Yearly Traffic Safety Analysis

236 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Crawford County, total crashes increased from 204 in 2023 to 236 in 2024, a 15.7% rise. While overall crash volume and injuries (91 vs. 81) increased, the number of fatalities decreased from 3 to 2. The most notable year-over-year shift was a 150% increase in crashes resulting in serious injuries, which grew from 4 to 10 incidents.

236

▲ 15.7%was 204

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

91

▲ 12.3%was 81

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

The overall trend shows an increase in traffic crashes year-over-year. Total crashes rose by 15.7% from 204 to 236, and the number of people injured increased by 12.3% from 81 to 91. However, this period saw a decrease in fatalities, with 2 recorded in 2024 compared to 3 in the prior year.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

3

Cyclists Injured

Prior: 0→%

88

Motorists Injured

Prior: 80↑10.0%

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 for crashes remained largely consistent between the two periods. Friday was the peak day for crashes in both 2024 (46 crashes) and 2023 (32 crashes), and the 3 p.m. hour was the peak hour in both years, with 26 crashes in 2024 and 20 in 2023. A notable shift occurred on Thursdays, which saw a substantial increase in crashes from 23 to 40, becoming the second-busiest day in 2024.

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 shifted year-over-year, with a notable increase in more severe non-fatal outcomes. While fatal crashes decreased from 3 to 2, crashes resulting in serious injury increased from 4 to 10. Consequently, the share of crashes classified as 'Serious Injury' rose from 2.0% of all crashes in 2023 to 4.2% in 2024.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.8%
↓-33.3%prior 3
Serious Injury10serious injury crashes4.2%
↑150.0%prior 4
Minor Injury24minor injury crashes10.2%
↑14.3%prior 21
Possible Injury37possible injury crashes15.7%
→0.0%prior 37
No Injury163no injury crashes69.1%
↑17.3%prior 139

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 top contributing factor in both periods, increasing in count from 28 in 2023 to 33 in 2024. The most significant change was in 'Lost Control' incidents, which tripled from 5 crashes in 2023 to 15 in 2024, representing a 200% increase in count. Conversely, crashes attributed to 'Failure to Yield from a Stop Sign' saw a slight decrease from 13 to 11 incidents.

Officer-Reported Primary Contributing Cause

Animal33 (14%)↑17.9%prior 28
Other (explain in narrative): Other21 (8.9%)↑10.5%prior 19
Lost Control15 (6.4%)↑200.0%prior 5
Driver Distraction: Other interior distraction13 (5.5%)↑62.5%prior 8
Ran off road - left13 (5.5%)↓-35.0%prior 20
Followed too close11 (4.7%)↓-8.3%prior 12
FTYROW: From stop sign11 (4.7%)↓-15.4%prior 13
Improper Backing11 (4.7%)↑83.3%prior 6
Ran off road - straight11 (4.7%)↑57.1%prior 7
Ran Stop Sign9 (3.8%)→0.0%prior 9

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

Road & Environmental Conditions

In both years, most crashes occurred in clear weather on dry roads during daylight hours. However, there was a marked increase in crashes under adverse conditions in 2024. Crashes occurring in snow increased from 4 to 13, and incidents on snow-covered roads rose from 9 to 21. Similarly, crashes on gravel roads more than doubled, from 5 in 2023 to 11 in 2024.

Weather

Clear152 (73.8%)
↑5.6%prior 144
Cloudy30 (14.6%)
↑15.4%prior 26
Snow13 (6.3%)
Freezing rain/drizzle4 (1.9%)
Rain2 (1.0%)
Fog, smoke, smog2 (1.0%)
Blowing Snow2 (1.0%)
Other (explain in narrative)1 (0.5%)

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

Lighting

Daylight142 (67.9%)
↑12.7%prior 126
Dark - roadway lighted28 (13.4%)
↑40.0%prior 20
Dark - roadway not lighted24 (11.5%)
↓-29.4%prior 34
Dusk7 (3.3%)
↓-30.0%prior 10
Dark - unknown roadway lighting4 (1.9%)
Dawn4 (1.9%)

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

Road Surface

Dry156 (75.7%)
↑6.1%prior 147
Snow21 (10.2%)
↑133.3%prior 9
Gravel11 (5.3%)
↑120.0%prior 5
Ice/frost10 (4.9%)
↓-37.5%prior 16
Wet7 (3.4%)
↓-41.7%prior 12
Slush1 (0.5%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes saw a shift in rankings between the two periods. Ford-made vehicles became the most frequently involved, with their count rising from 46 to 77, while Chevrolet (listed as 'CHEV') involvements decreased from 71 to 53. Regarding persons involved, the 16-20 age group saw its representation increase from 12.4% of all persons in 2023 to 15.1% in 2024.

Top Vehicle Makes (388 vehicles)

1
FORD77 (19.8%)
↑67.4%prior 46
2
CHEV53 (13.7%)
↓-25.4%prior 71
3
CHEVROLET26 (6.7%)
↑4.0%prior 25
4
JEEP21 (5.4%)
↑10.5%prior 19
5
TOYT20 (5.2%)
↑33.3%prior 15
6
GMC19 (4.9%)
↑58.3%prior 12
7
DODG18 (4.6%)
↑63.6%prior 11
8
HOND13 (3.4%)
↓-7.1%prior 14
9
NISS12 (3.1%)
↑33.3%prior 9
10
RAM9 (2.3%)
↑50.0%prior 6

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

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

Sex Distribution (249 persons with recorded sex)

Male153 (61.4%)
↓-21.1%prior 194
Female96 (38.6%)
↓-16.5%prior 115

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: 236
  • Total persons involved: 411
  • Total vehicles involved: 388

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

ThatCarHitMe.com · An Injuria.ai Company