Yearly Traffic Safety Analysis

212 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In 2023, Allamakee County recorded 212 total crashes, a 28.5% increase from the 165 crashes reported in 2022. While the number of fatalities remained unchanged at 3, total injuries rose from 53 to 62. The most significant year-over-year shift was the overall rise in crash volume across multiple categories.

212

▲ 28.5%was 165

Total Crash Events

3

Persons Killed

62

▲ 17.0%was 53

Persons Injured

3

Fatal Crash Events

Note: "Persons Killed" (3) 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 · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Allamakee County indicates a rising trend in traffic incidents year-over-year. Total crashes increased by 28.5%, from 165 in 2022 to 212 in 2023. This was accompanied by a 17.0% increase in total injuries, which grew from 53 to 62, while total fatalities held steady at 3 for both periods.

Vulnerable Road User Casualties

3

Motorists Killed

Prior: 3→0.0%

62

Motorists Injured

Prior: 52↑19.2%

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted between the two periods. In 2023, Sunday was the peak day for crashes with 44 incidents, a change from 2022 when Tuesday was the peak day with 30 incidents. The peak hour for crashes remained consistent at 5 p.m. in both years, though the number of crashes during that hour increased from 16 in 2022 to 20 in 2023.

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the total number of fatal crashes was identical in both years at 3, the fatal crash rate as a proportion of all crashes decreased from 1.8% in 2022 to 1.4% in 2023. The number of serious injury crashes increased from 1 to 7, and minor injury crashes more than doubled from 8 to 19. Conversely, crashes resulting in possible injuries decreased from 39 in 2022 to 29 in 2023.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.4%
→0.0%prior 3
Serious Injury7serious injury crashes3.3%
↑600.0%prior 1
Minor Injury19minor injury crashes9%
↑137.5%prior 8
Possible Injury29possible injury crashes13.7%
↓-25.6%prior 39
No Injury154no injury crashes72.6%
↑35.1%prior 114

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Top Contributing Factors

The top three contributing factors were the same in both years: collisions with an animal, drivers losing control, and running off a straight road. However, the counts for the top two factors increased significantly. Crashes involving an animal rose from 39 to 55, and incidents where a driver lost control increased from 25 to 41. The third-leading factor, running off a straight road, saw a smaller increase from 14 to 18 crashes.

Officer-Reported Primary Contributing Cause

Animal55 (25.9%)↑41.0%prior 39
Lost Control41 (19.3%)↑64.0%prior 25
Ran off road - straight18 (8.5%)↑28.6%prior 14
Driving too fast for conditions11 (5.2%)↑37.5%prior 8
Other (explain in narrative): Other9 (4.2%)↓-25.0%prior 12
Ran Stop Sign7 (3.3%)↑40.0%prior 5
Operating vehicle in an reckless, erratic, careless, negligent manner7 (3.3%)
FTYROW: From stop sign6 (2.8%)
Ran off road - left6 (2.8%)
Followed too close6 (2.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

In both years, the majority of crashes occurred in clear weather and on dry roads. However, there was a notable increase in crashes under adverse conditions in 2023 compared to 2022. Crashes in snow conditions increased from 4 to 15, and crashes on snowy road surfaces doubled from 8 to 16. The proportion of crashes occurring in daylight decreased from 52.1% in 2022 to 49.1% in 2023.

Weather

Clear126 (70.4%)
↑12.5%prior 112
Cloudy25 (14.0%)
↑19.0%prior 21
Snow15 (8.4%)
Rain4 (2.2%)
Freezing rain/drizzle3 (1.7%)
Fog, smoke, smog3 (1.7%)
Blowing Snow2 (1.1%)
Sleet, hail1 (0.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight104 (59.1%)
↑20.9%prior 86
Dark - roadway not lighted49 (27.8%)
↑32.4%prior 37
Dawn8 (4.5%)
↑33.3%prior 6
Dark - roadway lighted7 (4.0%)
↓-46.2%prior 13
Dusk6 (3.4%)
Dark - unknown roadway lighting2 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry131 (72.8%)
↑23.6%prior 106
Snow16 (8.9%)
↑100.0%prior 8
Ice/frost13 (7.2%)
↑18.2%prior 11
Wet8 (4.4%)
↓-42.9%prior 14
Gravel5 (2.8%)
↓-37.5%prior 8
Slush5 (2.8%)
Other (explain in narrative)1 (0.6%)
Mud, dirt1 (0.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Chevrolet and Ford ranking as the top two in both 2023 and 2022. An analysis of person demographics shows a significant increase in crash involvement for the 16-20 age group, which rose from 37 individuals in 2022 to 64 in 2023. The number of persons aged 65 and older involved in crashes also increased from 59 to 74.

Top Vehicle Makes (285 vehicles)

1
FORD48 (16.8%)
↑14.3%prior 42
2
CHEV41 (14.4%)
↑5.1%prior 39
3
CHEVROLET23 (8.1%)
↑27.8%prior 18
4
GMC15 (5.3%)
↑50.0%prior 10
5
DODG14 (4.9%)
↓-6.7%prior 15
6
BUIC11 (3.9%)
7
JEEP9 (3.2%)
→0.0%prior 9
8
RAM8 (2.8%)
9
NISS7 (2.5%)
10
FREIGHTLINER7 (2.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (257 persons with recorded sex)

Male162 (63.0%)
↑20.9%prior 134
Female95 (37.0%)
↑41.8%prior 67

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 212
  • Total persons involved: 414
  • Total vehicles involved: 285

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: 2023." Published September 9, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2023-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