Yearly Traffic Safety Analysis

129 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In Guthrie County, total traffic crashes increased by 20.6% from 107 in 2022 to 129 in 2023. While fatalities remained stable with one death in each period, the number of persons injured rose by 31% from 29 to 38. The most notable year-over-year shift was the increase in crashes attributed to drivers losing control.

129

▲ 20.6%was 107

Total Crash Events

1

Persons Killed

38

▲ 31.0%was 29

Persons Injured

1

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 · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in traffic incidents shows an increase year-over-year. Total crashes rose from 107 to 129, and the number of people injured increased from 29 to 38. The number of fatalities held steady at one for both 2022 and 2023.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

1

Pedestrians Injured

Prior: 1→0.0%

1

Cyclists Injured

Prior: 0→%

36

Motorists Injured

Prior: 28↑28.6%

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 primary time for crashes remained consistent between the two periods, with Wednesday being the peak day and 6 p.m. the peak hour in both 2022 and 2023. However, the monthly distribution of crashes shifted, as 2023 saw a pronounced spike in November with 20 crashes, compared to 12 in the same month of the prior year.

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

The number of fatal crashes was unchanged at one incident in both 2023 and 2022. The proportion of crashes resulting in any level of injury was also stable, recorded at 21.8% in 2023 versus 22.3% in 2022. However, the total number of individuals reported as injured increased from 29 to 38 year-over-year.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.8%
→0.0%prior 1
Serious Injury4serious injury crashes3.1%
→0.0%prior 4
Minor Injury10minor injury crashes7.8%
↓-23.1%prior 13
Possible Injury14possible injury crashes10.9%
↑100.0%prior 7
No Injury100no injury crashes77.5%
↑22.0%prior 82

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

Collisions with animals remained the leading contributing factor in both periods, accounting for 40 crashes in 2023 compared to 42 in 2022. The most significant change was a 58% increase in the count of crashes attributed to a driver losing control, which rose from 12 incidents in 2022 to 19 in 2023. Crashes where a driver ran off the road also increased from 9 to 12.

Officer-Reported Primary Contributing Cause

Animal40 (31%)↓-4.8%prior 42
Lost Control19 (14.7%)↑58.3%prior 12
Ran off road - straight12 (9.3%)↑33.3%prior 9
Other (explain in narrative): Other10 (7.8%)↑100.0%prior 5
Driving too fast for conditions7 (5.4%)↓-22.2%prior 9
Operating vehicle in an reckless, erratic, careless, negligent manner5 (3.9%)↓-16.7%prior 6
FTYROW: From stop sign4 (3.1%)
Followed too close3 (2.3%)
Ran off road - right3 (2.3%)
FTYROW: Making left turn2 (1.6%)

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

Road & Environmental Conditions

The proportion of crashes occurring in daylight was similar across both years, but incidents in dark, unlit conditions increased from 18 in 2022 to 26 in 2023. While dry roads were the most common surface condition in both periods, crashes on snow or ice-covered roads more than doubled, rising from a combined 9 incidents in 2022 to 19 in 2023.

Weather

Clear64 (67.4%)
↑12.3%prior 57
Cloudy16 (16.8%)
↑100.0%prior 8
Snow7 (7.4%)
↑16.7%prior 6
Rain3 (3.2%)
Freezing rain/drizzle2 (2.1%)
Blowing Snow1 (1.1%)
Sleet, hail1 (1.1%)
Fog, smoke, smog1 (1.1%)

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

Lighting

Daylight59 (61.5%)
↑13.5%prior 52
Dark - roadway not lighted26 (27.1%)
↑44.4%prior 18
Dawn5 (5.2%)
Dusk4 (4.2%)
Dark - roadway lighted2 (2.1%)

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

Road Surface

Dry66 (69.5%)
↑32.0%prior 50
Snow11 (11.6%)
↑57.1%prior 7
Ice/frost8 (8.4%)
Wet6 (6.3%)
↓-14.3%prior 7
Gravel2 (2.1%)
↓-75.0%prior 8
Mud, dirt1 (1.1%)
Other (explain in narrative)1 (1.1%)

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

Vehicles & Demographics

Ford and Chevrolet were the most common vehicle makes involved in crashes in both years, with the count for each increasing from 30 and 28 respectively in 2022 to 41 each in 2023. A notable shift occurred in the age distribution of persons involved in crashes; the 35-44 age group became the largest cohort with 46 individuals in 2023, up from 34 in the prior year. The number of people in the 26-34 age group involved in crashes also grew substantially from 18 to 44.

Top Vehicle Makes (173 vehicles)

1
FORD41 (23.7%)
↑36.7%prior 30
2
CHEV41 (23.7%)
↑46.4%prior 28
3
JEEP14 (8.1%)
4
CHEVROLET8 (4.6%)
↓-38.5%prior 13
5
GMC7 (4%)
↑16.7%prior 6
6
DODG6 (3.5%)
7
KIA4 (2.3%)
8
HOND3 (1.7%)
9
MITS3 (1.7%)
10
NISS3 (1.7%)

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

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

Sex Distribution (163 persons with recorded sex)

Male101 (62.0%)
↑31.2%prior 77
Female62 (38.0%)
↑40.9%prior 44

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: 129
  • Total persons involved: 253
  • Total vehicles involved: 173

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

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