Yearly Traffic Safety Analysis

374 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Iowa County recorded 374 total crashes, a 7.7% decrease from the 405 crashes documented in 2015. Despite the overall reduction in collisions and a 14.3% drop in total injuries from 154 to 132, the number of fatalities increased significantly. There were 8 fatalities in 2016, a 60% rise from the 5 fatalities reported in the prior year.

374

▼ -7.7%was 405

Total Crash Events

8

▲ 60.0%was 5

Persons Killed

132

▼ -14.3%was 154

Persons Injured

5

Fatal Crash Events

Note: "Persons Killed" (8) 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

The overall trend in traffic crashes in Iowa County was downward from 2015 to 2016, with total incidents falling by 7.7% from 405 to 374. The number of people injured also decreased by 14.3%, from 154 to 132. In contrast to this downward trend, fatalities increased from 5 in 2015 to 8 in 2016.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

8

Motorists Killed

Prior: 5↑60.0%

1

Pedestrians Injured

Prior: 0→%

131

Motorists Injured

Prior: 150↓-12.7%

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

Temporal crash patterns revealed a significant shift in the peak hour for incidents between the two years. In 2016, the most crashes occurred at 6 PM (36 crashes), a change from the 6 AM peak (32 crashes) observed in 2015. The peak day for crashes remained on the weekend, with Saturday being the highest in 2015 (63 crashes) and tied with Sunday for the highest in 2016 (62 crashes each).

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

While the total number of fatal crashes was unchanged at 5 for both 2015 and 2016, the number of people killed in those crashes increased from 5 to 8. This resulted in a higher fatal crash rate, which rose from 1.23 to 1.34 per 100 crashes. The proportion of crashes involving serious injuries declined from 2.5% to 2.1% year-over-year, while crashes with possible injuries increased as a share of the total from 10.6% to 12.8%.

Severity is per crash event (most severe injury). 5 fatal crash events resulted in 8 persons killed.

Outcome by Severity (Crash Events)

Fatal5fatal crashes1.3%
→0.0%prior 5
Serious Injury8serious injury crashes2.1%
↓-20.0%prior 10
Minor Injury31minor injury crashes8.3%
↓-8.8%prior 34
Possible Injury48possible injury crashes12.8%
↑11.6%prior 43
No Injury282no injury crashes75.4%
↓-9.9%prior 313

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 were consistent across both periods, with "Animal" being the top-ranked cause in 2015 and 2016. The count of crashes attributed to animals decreased by 17.9%, from 145 incidents in 2015 to 119 in 2016. Conversely, crashes where a driver "Lost Control" increased in count from 44 to 49, and incidents of "Followed too close" rose from 15 to 23.

Officer-Reported Primary Contributing Cause

Animal119 (31.8%)↓-17.9%prior 145
Lost Control49 (13.1%)↑11.4%prior 44
Ran off road - straight30 (8%)↓-23.1%prior 39
Driving too fast for conditions29 (7.8%)↑7.4%prior 27
Followed too close23 (6.1%)↑53.3%prior 15
FTYROW: From stop sign13 (3.5%)↓-13.3%prior 15
Other (explain in narrative): Other11 (2.9%)↑37.5%prior 8
Ran off road - left11 (2.9%)↓-26.7%prior 15
Ran Stop Sign6 (1.6%)↓-33.3%prior 9
Driver Distraction: Exterior distraction6 (1.6%)

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 distribution of crashes by environmental conditions showed some shifts between 2015 and 2016. The number of crashes on wet roads decreased from 26 to 16, and incidents during rainy conditions fell from 21 to 7. Conversely, crashes in snowy weather increased from 23 to 31, and those on dry road surfaces increased from 177 to 186. Crashes in daylight conditions remained relatively stable at 165 incidents in 2016 compared to 167 in 2015.

Weather

Clear136 (51.3%)
↓-4.9%prior 143
Cloudy80 (30.2%)
↑27.0%prior 63
Snow31 (11.7%)
↑34.8%prior 23
Rain7 (2.6%)
↓-66.7%prior 21
Blowing Snow3 (1.1%)
↓-50.0%prior 6
Severe Winds3 (1.1%)
Fog, smoke, smog2 (0.8%)
↓-60.0%prior 5
Other (explain in narrative)2 (0.8%)
Sleet, hail1 (0.4%)

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

Lighting

Daylight165 (62.3%)
↓-1.2%prior 167
Dark - roadway not lighted70 (26.4%)
↓-10.3%prior 78
Dawn13 (4.9%)
↑44.4%prior 9
Dark - roadway lighted12 (4.5%)
↑9.1%prior 11
Dusk4 (1.5%)
↓-42.9%prior 7
Dark - unknown roadway lighting1 (0.4%)

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

Road Surface

Dry186 (70.2%)
↑5.1%prior 177
Snow28 (10.6%)
↑27.3%prior 22
Ice/frost16 (6.0%)
↓-40.7%prior 27
Wet16 (6.0%)
↓-38.5%prior 26
Gravel13 (4.9%)
↓-7.1%prior 14
Slush6 (2.3%)

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

Vehicles & Demographics

A notable shift occurred in the top vehicle makes involved in crashes, with Chevrolet (73 vehicles) surpassing Ford (68 vehicles) as the most frequent make in 2016; in 2015, Ford led with 110 vehicles. An analysis of persons involved in crashes shows a change in age demographics. The share of individuals aged 16-20 increased from 10.3% to 12.7% of all persons involved, while the representation of those aged 65 and older decreased from 10.3% to 8.1%.

Top Vehicle Makes (524 vehicles)

1
CHEVROLET73 (13.9%)
↑15.9%prior 63
2
FORD68 (13%)
↓-38.2%prior 110
3
CHEV47 (9%)
↑11.9%prior 42
4
DODGE24 (4.6%)
↓-11.1%prior 27
5
FREIGHTLINER22 (4.2%)
↑29.4%prior 17
6
GMC18 (3.4%)
↑20.0%prior 15
7
TOYOTA15 (2.9%)
↑7.1%prior 14
8
DODG14 (2.7%)
→0.0%prior 14
9
PONTIAC13 (2.5%)
↑30.0%prior 10
10
HOND12 (2.3%)
↑50.0%prior 8

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

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

Sex Distribution (393 persons with recorded sex)

Male242 (61.6%)
↓-21.7%prior 309
Female151 (38.4%)
↓-17.5%prior 183

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: 374
  • Total persons involved: 615
  • Total vehicles involved: 524

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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