Yearly Traffic Safety Analysis

280 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Mills County recorded 280 total crashes, an increase of 4.1% from the 269 crashes reported in 2016. Despite the rise in total collisions, the number of fatalities decreased from 5 in the prior year to 3 in the current year. The total number of injuries remained nearly stable, with 104 in 2017 compared to 102 in 2016.

280

▲ 4.1%was 269

Total Crash Events

3

▼ -40.0%was 5

Persons Killed

104

▲ 2.0%was 102

Persons Injured

3

▼ -40.0%was 5

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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Mills County showed a slight upward trend, increasing from 269 in 2016 to 280 in 2017. While the total number of crashes rose by 4.1%, the outcomes were mixed, with total injuries seeing a marginal increase from 102 to 104, and fatalities decreasing from 5 to 3.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

2

Motorists Killed

Prior: 4↓-50.0%

1

Pedestrians Injured

Prior: 0→%

103

Motorists Injured

Prior: 101↑2.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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 2017, the peak day for crashes was Sunday with 48 incidents, a change from 2016 when Friday was the peak day with 50 crashes. The busiest hour also shifted later into the evening commute, moving from 3 p.m. in 2016 (21 crashes) to 5 p.m. in 2017 (22 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes increased, the severity profile showed a decrease in fatal outcomes. The fatal crash rate fell from 1.86% in 2016 to 1.07% in 2017. Conversely, crashes resulting in serious injuries increased, rising from 20 incidents (7.4% of total) in the prior year to 25 incidents (8.9% of total) in the current year. The proportion of crashes with no injuries decreased from 69.5% to 67.5%.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.1%
↓-40.0%prior 5
Serious Injury25serious injury crashes8.9%
↑25.0%prior 20
Minor Injury25minor injury crashes8.9%
↑19.0%prior 21
Possible Injury38possible injury crashes13.6%
↑5.6%prior 36
No Injury189no injury crashes67.5%
↑1.1%prior 187

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors remained consistent year-over-year, with 'Animal' (44 crashes in 2017 vs. 41 in 2016) and 'Lost Control' (38 crashes vs. 34) topping the list in both periods. A notable increase was observed in crashes attributed to 'Ran off road - straight,' which rose from 19 incidents in 2016 to 32 in 2017, a 68% increase in count. Crashes involving 'Driving too fast for conditions' also saw a modest rise in count from 16 to 18 incidents.

Officer-Reported Primary Contributing Cause

Animal44 (15.7%)↑7.3%prior 41
Lost Control38 (13.6%)↑11.8%prior 34
Ran off road - straight32 (11.4%)↑68.4%prior 19
Other (explain in narrative): Other19 (6.8%)↑111.1%prior 9
Driving too fast for conditions18 (6.4%)↑12.5%prior 16
Followed too close17 (6.1%)↑13.3%prior 15
Ran off road - left14 (5%)↑16.7%prior 12
FTYROW: From stop sign12 (4.3%)↑33.3%prior 9
Operating vehicle in an reckless, erratic, careless, negligent manner10 (3.6%)↓-23.1%prior 13
Driver Distraction: Other interior distraction7 (2.5%)↓-22.2%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in clear weather and on dry roads remained the most common scenario in both years. However, there was a notable shift in lighting conditions, with the proportion of crashes occurring in daylight decreasing from 60.2% in 2016 to 53.9% in 2017. Correspondingly, crashes on dark, unlighted roadways increased in both count (from 51 to 69) and share (from 19.0% to 24.6%). The proportion of crashes during adverse weather and on adverse road surfaces remained relatively stable year-over-year.

Weather

Clear165 (64.2%)
↑10.7%prior 149
Cloudy57 (22.2%)
↓-1.7%prior 58
Rain16 (6.2%)
↑60.0%prior 10
Snow9 (3.5%)
↓-35.7%prior 14
Freezing rain/drizzle8 (3.1%)
Fog, smoke, smog2 (0.8%)
↓-66.7%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight151 (58.8%)
↓-6.8%prior 162
Dark - roadway not lighted69 (26.8%)
↑35.3%prior 51
Dark - roadway lighted13 (5.1%)
→0.0%prior 13
Dusk12 (4.7%)
↑20.0%prior 10
Dawn10 (3.9%)
↑25.0%prior 8
Dark - unknown roadway lighting2 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry188 (73.2%)
↑2.7%prior 183
Wet30 (11.7%)
↑57.9%prior 19
Ice/frost19 (7.4%)
↓-13.6%prior 22
Gravel9 (3.5%)
↓-10.0%prior 10
Snow8 (3.1%)
↓-33.3%prior 12
Sand3 (1.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

An analysis of vehicles involved shows a shift in the top makes; while Ford was the most common make in 2016 with 75 vehicles, Chevrolet-branded vehicles became most frequent in 2017 with a combined 86 vehicles (up from 64). Examining the age distribution of persons involved in crashes reveals notable changes in certain cohorts. The number of persons aged 16-20 increased from 57 to 70, and the 35-44 age group saw a substantial rise from 54 to 86 persons. Conversely, the 26-34 age group saw a decrease from 80 to 61 persons involved.

Top Vehicle Makes (402 vehicles)

1
FORD65 (16.2%)
↓-13.3%prior 75
2
CHEV50 (12.4%)
↑72.4%prior 29
3
CHEVROLET36 (9%)
↑2.9%prior 35
4
DODG19 (4.7%)
↑58.3%prior 12
5
JEEP17 (4.2%)
↑30.8%prior 13
6
TOYT14 (3.5%)
↑133.3%prior 6
7
DODGE13 (3.2%)
↓-48.0%prior 25
8
KIA13 (3.2%)
↑62.5%prior 8
9
GMC11 (2.7%)
↓-15.4%prior 13
10
HONDA11 (2.7%)
↓-15.4%prior 13

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (289 persons with recorded sex)

Male160 (55.4%)
↓-16.2%prior 191
Female129 (44.6%)
↑44.9%prior 89

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 280
  • Total persons involved: 469
  • Total vehicles involved: 402

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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