Yearly Traffic Safety Analysis

181 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In Emmet County, total crashes remained stable year-over-year, with 181 incidents in 2024 compared to 183 in 2023, a decrease of approximately 1.1%. While total crashes saw little change, there was a significant positive development with fatalities dropping from one in 2023 to zero in 2024. Conversely, the number of people injured in crashes increased by 24.4%, rising from 45 to 56.

181

▼ -1.1%was 183

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

56

▲ 24.4%was 45

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

Overall, the total number of crashes in Emmet County showed a slight decrease, falling by 1.1% from 183 in 2023 to 181 in 2024. Despite this stability in crash volume, the number of resulting injuries saw a notable increase of 24.4% year-over-year, while fatalities were eliminated entirely.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 1↓-100.0%

56

Motorists Injured

Prior: 43↑30.2%

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 in Emmet County shifted between the two periods. In 2024, Friday was the most common day for crashes with 36 incidents, compared to 2023 when Tuesday and Friday tied for the peak with 31 crashes each. A more significant change was observed in the peak hour, which moved from 3 PM in 2023 (22 crashes) to 7 AM in 2024 (20 crashes), indicating a shift in risk from the afternoon to the morning commute.

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 improved in one key metric, with fatal crashes decreasing from one in 2023 to zero in 2024. However, the proportion of crashes involving injuries shifted. The share of minor injury crashes increased from 8.2% of all crashes in 2023 to 12.7% in 2024, and serious injury crashes rose slightly from 2.2% to 2.8%. Conversely, crashes resulting in possible injuries decreased from a 10.9% share to a 6.6% share of the total.

Outcome by Severity (Crash Events)

Serious Injury5serious injury crashes2.8%
↑25.0%prior 4
Minor Injury23minor injury crashes12.7%
↑53.3%prior 15
Possible Injury12possible injury crashes6.6%
↓-40.0%prior 20
No Injury141no injury crashes77.9%
↓-1.4%prior 143

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 periods, with the count increasing from 42 crashes in 2023 to 48 in 2024. 'Driving too fast for conditions' also saw a slight increase, from 14 to 16 incidents. Notably, crashes attributed to 'Lost Control' decreased significantly, falling from 16 incidents in 2023 to just 5 in 2024, a 68.8% reduction in count. Conversely, 'Ran off road - left' incidents tripled from 3 to 9 year-over-year.

Officer-Reported Primary Contributing Cause

Animal48 (26.5%)↑14.3%prior 42
Driving too fast for conditions16 (8.8%)↑14.3%prior 14
Other (explain in narrative): Other11 (6.1%)↓-42.1%prior 19
Ran off road - left9 (5%)
Followed too close8 (4.4%)↓-33.3%prior 12
Operating vehicle in an reckless, erratic, careless, negligent manner8 (4.4%)↑33.3%prior 6
Ran off road - straight7 (3.9%)
FTYROW: Making left turn6 (3.3%)
FTYROW: From stop sign6 (3.3%)↓-14.3%prior 7
Lost Control5 (2.8%)↓-68.8%prior 16

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

Road & Environmental Conditions

While total crashes were nearly identical, the conditions under which they occurred shifted. In 2024, a smaller proportion of crashes happened during daylight (89 crashes) compared to 2023 (107 crashes), while crashes on unlit dark roadways increased from 26 to 36. Regarding weather, incidents during clear conditions decreased from 122 to 105. However, crashes in snowy conditions more than doubled from 4 to 10, and incidents on wet road surfaces also doubled from 9 to 18.

Weather

Clear105 (69.5%)
↓-13.9%prior 122
Cloudy16 (10.6%)
↓-5.9%prior 17
Snow10 (6.6%)
Fog, smoke, smog10 (6.6%)
Rain7 (4.6%)
→0.0%prior 7
Blowing Snow3 (2.0%)

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

Lighting

Daylight89 (58.2%)
↓-16.8%prior 107
Dark - roadway not lighted36 (23.5%)
↑38.5%prior 26
Dark - roadway lighted17 (11.1%)
→0.0%prior 17
Dusk5 (3.3%)
Dawn3 (2.0%)
↓-50.0%prior 6
Dark - unknown roadway lighting3 (2.0%)

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

Road Surface

Dry106 (69.7%)
↓-9.4%prior 117
Wet18 (11.8%)
↑100.0%prior 9
Ice/frost12 (7.9%)
↓-25.0%prior 16
Snow10 (6.6%)
↓-23.1%prior 13
Gravel4 (2.6%)
Slush1 (0.7%)
Mud, dirt1 (0.7%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Chevrolet and Ford vehicles being the most frequent in both 2023 and 2024. An analysis of the age of persons involved in crashes reveals a shift in demographics. The number of individuals aged 65 and older involved in crashes increased from 41 in 2023 to 50 in 2024. In contrast, there was a marked decrease in the number of people involved from several middle-age groups, including the 26-34 age group (from 51 to 31) and the 55-64 group (from 51 to 22).

Top Vehicle Makes (265 vehicles)

1
CHEV53 (20%)
↑8.2%prior 49
2
FORD42 (15.8%)
↓-8.7%prior 46
3
CHEVROLET23 (8.7%)
↑35.3%prior 17
4
TOYO15 (5.7%)
↑200.0%prior 5
5
GMC14 (5.3%)
↓-6.7%prior 15
6
BUIC12 (4.5%)
↑71.4%prior 7
7
NR11 (4.2%)
→0.0%prior 11
8
JEEP11 (4.2%)
↓-21.4%prior 14
9
CHRY9 (3.4%)
↑12.5%prior 8
10
DODG8 (3%)
↓-11.1%prior 9

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

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

Sex Distribution (152 persons with recorded sex)

Male87 (57.2%)
↓-38.7%prior 142
Female65 (42.8%)
↓-32.3%prior 96

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: 276
  • Total vehicles involved: 265

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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