Yearly Traffic Safety Analysis

201 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In 2024, Allamakee County recorded 201 total traffic crashes, a 5.2% decrease from the 212 crashes reported in 2023. This overall decline was accompanied by fewer fatalities, which dropped from 3 to 2, and fewer injuries, which decreased from 62 to 57. A notable year-over-year change was the 26.8% decrease in the count of crashes attributed to "Lost Control," which fell from 41 incidents in 2023 to 30 in 2024.

201

▼ -5.2%was 212

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

57

▼ -8.1%was 62

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 in traffic crashes in Allamakee County shows a modest decline year-over-year. Total crashes fell by 5.2%, from 212 in 2023 to 201 in 2024. Similarly, the human impact lessened, with total fatalities decreasing from 3 to 2 and total injuries declining from 62 to 57 over the same period.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

1

Cyclists Injured

Prior: 0→%

56

Motorists Injured

Prior: 62↓-9.7%

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 some shifts between the two periods. The peak day for crashes moved from Sunday (44 crashes) in 2023 to Thursday (33 crashes) in 2024. While the 5 PM hour remained the peak time for collisions in both years, the number of crashes during this hour increased from 20 to 26. Monthly patterns also varied, with 2023 experiencing a significant spike of 45 crashes in November, whereas 2024's crashes were more evenly distributed, peaking at 25 in the same month.

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 saw a slight improvement year-over-year. The number of fatal crashes decreased from 3 in 2023 to 2 in 2024, lowering the fatal crash rate from 1.4% to 1.0% of all collisions. The total number of injury-resulting crashes (serious, minor, and possible) also fell slightly from 55 to 52. Crashes resulting in no injuries accounted for a slightly higher proportion of the total, rising from 72.6% in 2023 to 73.1% in 2024.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1%
↓-33.3%prior 3
Serious Injury7serious injury crashes3.5%
→0.0%prior 7
Minor Injury16minor injury crashes8%
↓-15.8%prior 19
Possible Injury29possible injury crashes14.4%
→0.0%prior 29
No Injury147no injury crashes73.1%
↓-4.5%prior 154

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 top contributing factor in both periods, with a nearly identical count of 54 in 2024 versus 55 in 2023. A significant change occurred in the second-ranked factor, "Lost Control," which saw its crash count drop by 26.8% from 41 incidents in 2023 to 30 in 2024. Conversely, crashes attributed to "Ran off road - left" more than doubled, increasing from 6 to 15, while incidents of "Ran off road - straight" decreased from 18 to 12.

Officer-Reported Primary Contributing Cause

Animal54 (26.9%)↓-1.8%prior 55
Lost Control30 (14.9%)↓-26.8%prior 41
Ran off road - left15 (7.5%)↑150.0%prior 6
Ran off road - straight12 (6%)↓-33.3%prior 18
Other (explain in narrative): Other10 (5%)↑11.1%prior 9
Driving too fast for conditions7 (3.5%)↓-36.4%prior 11
Followed too close7 (3.5%)↑16.7%prior 6
FTYROW: Making left turn6 (3%)
Improper Backing6 (3%)
Driver Distraction: Other interior distraction5 (2.5%)

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 2024 and 2023, the majority of crashes occurred in clear weather on dry roads during daylight hours. However, there was a marked decrease in crashes under adverse conditions in 2024. The number of collisions on snowy, icy, or slushy road surfaces fell from 34 in 2023 to just 16 in 2024. Similarly, crashes during snowy weather dropped from 15 to 5, and collisions in darkness on unlit roads decreased from 49 to 30.

Weather

Clear115 (73.7%)
↓-8.7%prior 126
Cloudy20 (12.8%)
↓-20.0%prior 25
Rain6 (3.8%)
Snow5 (3.2%)
↓-66.7%prior 15
Freezing rain/drizzle3 (1.9%)
Fog, smoke, smog3 (1.9%)
Other (explain in narrative)2 (1.3%)
Blowing Snow2 (1.3%)

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

Lighting

Daylight111 (69.8%)
↑6.7%prior 104
Dark - roadway not lighted30 (18.9%)
↓-38.8%prior 49
Dark - roadway lighted7 (4.4%)
→0.0%prior 7
Dawn4 (2.5%)
↓-50.0%prior 8
Dusk4 (2.5%)
↓-33.3%prior 6
Dark - unknown roadway lighting3 (1.9%)

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

Road Surface

Dry118 (74.7%)
↓-9.9%prior 131
Wet15 (9.5%)
↑87.5%prior 8
Snow9 (5.7%)
↓-43.8%prior 16
Gravel9 (5.7%)
↑80.0%prior 5
Ice/frost4 (2.5%)
↓-69.2%prior 13
Slush3 (1.9%)
↓-40.0%prior 5

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

Vehicles & Demographics

An analysis of vehicles involved in crashes shows Chevrolet and Ford as the top two makes in both years. The number of Chevrolet vehicles involved increased from 64 in 2023 to 69 in 2024, while Ford vehicles increased from 48 to 57. Regarding driver demographics, the representation of most age groups remained relatively stable. However, individuals in the 26-34 age group represented a smaller share of people involved in crashes, dropping from 15.9% in 2023 to 11.0% in 2024.

Top Vehicle Makes (282 vehicles)

1
FORD57 (20.2%)
↑18.8%prior 48
2
CHEV36 (12.8%)
↓-12.2%prior 41
3
CHEVROLET33 (11.7%)
↑43.5%prior 23
4
GMC14 (5%)
↓-6.7%prior 15
5
DODG11 (3.9%)
↓-21.4%prior 14
6
DODGE10 (3.5%)
↑66.7%prior 6
7
NISSAN7 (2.5%)
8
NR7 (2.5%)
↑40.0%prior 5
9
CHRY6 (2.1%)
→0.0%prior 6
10
TOYOTA6 (2.1%)
→0.0%prior 6

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

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

Sex Distribution (143 persons with recorded sex)

Male81 (56.6%)
↓-50.0%prior 162
Female62 (43.4%)
↓-34.7%prior 95

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: 201
  • Total persons involved: 290
  • Total vehicles involved: 282

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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