Yearly Traffic Safety Analysis

50 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Taylor County, total vehicle crashes decreased by 20.6%, from 63 incidents in 2023 to 50 in 2024. The most significant year-over-year change was the elimination of fatal crashes, which dropped from one to zero. Despite the overall reduction in collisions, the total number of people injured increased from 15 to 20.

50

▼ -20.6%was 63

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

20

▲ 33.3%was 15

Persons Injured

0

▼ -100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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

The overall trend in Taylor County shows a decline in the total number of crashes, which fell by 20.6% from 63 in 2023 to 50 in 2024. While fatal incidents were eliminated, the number of persons injured increased by 33.3% year-over-year, rising from 15 to 20.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 1↓-100.0%

1

Pedestrians Injured

Prior: 0→%

2

Cyclists Injured

Prior: 0→%

17

Motorists Injured

Prior: 15↑13.3%

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. The peak day for collisions moved from Thursday in 2023 to Monday in 2024, though the peak volume on those days was identical at 12 crashes. The most active time for crashes changed from the 3 p.m. hour in the prior year (9 crashes) to a dual peak at 5 a.m. and 8 p.m. in the current year (5 crashes each).

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

Crash severity outcomes showed a mixed trend. Fatal crashes were eliminated, dropping from one in 2023 to zero in 2024. However, the proportion of all crashes that resulted in an injury increased from 22.3% to 36.0% year-over-year. This was driven by a threefold increase in minor injury crashes, which rose from 3 to 9 incidents.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes4%
↓-33.3%prior 3
Minor Injury9minor injury crashes18%
↑200.0%prior 3
Possible Injury7possible injury crashes14%
↓-12.5%prior 8
No Injury32no injury crashes64%
↓-33.3%prior 48

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 involving an animal remained the top contributing factor in both periods, although the count of these incidents decreased by 39.1% from 23 in 2023 to 14 in 2024. "Lost Control" became the second-most cited factor in the current period, with its count increasing from 3 to 4 crashes. Notably, crashes attributed to a driver "Operating vehicle in an reckless, erratic, careless, negligent manner" decreased from 5 incidents to 1.

Officer-Reported Primary Contributing Cause

Animal14 (28%)↓-39.1%prior 23
Lost Control4 (8%)
FTYROW: From stop sign3 (6%)
FTYROW: Making left turn2 (4%)
Cargo/equipment loss or shift2 (4%)
Driving too fast for conditions2 (4%)
FTYROW: From yield sign2 (4%)
FTYROW: Other (explain in narrative)2 (4%)
Illegally Parked/Unattended2 (4%)
Other (explain in narrative): Other2 (4%)

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 the conditions under which crashes occurred. The proportion of collisions taking place in darkness on unlit roadways increased from 9.5% of all crashes (6 incidents) in 2023 to 30% (15 incidents) in 2024. While the share of crashes in clear weather was stable, incidents on adverse road surfaces like wet, snow, or ice more than doubled, rising from 4 in the prior year to 11 in the current year.

Weather

Clear28 (71.8%)
↓-20.0%prior 35
Cloudy5 (12.8%)
Rain3 (7.7%)
Freezing rain/drizzle1 (2.6%)
Blowing Snow1 (2.6%)
Snow1 (2.6%)

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

Lighting

Dark - roadway not lighted15 (38.5%)
↑150.0%prior 6
Daylight14 (35.9%)
↓-51.7%prior 29
Dark - roadway lighted5 (12.8%)
→0.0%prior 5
Dawn3 (7.7%)
Dusk2 (5.1%)

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

Road Surface

Dry25 (64.1%)
↓-32.4%prior 37
Wet6 (15.4%)
Snow3 (7.7%)
Gravel2 (5.1%)
Ice/frost2 (5.1%)
Mud, dirt1 (2.6%)

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

Vehicles & Demographics

Chevrolet and Ford vehicles were the most frequently involved in crashes in both periods. The number of Chevrolet vehicles (combining 'CHEV' and 'CHEVROLET' records) increased from 22 to 24, while Ford vehicles decreased from 17 to 13. A significant demographic shift occurred among persons involved in collisions, as the 16-20 age group became the most represented, with their count increasing from 17 to 21 individuals. Conversely, involvement for the 26-34 age group fell from 20 to 9.

Top Vehicle Makes (67 vehicles)

1
CHEV18 (26.9%)
↑5.9%prior 17
2
FORD13 (19.4%)
↓-23.5%prior 17
3
CHEVROLET6 (9%)
↑20.0%prior 5
4
JEEP4 (6%)
5
GMC3 (4.5%)
6
DODG3 (4.5%)
↓-57.1%prior 7
7
SUBA2 (3%)
8
TOYOTA2 (3%)
9
NISSAN2 (3%)
10
DODGE2 (3%)

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

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

Sex Distribution (35 persons with recorded sex)

Male21 (60.0%)
↓-58.0%prior 50
Female14 (40.0%)
↓-51.7%prior 29

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: 50
  • Total persons involved: 74
  • Total vehicles involved: 67

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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