Yearly Traffic Safety Analysis

62 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Winnebago County, a total of 62 traffic crashes were recorded in 2016, a slight increase from the 61 crashes documented in 2015. While the overall crash volume remained stable, the number of crashes attributed to Driving Under the Influence (DUI) decreased by 50%, from 6 incidents in 2015 to 3 in 2016. Conversely, crashes related to stop sign violations more than doubled year-over-year.

62

▲ 1.6%was 61

Total Crash Events

0

Persons Killed

27

▼ -6.9%was 29

Persons Injured

0

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

Overall crash trends in Winnebago County were relatively stable year-over-year, with total collisions rising by a single incident from 61 in 2015 to 62 in 2016. The number of people injured in these crashes saw a slight decline of 6.9%, from 29 in the prior year to 27 in the current year. There were no fatalities recorded in either period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 1→0.0%

26

Motorists Injured

Prior: 28↓-7.1%

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, Monday was the peak day for crashes with 13 incidents, a change from Thursday in 2015, which saw 15 crashes. Similarly, the peak hour for collisions moved earlier in the day, from 7 p.m. (6 crashes) in 2015 to 3 p.m. (7 crashes) in 2016.

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 showed a notable shift despite total injuries decreasing slightly from 29 to 27. There were no fatal crashes in either 2015 or 2016. The number of crashes resulting in serious injuries remained unchanged at 4. However, the count of minor injury crashes doubled from 5 in 2015 to 10 in 2016, while possible injury crashes decreased from 16 to 9.

Outcome by Severity (Crash Events)

Serious Injury4serious injury crashes6.5%
→0.0%prior 4
Minor Injury10minor injury crashes16.1%
↑100.0%prior 5
Possible Injury9possible injury crashes14.5%
↓-43.8%prior 16
No Injury39no injury crashes62.9%
↑8.3%prior 36

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

The leading contributing factors for crashes changed significantly year-over-year. In 2016, failure to yield from a stop sign became the top factor with 7 crashes, more than doubling from 3 in 2015. Running a stop sign also doubled from 3 to 6 incidents. Conversely, crashes attributed to reckless or erratic driving, the top factor in 2015 with 7 incidents, decreased to 4 incidents in 2016.

Officer-Reported Primary Contributing Cause

FTYROW: From stop sign7 (11.3%)
Ran Stop Sign6 (9.7%)
Lost Control6 (9.7%)→0.0%prior 6
Operating vehicle in an reckless, erratic, careless, negligent manner4 (6.5%)↓-42.9%prior 7
Passing: Other passing (explain in narrative)3 (4.8%)
Ran off road - straight3 (4.8%)
Driver Distraction: Other interior distraction3 (4.8%)
Followed too close3 (4.8%)
Ran off road - left3 (4.8%)
Driving too fast for conditions3 (4.8%)

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

Road & Environmental Conditions

The conditions under which crashes occurred remained largely consistent between 2015 and 2016. Crashes on dry roads accounted for approximately 66% of all incidents in both years (40 in 2015 and 41 in 2016). Similarly, collisions in clear weather represented the majority, with 39 incidents in 2015 and 38 in 2016. There were no significant year-over-year shifts in the proportions of crashes occurring during adverse weather, lighting, or road surface conditions.

Weather

Clear38 (62.3%)
↓-2.6%prior 39
Cloudy12 (19.7%)
↑71.4%prior 7
Rain3 (4.9%)
Blowing Snow3 (4.9%)
Snow3 (4.9%)
Fog, smoke, smog2 (3.3%)

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

Lighting

Daylight40 (66.7%)
↑14.3%prior 35
Dark - roadway not lighted12 (20.0%)
↓-7.7%prior 13
Dark - roadway lighted7 (11.7%)
↑40.0%prior 5
Dusk1 (1.7%)

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

Road Surface

Dry41 (67.2%)
↑2.5%prior 40
Ice/frost6 (9.8%)
↑20.0%prior 5
Snow5 (8.2%)
Wet5 (8.2%)
Gravel3 (4.9%)
↓-40.0%prior 5
Mud, dirt1 (1.6%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes showed some changes between periods. Ford vehicles were consistently a top make, involved in 22 crashes in both 2015 and 2016. The involvement of Chevrolet vehicles (including CHEV and CHEVROLET) increased from 20 to 26. The age distribution of people involved in crashes shifted notably, with a large increase in the 45-54 age group (from 10 to 23 people) and a marked decrease in the 26-34 and 35-44 age groups.

Top Vehicle Makes (105 vehicles)

1
FORD22 (21%)
→0.0%prior 22
2
CHEV15 (14.3%)
↑50.0%prior 10
3
CHEVROLET11 (10.5%)
↑10.0%prior 10
4
DODGE7 (6.7%)
5
BUIC4 (3.8%)
6
PONT4 (3.8%)
7
BUICK3 (2.9%)
8
CHRYSLER3 (2.9%)
9
TOYOTA3 (2.9%)
10
DODG3 (2.9%)

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

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

Sex Distribution (78 persons with recorded sex)

Male43 (55.1%)
↓-6.5%prior 46
Female35 (44.9%)
↑12.9%prior 31

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: 62
  • Total persons involved: 118
  • Total vehicles involved: 105

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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