Yearly Traffic Safety Analysis

49 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Wayne County, total traffic crashes increased by 6.5%, from 46 incidents in 2015 to 49 in 2016. During this period, the number of reported injuries rose from 24 to 28. The most significant year-over-year change was the reduction in traffic fatalities, which dropped from one in the prior year to zero in the current year.

49

6.5%was 46

Total Crash Events

0

-100.0%was 1

Persons Killed

28

16.7%was 24

Persons Injured

0

-100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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

The overall trend in Wayne County shows a slight increase in traffic incidents year-over-year. Total crashes rose by 6.5% from 46 to 49, and total injuries increased by 16.7% from 24 to 28. In a positive development, there were no fatal crashes recorded in 2016, compared to one in 2015.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 1-100.0%

28

Motorists Injured

Prior: 2416.7%

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 timing of crashes shifted between the two periods. In 2016, the peak day for crashes was Wednesday with 12 incidents, a change from 2015 when Friday was the peak day with 11 incidents. Similarly, the peak hour for crashes moved two hours earlier, from the 6 p.m. hour in 2015 to the 4 p.m. hour in 2016, with each period's peak hour recording 6 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 patterns shifted year-over-year, with fatal crashes being eliminated in 2016, down from one fatal incident in 2015. The proportion of crashes resulting in any type of injury increased from 32.6% to 42.8%. This was driven by a more than twofold increase in minor injury crashes, which rose from 5 to 11, while serious injury crashes decreased from 2 to 1. Consequently, the share of no-injury crashes fell from 65.2% to 57.1%.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes2%
-50.0%prior 2
Minor Injury11minor injury crashes22.4%
120.0%prior 5
Possible Injury9possible injury crashes18.4%
12.5%prior 8
No Injury28no injury crashes57.1%
-6.7%prior 30

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 with animals remained a primary contributing factor in both periods, though the count of such incidents decreased from 16 in 2015 to 9 in 2016. A notable change was the rise in crashes attributed to "Driving too fast for conditions," which increased from 1 incident in 2015 to 6 in 2016, becoming the second-most cited factor. Crashes involving "Lost Control" and "Ran off road - straight" also saw an increase in count, rising from 3 to 5 incidents for each factor.

Officer-Reported Primary Contributing Cause

Animal9 (18.4%)-43.8%prior 16
Driving too fast for conditions6 (12.2%)
Lost Control5 (10.2%)
Ran off road - straight5 (10.2%)
Other (explain in narrative): Other3 (6.1%)
Ran off road - left3 (6.1%)
FTYROW: Other (explain in narrative)2 (4.1%)
Driver Distraction: Manual operation of an electronic communication device2 (4.1%)
Driver Distraction: Other interior distraction2 (4.1%)
FTYROW: Making left turn1 (2%)

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

Road & Environmental Conditions

While the number of crashes on dry roads was identical in both periods at 26 incidents, there was a significant increase in crashes on gravel roads, which more than tripled from 3 in 2015 to 10 in 2016. The proportion of crashes occurring in clear weather also grew substantially, from 47.8% of all crashes in 2015 to 73.5% in 2016. Daylight conditions continued to be the most common lighting environment, with its share of crashes rising from 52.2% to 59.2%.

Weather

Clear36 (80.0%)
63.6%prior 22
Cloudy3 (6.7%)
-70.0%prior 10
Freezing rain/drizzle2 (4.4%)
Snow2 (4.4%)
Rain1 (2.2%)
Blowing Snow1 (2.2%)

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

Lighting

Daylight29 (64.4%)
20.8%prior 24
Dark - roadway not lighted11 (24.4%)
0.0%prior 11
Dark - roadway lighted2 (4.4%)
Dusk2 (4.4%)
Dawn1 (2.2%)

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

Road Surface

Dry26 (57.8%)
0.0%prior 26
Gravel10 (22.2%)
Snow3 (6.7%)
Wet3 (6.7%)
-40.0%prior 5
Ice/frost2 (4.4%)
Slush1 (2.2%)

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

Vehicles & Demographics

Ford and Chevrolet vehicles were the most frequently involved in crashes during both periods, with Chevrolet's involvement increasing from 16 to 19 vehicles and Ford's decreasing from 15 to 14. The age demographics of individuals involved in crashes shifted notably; the 16-20 age group became the largest cohort in 2016 with 23 individuals, up from 16 in 2015. Conversely, the 55-64 age group, which was the largest in 2015 with 18 individuals, saw its count drop to 11 in 2016.

Top Vehicle Makes (66 vehicles)

1
FORD14 (21.2%)
-6.7%prior 15
2
CHEV10 (15.2%)
-9.1%prior 11
3
CHEVROLET9 (13.6%)
80.0%prior 5
4
BUIC3 (4.5%)
5
DODG3 (4.5%)
6
PETERBILT3 (4.5%)
7
TOYOTA2 (3%)
8
JEEP2 (3%)
9
DODGE2 (3%)
10
BUICK2 (3%)

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

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

Sex Distribution (50 persons with recorded sex)

Male31 (62.0%)
3.3%prior 30
Female19 (38.0%)
-29.6%prior 27

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: 49
  • Total persons involved: 84
  • Total vehicles involved: 66

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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Wayne County, IA Crash Report — 2016 | ThatCarHitMe.com