Yearly Traffic Safety Analysis

80 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Van Buren County, total crashes decreased slightly from 82 in 2015 to 80 in 2016, a change of approximately 2.4%. While the overall crash volume was stable, the most significant year-over-year change was the occurrence of 2 fatal crashes in 2016, resulting in 2 fatalities, compared to zero fatal crashes and fatalities in the prior year.

80

▼ -2.4%was 82

Total Crash Events

2

Persons Killed

32

▼ -17.9%was 39

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

The overall crash trend in Van Buren County showed relative stability year-over-year, with total crashes decreasing by 2.4% from 82 in 2015 to 80 in 2016. The number of injuries also saw a decrease, falling from 39 to 32. However, this period saw the emergence of fatal crashes, with 2 fatalities recorded in 2016 compared to none in the previous year.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 0→%

32

Motorists Injured

Prior: 36↓-11.1%

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

Temporal patterns of crashes shifted between the two periods. In 2016, the peak day for crashes was Monday with 18 incidents, a change from 2015 when Saturday was the peak day with 20 crashes. The peak hour for crashes also moved from a dual peak at 10 a.m. and 3 p.m. (7 crashes each) in 2015 to a single peak at 4 p.m. (8 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

The severity of crashes changed notably year-over-year. In 2016, there were 2 fatal crashes, accounting for 2.5% of all incidents, whereas there were no fatal crashes in 2015. The proportion of serious injury crashes decreased from 9.8% (8 crashes) in 2015 to 3.8% (3 crashes) in 2016. Conversely, the share of minor and possible injury crashes increased, rising from a combined 24.4% in 2015 to 31.3% in 2016.

Outcome by Severity (Crash Events)

Fatal2fatal crashes2.5%
Serious Injury3serious injury crashes3.8%
↓-62.5%prior 8
Minor Injury13minor injury crashes16.3%
↑30.0%prior 10
Possible Injury12possible injury crashes15%
↑20.0%prior 10
No Injury50no injury crashes62.5%
↓-7.4%prior 54

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 remained consistent, though their counts shifted. "Animal" was the top factor in both years, but its count decreased from 22 crashes in 2015 to 17 in 2016. Similarly, crashes attributed to "Lost Control" fell from 17 to 15. In contrast, incidents involving "Ran off road - straight" increased, rising from 7 crashes in 2015 to become the third-most common factor in 2016 with 12 crashes.

Officer-Reported Primary Contributing Cause

Animal17 (21.3%)↓-22.7%prior 22
Lost Control15 (18.8%)↓-11.8%prior 17
Ran off road - straight12 (15%)↑71.4%prior 7
Driving too fast for conditions8 (10%)
Followed too close3 (3.8%)
FTYROW: From stop sign3 (3.8%)
FTYROW: Making left turn3 (3.8%)
Ran off road - left3 (3.8%)
Other (explain in narrative): Other2 (2.5%)
FTYROW: From parked position1 (1.3%)

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 year-over-year. Crashes during daylight hours increased as a proportion of the total, from 51.2% in 2015 to 61.3% in 2016. While clear weather remained the most common condition, its share of crashes decreased from 70.7% to 58.8%. Notably, the proportion of crashes occurring in adverse weather (rain, snow, freezing rain) increased from 6.1% (5 crashes) in 2015 to 13.8% (11 crashes) in 2016.

Weather

Clear47 (64.4%)
↓-19.0%prior 58
Cloudy15 (20.5%)
↑50.0%prior 10
Rain4 (5.5%)
Snow4 (5.5%)
Freezing rain/drizzle2 (2.7%)
Fog, smoke, smog1 (1.4%)

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

Lighting

Daylight49 (67.1%)
↑16.7%prior 42
Dark - roadway not lighted16 (21.9%)
↓-20.0%prior 20
Dusk4 (5.5%)
↓-50.0%prior 8
Dawn2 (2.7%)
Dark - roadway lighted1 (1.4%)
Dark - unknown roadway lighting1 (1.4%)

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

Road Surface

Dry51 (70.8%)
↓-8.9%prior 56
Gravel7 (9.7%)
↑40.0%prior 5
Wet6 (8.3%)
Snow5 (6.9%)
↓-16.7%prior 6
Ice/frost3 (4.2%)

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

Vehicles & Demographics

Analysis of vehicles and persons involved shows shifts in makes and age demographics. While Ford was the top individual make in both years, its involvement decreased from 23 vehicles in 2015 to 18 in 2016. When combining name variations (CHEV/CHEVROLET), Chevrolet-brand vehicles were involved in 25 crashes in 2016, up from 20 in the prior year. Regarding persons involved, there was a notable decrease in the 65+ age group, which fell from 26 individuals in 2015 to 14 in 2016. Conversely, the 26-34 age group saw an increase in involvement from 17 to 23 persons.

Top Vehicle Makes (104 vehicles)

1
FORD18 (17.3%)
↓-21.7%prior 23
2
CHEVROLET13 (12.5%)
↑30.0%prior 10
3
CHEV12 (11.5%)
↑20.0%prior 10
4
DODG9 (8.7%)
5
PONT6 (5.8%)
6
DODGE6 (5.8%)
↑20.0%prior 5
7
TOYOTA4 (3.8%)
↓-20.0%prior 5
8
FREIGHTLINER3 (2.9%)
9
BUICK3 (2.9%)
10
TOYT3 (2.9%)
↓-40.0%prior 5

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

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

Sex Distribution (82 persons with recorded sex)

Male47 (57.3%)
↓-28.8%prior 66
Female35 (42.7%)
↑25.0%prior 28

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: 80
  • Total persons involved: 131
  • Total vehicles involved: 104

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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