Yearly Traffic Safety Analysis

111 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

Total crashes in Monroe County remained stable, with 111 incidents recorded in 2024 compared to 112 in 2023. Despite the consistent volume, outcomes improved significantly, as total injuries decreased by 37% from 35 to 22, and fatalities dropped from 2 to 1. The most notable year-over-year shift was an 80% reduction in crashes attributed to DUI, which fell from 5 incidents in 2023 to 1 in 2024.

111

▼ -0.9%was 112

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

22

▼ -37.1%was 35

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 totals in Monroe County were nearly unchanged year-over-year, with 111 incidents in 2024 compared to 112 in 2023, a decrease of less than 1%. Despite the stable crash volume, the severity of these incidents decreased. The number of people injured fell by 37% from 35 to 22, and the number of fatalities was halved from 2 to 1.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Motorists Killed

Prior: 2↓-100.0%

0

Pedestrians Injured

Prior: 0→0.0%

22

Motorists Injured

Prior: 35↓-37.1%

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 shifted between the two periods. In 2024, the peak day for crashes was Friday with 23 incidents, a change from Thursday, which saw 19 incidents in the prior year. The peak hour also moved from the afternoon at 3 p.m. (12 crashes in 2023) to the morning at 7 a.m. (11 crashes 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

Crash severity improved year-over-year, with the fatal crash rate decreasing from 1.8% of all crashes in 2023 to 0.9% in 2024. The proportion of crashes resulting in any level of injury fell from 24.1% to 15.3%. Correspondingly, the share of crashes with no reported injuries increased, accounting for 84.7% of all incidents in 2024, up from 75.9% in the previous year.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.9%
↓-50.0%prior 2
Serious Injury2serious injury crashes1.8%
↓-50.0%prior 4
Minor Injury9minor injury crashes8.1%
↑28.6%prior 7
Possible Injury5possible injury crashes4.5%
↓-64.3%prior 14
No Injury94no injury crashes84.7%
↑10.6%prior 85

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 animals remained the leading contributing factor in both periods, and the count of these incidents increased by 29%, from 31 in 2023 to 40 in 2024. In contrast, several other common factors saw a decline in count, including crashes from following too closely (down from 12 to 8) and driving too fast for conditions (down from 9 to 6). Notably, crashes attributed to reckless, erratic, or careless driving fell from 8 incidents to just 1.

Officer-Reported Primary Contributing Cause

Animal40 (36%)↑29.0%prior 31
Followed too close8 (7.2%)↓-33.3%prior 12
Driving too fast for conditions6 (5.4%)↓-33.3%prior 9
Ran off road - straight5 (4.5%)↓-28.6%prior 7
Ran off road - left5 (4.5%)→0.0%prior 5
Driver Distraction: Other interior distraction5 (4.5%)→0.0%prior 5
Failed to keep in proper lane5 (4.5%)
FTYROW: From stop sign3 (2.7%)↓-57.1%prior 7
Exceeded authorized speed3 (2.7%)
FTYROW: From driveway3 (2.7%)

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

Road & Environmental Conditions

Crash conditions were largely similar year-over-year, with the majority of incidents in both periods occurring in daylight and on dry roads. In 2024, 66% of crashes happened during daylight, compared to 68% in 2023, while 72% occurred on dry roads, versus 83% in the prior year. There was a notable increase in crashes on snow-covered roads, which doubled from 4 incidents in 2023 to 8 in 2024.

Weather

Clear61 (77.2%)
↓-11.6%prior 69
Cloudy10 (12.7%)
→0.0%prior 10
Snow5 (6.3%)
Rain2 (2.5%)
Blowing Snow1 (1.3%)

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

Lighting

Daylight55 (66.3%)
↓-6.8%prior 59
Dark - roadway not lighted10 (12.0%)
→0.0%prior 10
Dawn8 (9.6%)
↑60.0%prior 5
Dark - roadway lighted5 (6.0%)
↓-58.3%prior 12
Dark - unknown roadway lighting5 (6.0%)

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

Road Surface

Dry57 (72.2%)
↓-20.8%prior 72
Snow8 (10.1%)
Gravel7 (8.9%)
Wet4 (5.1%)
Ice/frost2 (2.5%)
Other (explain in narrative)1 (1.3%)

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 remained consistent, with Ford and Chevrolet vehicles being the most common in both 2024 and 2023. The number of Ford vehicles involved increased slightly from 36 to 38. The total number of persons involved in crashes decreased from 241 to 167, with the count of individuals in the 16-20 age group falling from 45 to 22 and the 35-44 age group falling from 51 to 27.

Top Vehicle Makes (163 vehicles)

1
FORD38 (23.3%)
↑5.6%prior 36
2
CHEV27 (16.6%)
↑3.8%prior 26
3
GMC9 (5.5%)
↑28.6%prior 7
4
TOYO8 (4.9%)
↑60.0%prior 5
5
JEEP8 (4.9%)
↓-20.0%prior 10
6
CHEVROLET8 (4.9%)
→0.0%prior 8
7
RAM7 (4.3%)
8
DODG6 (3.7%)
↓-25.0%prior 8
9
DODGE4 (2.5%)
10
PONT3 (1.8%)

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

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

Sex Distribution (102 persons with recorded sex)

Male62 (60.8%)
↓-36.7%prior 98
Female40 (39.2%)
↓-36.5%prior 63

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: 111
  • Total persons involved: 167
  • Total vehicles involved: 163

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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