Yearly Traffic Safety Analysis

227 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Carroll County, total traffic crashes increased by 11.3% year-over-year, rising from 204 incidents in 2023 to 227 in 2024. While total fatalities increased from 3 to 4, the number of fatal crashes remained stable at 3 for both periods. The most significant shift was a 20.2% increase in crashes within the city of Carroll, which accounted for the majority of the county's overall rise in collisions.

227

▲ 11.3%was 204

Total Crash Events

4

▲ 33.3%was 3

Persons Killed

73

▲ 12.3%was 65

Persons Injured

3

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 data indicates a rising trend in traffic crashes in Carroll County. The total number of crashes grew from 204 in the prior year to 227 in the current year, an increase of 23 incidents or 11.3%. This was accompanied by a 12.3% increase in the number of people injured, which rose from 65 to 73.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 3↑33.3%

3

Pedestrians Injured

Prior: 2↑50.0%

1

Cyclists Injured

Prior: 2↓-50.0%

69

Motorists Injured

Prior: 61↑13.1%

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 timing of crashes showed a notable shift between the two periods. The peak day for crashes moved from Wednesday (41 crashes) in the prior year to Friday (45 crashes) in the current year. While the 3 p.m. hour remained the peak time for collisions in both years, the number of crashes during this hour surged by 50%, increasing from 20 to 30 incidents.

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 patterns shifted year-over-year. The number of fatal crashes was unchanged at 3, though total fatalities rose from 3 to 4. The proportion of crashes resulting in serious or minor injuries decreased, while the share of 'Possible Injury' crashes grew from 9.8% of all crashes in the prior period to 15.4% in the current period. Crashes with no reported injuries decreased as a share of the total, from 74.0% to 71.8%.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.3%
→0.0%prior 3
Serious Injury7serious injury crashes3.1%
↓-12.5%prior 8
Minor Injury19minor injury crashes8.4%
↓-13.6%prior 22
Possible Injury35possible injury crashes15.4%
↑75.0%prior 20
No Injury163no injury crashes71.8%
↑7.9%prior 151

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

The leading contributing factors for crashes changed year-over-year. Failure to yield from a stop sign became the top factor in the current period with 20 crashes, a 42.9% increase in count from 14 crashes in the prior year when it ranked third. Conversely, 'Followed too close,' the top factor in the prior period with 16 crashes, decreased to 14 crashes and fell to the third position. Crashes attributed to 'Improper Backing' also saw a notable increase, rising from 8 to 13 incidents.

Officer-Reported Primary Contributing Cause

FTYROW: From stop sign20 (8.8%)↑42.9%prior 14
Other (explain in narrative): Other15 (6.6%)→0.0%prior 15
Followed too close14 (6.2%)↓-12.5%prior 16
Ran Stop Sign13 (5.7%)↑44.4%prior 9
Improper Backing13 (5.7%)↑62.5%prior 8
Driving too fast for conditions12 (5.3%)↓-7.7%prior 13
Made improper turn11 (4.8%)↑57.1%prior 7
FTYROW: Making left turn10 (4.4%)↑11.1%prior 9
Ran Traffic Signal10 (4.4%)
Driver Distraction: Other interior distraction10 (4.4%)↓-9.1%prior 11

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 environmental conditions at the time of crashes remained broadly consistent year-over-year. Crashes in clear weather and on dry roads continued to be the most common scenario in both periods, accounting for approximately 70% of all incidents. The proportion of crashes occurring during daylight hours was also stable at around 70%. The only notable change was an increase in crashes during snowy weather, which rose from 6 to 13 incidents, and on snowy road surfaces, which increased from 7 to 16 crashes.

Weather

Clear158 (71.8%)
↑9.7%prior 144
Cloudy36 (16.4%)
→0.0%prior 36
Snow13 (5.9%)
↑116.7%prior 6
Fog, smoke, smog4 (1.8%)
Rain3 (1.4%)
↓-62.5%prior 8
Freezing rain/drizzle2 (0.9%)
Severe Winds2 (0.9%)
Blowing sand, soil, dirt1 (0.5%)
Blowing Snow1 (0.5%)

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

Lighting

Daylight159 (71.9%)
↑12.0%prior 142
Dark - roadway lighted31 (14.0%)
↑121.4%prior 14
Dark - roadway not lighted21 (9.5%)
↓-30.0%prior 30
Dusk5 (2.3%)
↓-54.5%prior 11
Dark - unknown roadway lighting3 (1.4%)
Dawn2 (0.9%)

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

Road Surface

Dry161 (72.5%)
↑7.3%prior 150
Wet24 (10.8%)
↑26.3%prior 19
Snow16 (7.2%)
↑128.6%prior 7
Ice/frost12 (5.4%)
↓-20.0%prior 15
Gravel9 (4.1%)
↓-18.2%prior 11

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

Vehicles & Demographics

While Chevrolet and Ford remained the two most common vehicle makes involved in crashes, the number of Ford vehicles in collisions increased by 42.1%, from 57 in the prior year to 81 in the current year. There was also a significant shift in the age demographics of persons involved in crashes. The number of individuals in the 26-34 age group dropped from 76 to 40, while involvement for the 45-54 age group increased from 39 to 55.

Top Vehicle Makes (405 vehicles)

1
CHEV87 (21.5%)
↑3.6%prior 84
2
FORD81 (20%)
↑42.1%prior 57
3
CHEVROLET22 (5.4%)
↑22.2%prior 18
4
DODG19 (4.7%)
↑216.7%prior 6
5
JEEP19 (4.7%)
↑46.2%prior 13
6
BUIC14 (3.5%)
↑40.0%prior 10
7
TOYT13 (3.2%)
↓-13.3%prior 15
8
TOYO12 (3%)
↑20.0%prior 10
9
GMC11 (2.7%)
↓-45.0%prior 20
10
NR11 (2.7%)
↑22.2%prior 9

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

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

Sex Distribution (283 persons with recorded sex)

Male155 (54.8%)
↓-10.4%prior 173
Female128 (45.2%)
↓-12.3%prior 146

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: 227
  • Total persons involved: 425
  • Total vehicles involved: 405

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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