Yearly Traffic Safety Analysis

219 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In 2023, Mills County recorded 219 total crashes, a 6.8% decrease from the 235 crashes reported in 2022. The most significant year-over-year change was a substantial reduction in traffic fatalities, which fell from 5 in 2022 to 1 in 2023. Total injuries also saw a decrease, dropping from 102 to 76 over the same period.

219

▼ -6.8%was 235

Total Crash Events

1

▼ -80.0%was 5

Persons Killed

76

▼ -25.5%was 102

Persons Injured

1

▼ -80.0%was 5

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

Overall traffic safety trends in Mills County improved from 2022 to 2023. The total number of crashes decreased by 6.8%, from 235 to 219. This improvement was also reflected in crash outcomes, with total injuries declining by 25.5% and fatalities dropping by 80% year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 5↓-80.0%

1

Pedestrians Injured

Prior: 0→%

75

Motorists Injured

Prior: 102↓-26.5%

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 timing of crashes shifted between 2022 and 2023. The peak day for collisions moved from Tuesday (35 crashes) in the prior year to Friday (38 crashes) in the current year. The peak hour also shifted earlier, from 7 p.m. in 2022 (18 crashes) to 5 p.m. in 2023 (18 crashes). While 2022 saw a relatively even distribution of crashes across the week, 2023 showed a more pronounced concentration on Fridays and Saturdays.

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

Crash severity decreased notably in 2023 compared to the previous year. The number of fatal crashes fell from 5 to 1, causing the fatal crash rate to drop from 2.13% to 0.46%. The proportion of crashes resulting in serious injuries remained stable at approximately 6.0%, though the count decreased from 14 to 13. Crashes involving minor or possible injuries also saw a reduction in both their counts and their share of the total, while the share of no-injury crashes increased from 65.5% to 69.4%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.5%
↓-80.0%prior 5
Serious Injury13serious injury crashes5.9%
↓-7.1%prior 14
Minor Injury31minor injury crashes14.2%
↓-18.4%prior 38
Possible Injury22possible injury crashes10%
↓-8.3%prior 24
No Injury152no injury crashes69.4%
↓-1.3%prior 154

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 involving an animal remained the leading contributing factor in both periods, though the count decreased from 44 in 2022 to 39 in 2023. 'Lost Control' also remained the second-ranked factor, but its count fell from 37 to 24. A notable change was the 100% increase in crashes attributed to 'Failure to Yield Right of Way from a stop sign,' which grew from 8 to 16 incidents, making it the third most common factor in 2023. Conversely, crashes related to 'Driving too fast for conditions' declined from 15 to 10.

Officer-Reported Primary Contributing Cause

Animal39 (17.8%)↓-11.4%prior 44
Lost Control24 (11%)↓-35.1%prior 37
FTYROW: From stop sign16 (7.3%)↑100.0%prior 8
Ran off road - left14 (6.4%)↓-12.5%prior 16
Followed too close11 (5%)↑37.5%prior 8
Ran off road - straight10 (4.6%)↓-28.6%prior 14
Driving too fast for conditions10 (4.6%)↓-33.3%prior 15
Other (explain in narrative): Other9 (4.1%)→0.0%prior 9
Operating vehicle in an reckless, erratic, careless, negligent manner8 (3.7%)
Driver Distraction: Other interior distraction7 (3.2%)↑40.0%prior 5

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 distribution of crashes across different lighting and weather conditions remained largely consistent year-over-year, with most incidents in both periods occurring in clear weather and daylight. However, there was a notable shift in road surface conditions. The proportion of crashes on dry surfaces decreased from 69.8% in 2022 to 63.5% in 2023. Conversely, the share of crashes occurring on gravel roads increased from 3.0% (7 crashes) to 5.9% (13 crashes).

Weather

Clear147 (79.0%)
↓-9.3%prior 162
Cloudy17 (9.1%)
↓-15.0%prior 20
Snow11 (5.9%)
↓-8.3%prior 12
Rain6 (3.2%)
↓-25.0%prior 8
Freezing rain/drizzle3 (1.6%)
Fog, smoke, smog1 (0.5%)
Severe Winds1 (0.5%)

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

Lighting

Daylight124 (66.3%)
↓-6.8%prior 133
Dark - roadway not lighted39 (20.9%)
↓-4.9%prior 41
Dark - roadway lighted14 (7.5%)
↓-17.6%prior 17
Dawn6 (3.2%)
↓-25.0%prior 8
Dusk3 (1.6%)
↓-66.7%prior 9
Dark - unknown roadway lighting1 (0.5%)

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

Road Surface

Dry139 (74.3%)
↓-15.2%prior 164
Wet17 (9.1%)
↓-10.5%prior 19
Gravel13 (7.0%)
↑85.7%prior 7
Snow11 (5.9%)
↓-38.9%prior 18
Ice/frost3 (1.6%)
Slush3 (1.6%)
Mud, dirt1 (0.5%)

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

Vehicles & Demographics

Ford remained the most common vehicle make involved in crashes, with its count increasing from 57 in 2022 to 67 in 2023. In contrast, the combined count for Chevrolet vehicles (listed as 'CHEV' and 'CHEVROLET') decreased from 58 to 46. The demographic profile of persons involved in crashes also shifted; the share of individuals in the 26-34 and 35-44 age groups increased, while the representation of younger people (16-20) and older people (65+) declined compared to the prior year.

Top Vehicle Makes (327 vehicles)

1
FORD67 (20.5%)
↑17.5%prior 57
2
CHEV25 (7.6%)
↓-26.5%prior 34
3
CHEVROLET21 (6.4%)
↓-12.5%prior 24
4
JEEP19 (5.8%)
↑35.7%prior 14
5
FREIGHTLINER13 (4%)
↑116.7%prior 6
6
GMC12 (3.7%)
↑33.3%prior 9
7
RAM12 (3.7%)
→0.0%prior 12
8
NISSAN10 (3.1%)
↑66.7%prior 6
9
TOYT10 (3.1%)
↑25.0%prior 8
10
DODG10 (3.1%)
→0.0%prior 10

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 (299 persons with recorded sex)

Male195 (65.2%)
↑8.3%prior 180
Female104 (34.8%)
↓-14.0%prior 121

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: 219
  • Total persons involved: 456
  • Total vehicles involved: 327

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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