: 181
  • Total persons involved: 304
  • Total vehicles involved: 293

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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