Yearly Traffic Safety Analysis

292 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Buena Vista County recorded 292 total crashes, a 21.1% decrease from the 370 crashes reported in 2015. While overall collisions, injuries (113, down from 124), and fatalities (2, down from 3) all declined, crashes involving driving under the influence (DUI) increased from 8 in 2015 to 11 in 2016, a 37.5% rise.

292

▼ -21.1%was 370

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

113

▼ -8.9%was 124

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 crash trends in Buena Vista County showed a significant year-over-year decrease. Total crashes fell by 21.1%, from 370 in 2015 to 292 in 2016. This downward trend was also reflected in total injuries, which decreased by 8.9% from 124 to 113, and fatalities, which fell from 3 to 2.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

2

Pedestrians Injured

Prior: 2→0.0%

1

Cyclists Injured

Prior: 3↓-66.7%

110

Motorists Injured

Prior: 119↓-7.6%

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 showed both consistency and shifts between the two periods. Friday remained the peak day for crashes in both 2015 (75 crashes) and 2016 (61 crashes). However, the peak hour for collisions shifted one hour later, from the 5 PM hour in 2015 (33 crashes) to the 6 PM hour in 2016 (24 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

Although the absolute number of fatal crashes was unchanged at 2 in both years, the fatal crash rate per 100 crashes increased from 0.54 in 2015 to 0.68 in 2016 due to the lower total number of collisions. The proportion of crashes resulting in any injury (fatal, serious, minor, or possible) rose from 25.7% in 2015 to 30.8% in 2016, with the share of serious injury crashes increasing from 0.3% to 1.0% of all incidents.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.7%
→0.0%prior 2
Serious Injury3serious injury crashes1%
↑200.0%prior 1
Minor Injury34minor injury crashes11.6%
↑13.3%prior 30
Possible Injury51possible injury crashes17.5%
↓-17.7%prior 62
No Injury202no injury crashes69.2%
↓-26.5%prior 275

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 an animal remained the top contributing factor in both periods, though the count decreased by 36.7% from 60 crashes in 2015 to 38 in 2016. A notable shift occurred with 'Failure to Yield Right of Way from a stop sign,' which increased in count by 35% from 20 to 27 incidents, moving it from the fifth to the second most common factor. Conversely, crashes attributed to 'Lost Control' saw a 40% count reduction (from 30 to 18), and 'Driving too fast for conditions' decreased by 25% (from 24 to 18).

Officer-Reported Primary Contributing Cause

Animal38 (13%)↓-36.7%prior 60
FTYROW: From stop sign27 (9.2%)↑35.0%prior 20
Other (explain in narrative): Other21 (7.2%)↓-58.8%prior 51
Lost Control18 (6.2%)↓-40.0%prior 30
Driving too fast for conditions18 (6.2%)↓-25.0%prior 24
Followed too close17 (5.8%)↑6.3%prior 16
Ran off road - straight12 (4.1%)↓-7.7%prior 13
Ran off road - left10 (3.4%)↑11.1%prior 9
Driver Distraction: Other interior distraction10 (3.4%)↑42.9%prior 7
FTYROW: Making left turn10 (3.4%)↓-23.1%prior 13

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 proportion of crashes occurring under adverse conditions decreased year-over-year. In 2016, 31.2% of crashes occurred in non-clear weather (such as cloudy, snow, or rain), down from 39.7% in 2015. Similarly, collisions on non-dry road surfaces like wet, snow, or ice accounted for 38.4% of crashes in 2016, a decrease from 43.5% in 2015. The share of crashes happening in daylight conditions saw a slight decrease from 63.5% in 2015 to 59.6% in 2016.

Weather

Clear201 (73.6%)
↓-9.9%prior 223
Cloudy36 (13.2%)
↓-16.3%prior 43
Snow17 (6.2%)
↓-37.0%prior 27
Rain10 (3.7%)
↓-65.5%prior 29
Fog, smoke, smog6 (2.2%)
Blowing Snow2 (0.7%)
Severe Winds1 (0.4%)

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

Lighting

Daylight174 (64.4%)
↓-26.0%prior 235
Dark - roadway not lighted54 (20.0%)
↓-1.8%prior 55
Dark - roadway lighted29 (10.7%)
↑20.8%prior 24
Dusk8 (3.0%)
→0.0%prior 8
Dawn4 (1.5%)
↓-63.6%prior 11
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

Dry180 (65.9%)
↓-13.9%prior 209
Wet31 (11.4%)
↓-26.2%prior 42
Snow24 (8.8%)
↓-36.8%prior 38
Ice/frost17 (6.2%)
↓-41.4%prior 29
Gravel13 (4.8%)
↓-7.1%prior 14
Slush5 (1.8%)
Sand3 (1.1%)

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

Vehicles & Demographics

The top three vehicle makes involved in crashes remained consistent across both years: Chevrolet, Ford, and Dodge. In 2016, Chevrolet vehicles were involved in 96 crashes, Ford in 70, and Dodge in 50, compared to 127, 86, and 49, respectively, in 2015. When examining the age of persons involved in collisions, the 26-34 age group was the most frequently represented in both 2016 (91 persons) and 2015 (114 persons). The total number of individuals involved in crashes decreased from 718 to 547, with reductions seen across most age brackets.

Top Vehicle Makes (487 vehicles)

1
FORD70 (14.4%)
↓-18.6%prior 86
2
CHEVROLET52 (10.7%)
↑15.6%prior 45
3
CHEV44 (9%)
↓-46.3%prior 82
4
DODG30 (6.2%)
↑3.4%prior 29
5
GMC25 (5.1%)
↑4.2%prior 24
6
DODGE20 (4.1%)
→0.0%prior 20
7
HONDA16 (3.3%)
↑60.0%prior 10
8
PONTIAC12 (2.5%)
↑100.0%prior 6
9
CHRY12 (2.5%)
↓-29.4%prior 17
10
NISSAN11 (2.3%)

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

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

Sex Distribution (357 persons with recorded sex)

Male209 (58.5%)
↓-35.5%prior 324
Female148 (41.5%)
↓-27.8%prior 205

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: 292
  • Total persons involved: 547
  • Total vehicles involved: 487

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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