Yearly Traffic Safety Analysis

58 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Van Buren County, total vehicle crashes decreased by 34.8% from 89 in 2019 to 58 in 2020. This overall reduction in collisions was accompanied by a significant improvement in safety outcomes. The most notable year-over-year shift was the complete elimination of traffic fatalities, which dropped from 4 in 2019 to 0 in 2020.

58

▼ -34.8%was 89

Total Crash Events

0

▼ -100.0%was 4

Persons Killed

25

▼ -30.6%was 36

Persons Injured

0

▼ -100.0%was 4

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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Van Buren County shows a significant downward trend year-over-year. Total crashes fell from 89 to 58, a 34.8% decrease. Correspondingly, total injuries declined by 30.6% from 36 to 25, and total fatalities dropped from 4 to 0.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 4↓-100.0%

25

Motorists Injured

Prior: 33↓-24.2%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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 2020, the peak day for crashes was Tuesday with 11 incidents, a change from Friday, which saw 17 crashes in 2019. The peak hour also moved from the 7 a.m. morning commute in 2019 (11 crashes) to the 5 p.m. evening commute in 2020 (7 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased significantly, with fatal crashes dropping from 4 in 2019 to 0 in 2020. The number of serious injury crashes also fell from 2 to 1. While the total count of injury-related crashes decreased, their share of all crashes rose slightly from 33.7% in 2019 to 36.2% in 2020.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes1.7%
↓-50.0%prior 2
Minor Injury12minor injury crashes20.7%
↓-14.3%prior 14
Possible Injury8possible injury crashes13.8%
↓-42.9%prior 14
No Injury37no injury crashes63.8%
↓-32.7%prior 55

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Most severe injury per crash record

Top Contributing Factors

The top two contributing factors remained consistent across both years, with collisions involving an animal and drivers losing control leading the list. The count of crashes attributed to animals decreased from 22 in 2019 to 16 in 2020, a 27.3% reduction. Incidents where a driver lost control saw a larger drop, falling 36.8% from 19 crashes in 2019 to 12 in 2020.

Officer-Reported Primary Contributing Cause

Animal16 (27.6%)↓-27.3%prior 22
Lost Control12 (20.7%)↓-36.8%prior 19
Ran off road - straight7 (12.1%)↓-30.0%prior 10
Other (explain in narrative): Other5 (8.6%)
Driving too fast for conditions3 (5.2%)
Ran off road - right2 (3.4%)
Ran off road - left2 (3.4%)↓-60.0%prior 5
Passing: Other passing (explain in narrative)2 (3.4%)
Traveling wrong way or on wrong side of road1 (1.7%)
Driver Distraction: Adjusting devices (radio, climate)1 (1.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions were largely consistent year-over-year, with the majority of incidents in both periods occurring in daylight and on dry roads. In 2020, 58.6% of crashes occurred in daylight, compared to 56.2% in 2019. The proportion of crashes on non-dry surfaces like wet or snowy roads increased from a 24.7% share in 2019 to a 32.8% share in 2020, even as the absolute number of these incidents fell from 22 to 19.

Weather

Clear32 (62.7%)
↓-38.5%prior 52
Cloudy6 (11.8%)
↓-60.0%prior 15
Snow5 (9.8%)
Fog, smoke, smog3 (5.9%)
Rain3 (5.9%)
Freezing rain/drizzle2 (3.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Weather condition at time of crash

Lighting

Daylight34 (66.7%)
↓-32.0%prior 50
Dark - roadway not lighted11 (21.6%)
↓-45.0%prior 20
Dark - roadway lighted3 (5.9%)
Dawn3 (5.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Lighting condition field

Road Surface

Dry33 (63.5%)
↓-42.1%prior 57
Wet9 (17.3%)
→0.0%prior 9
Snow6 (11.5%)
→0.0%prior 6
Ice/frost2 (3.8%)
↓-60.0%prior 5
Slush1 (1.9%)
Gravel1 (1.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet vehicles were the most frequently involved makes in both years, with the count for both decreasing in 2020. The number of Ford vehicles in crashes fell from 30 to 18, while Chevrolet-branded vehicles (listed as 'CHEV' and 'CHEVROLET') collectively dropped from 26 to 17. The age distribution of people involved in crashes also shifted, with the 55-64 age group's involvement decreasing from 34 individuals to 18, while the 16-20 age group increased from 15 to 19.

Top Vehicle Makes (75 vehicles)

1
FORD18 (24%)
↓-40.0%prior 30
2
CHEVROLET9 (12%)
→0.0%prior 9
3
CHEV8 (10.7%)
↓-52.9%prior 17
4
JEEP5 (6.7%)
↓-16.7%prior 6
5
HONDA3 (4%)
6
TOYT2 (2.7%)
7
HOND2 (2.7%)
8
GMC2 (2.7%)
9
PONT2 (2.7%)
10
KENWORTH2 (2.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Vehicle unit records

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

Sex Distribution (72 persons with recorded sex)

Male49 (68.1%)
↓-16.9%prior 59
Female23 (31.9%)
↓-52.1%prior 48

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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: 2020-01-01 through 2020-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 58
  • Total persons involved: 116
  • Total vehicles involved: 75

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: 2020." Published September 9, 2026. Reporting period: 2020-01-01 to 2020-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2020-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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