Yearly Traffic Safety Analysis

269 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Mills County recorded 269 total crashes, an 18.0% increase from the 228 crashes reported in 2015. While the total number of injuries saw a slight decrease from 113 to 102, the most notable year-over-year shift was a 150% increase in traffic fatalities, which rose from 2 in 2015 to 5 in 2016.

269

▲ 18.0%was 228

Total Crash Events

5

▲ 150.0%was 2

Persons Killed

102

▼ -9.7%was 113

Persons Injured

5

▲ 150.0%was 2

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Mills County showed a rising trend, increasing by 18.0% from 228 in 2015 to 269 in 2016. Despite the higher crash volume, the total number of persons injured decreased by 9.7% from 113 to 102. Conversely, the number of fatalities more than doubled, increasing from 2 to 5 over the same period.

Vulnerable Road User Casualties

1

Cyclists Killed

Prior: 0→%

4

Motorists Killed

Prior: 2↑100.0%

1

Cyclists Injured

Prior: 0→%

101

Motorists Injured

Prior: 113↓-10.6%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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 years. In 2016, the peak day for crashes was Friday with 50 incidents, a change from Wednesday (44 incidents) in 2015. The peak hour for collisions also shifted slightly earlier, moving from the 4 p.m. hour in 2015 (18 crashes) to the 3 p.m. hour in 2016 (21 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased in 2016 compared to the previous year. The number of fatal crashes rose from 2 to 5, and their corresponding share of all crashes increased from 0.9% to 1.9%. While the proportion of serious injury crashes remained stable at approximately 7.4%, the share of no-injury crashes grew from 63.2% in 2015 to 69.5% in 2016.

Outcome by Severity (Crash Events)

Fatal5fatal crashes1.9%
↑150.0%prior 2
Serious Injury20serious injury crashes7.4%
↑17.6%prior 17
Minor Injury21minor injury crashes7.8%
↓-32.3%prior 31
Possible Injury36possible injury crashes13.4%
↑5.9%prior 34
No Injury187no injury crashes69.5%
↑29.9%prior 144

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the top contributing factor in both periods, though the count of such incidents decreased by 30.5% from 59 in 2015 to 41 in 2016. The second most common factor, 'Lost Control,' held steady with 34 incidents in both years. A significant increase was observed in crashes attributed to 'Followed too close,' where the count rose from 4 to 15 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal41 (15.2%)↓-30.5%prior 59
Lost Control34 (12.6%)→0.0%prior 34
Ran off road - straight19 (7.1%)→0.0%prior 19
Driving too fast for conditions16 (5.9%)→0.0%prior 16
Followed too close15 (5.6%)
Operating vehicle in an reckless, erratic, careless, negligent manner13 (4.8%)↑18.2%prior 11
Ran off road - left12 (4.5%)↑100.0%prior 6
FTYROW: From stop sign9 (3.3%)↓-25.0%prior 12
Other (explain in narrative): Other9 (3.3%)↑50.0%prior 6
Driver Distraction: Other interior distraction9 (3.3%)↑12.5%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

In 2016, a greater share of crashes occurred in daylight, rising to 60.2% of all incidents from 52.6% in 2015. Correspondingly, crashes in darkness on unlit roadways decreased in count from 65 to 51. While the majority of crashes in both years occurred on dry roads, the number of incidents on icy or frosty surfaces increased from 12 in 2015 to 22 in 2016.

Weather

Clear149 (60.3%)
↑24.2%prior 120
Cloudy58 (23.5%)
→0.0%prior 58
Snow14 (5.7%)
↑100.0%prior 7
Rain10 (4.0%)
↓-41.2%prior 17
Fog, smoke, smog6 (2.4%)
Severe Winds4 (1.6%)
Freezing rain/drizzle3 (1.2%)
Blowing Snow2 (0.8%)
Other (explain in narrative)1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight162 (65.6%)
↑35.0%prior 120
Dark - roadway not lighted51 (20.6%)
↓-21.5%prior 65
Dark - roadway lighted13 (5.3%)
↑62.5%prior 8
Dusk10 (4.0%)
↑100.0%prior 5
Dawn8 (3.2%)
↓-11.1%prior 9
Dark - unknown roadway lighting3 (1.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry183 (73.8%)
↑22.8%prior 149
Ice/frost22 (8.9%)
↑83.3%prior 12
Wet19 (7.7%)
↓-24.0%prior 25
Snow12 (4.8%)
Gravel10 (4.0%)
↓-28.6%prior 14
Slush2 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes were consistent year-over-year, led by Ford and Chevrolet. The number of Fords involved in crashes increased from 64 to 75, while Chevrolet models (including 'Chev') rose from 54 to 64. Examining the age of persons involved, the 26-34 age group saw the largest increase in count, from 66 individuals in 2015 to 80 in 2016.

Top Vehicle Makes (403 vehicles)

1
FORD75 (18.6%)
↑17.2%prior 64
2
CHEVROLET35 (8.7%)
↑52.2%prior 23
3
CHEV29 (7.2%)
↓-6.5%prior 31
4
DODGE25 (6.2%)
↑177.8%prior 9
5
JEEP13 (3.2%)
↑160.0%prior 5
6
HONDA13 (3.2%)
↑44.4%prior 9
7
GMC13 (3.2%)
↑30.0%prior 10
8
DODG12 (3%)
↓-20.0%prior 15
9
FREIGHTLINER9 (2.2%)
10
NISSAN9 (2.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (280 persons with recorded sex)

Male191 (68.2%)
↑40.4%prior 136
Female89 (31.8%)
↓-28.2%prior 124

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 269
  • Total persons involved: 477
  • Total vehicles involved: 403

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: 2016." Published September 9, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-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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