Yearly Traffic Safety Analysis

71 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

Total crashes in Van Buren County decreased from 82 in 2017 to 71 in 2018, a 13.4% reduction. Alongside the drop in overall collisions, the most significant change was the elimination of traffic fatalities, with zero recorded in 2018 compared to one in the prior year. Total injuries also declined from 41 to 31.

71

▼ -13.4%was 82

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

31

▼ -24.4%was 41

Persons Injured

0

▼ -100.0%was 1

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 · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic collisions in Van Buren County showed a downward trend year-over-year. Total crashes fell by 13.4%, from 82 in 2017 to 71 in 2018. This decrease was also reflected in crash outcomes, as total injuries dropped from 41 to 31 and fatalities were reduced from one to zero.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 1↓-100.0%

31

Motorists Injured

Prior: 39↓-20.5%

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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. The peak day for collisions moved from Wednesday and Friday (15 crashes each) in 2017 to Saturday (16 crashes) in 2018. While the 5 p.m. hour remained a peak time for crashes, it saw a higher concentration of incidents in 2018, with 11 crashes compared to 7 in the prior year.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Overall crash severity decreased from 2017 to 2018, with fatal crashes dropping from one to zero. The proportion of crashes resulting in any level of injury was nearly stable, at 39.4% in 2018 versus 40.2% in 2017. However, the distribution of injuries changed, with the share of minor injury crashes increasing from 12.2% to 22.5% of all incidents, while the share of possible injury crashes decreased from 22.0% to 12.7%.

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes4.2%
↓-25.0%prior 4
Minor Injury16minor injury crashes22.5%
↑60.0%prior 10
Possible Injury9possible injury crashes12.7%
↓-50.0%prior 18
No Injury43no injury crashes60.6%
↓-12.2%prior 49

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an "Animal" remained the top contributing factor in both periods, though the count of such incidents decreased from 29 in 2017 to 20 in 2018. "Lost Control" was the second-most cited factor, with its count increasing from 10 to 14 crashes year-over-year. The third-ranked factor, "Ran off road - straight," remained stable with 8 incidents reported in both years.

Officer-Reported Primary Contributing Cause

Animal20 (28.2%)↓-31.0%prior 29
Lost Control14 (19.7%)↑40.0%prior 10
Ran off road - straight8 (11.3%)→0.0%prior 8
Driving too fast for conditions6 (8.5%)↑20.0%prior 5
Ran off road - left5 (7%)
Operating vehicle in an reckless, erratic, careless, negligent manner3 (4.2%)
FTYROW: From yield sign2 (2.8%)
Passing: Other passing (explain in narrative)2 (2.8%)
Driver Distraction: Other interior distraction2 (2.8%)
Other (explain in narrative): Other1 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While weather and road surface conditions at the time of crashes were proportionally similar year-over-year, there was a notable shift in lighting conditions. The share of collisions occurring in daylight decreased from 54.9% of all crashes in 2017 to 42.3% in 2018. Conversely, the proportion of crashes happening on unlit dark roadways increased from 18.3% in 2017 to 31.0% in 2018.

Weather

Clear41 (67.2%)
↓-16.3%prior 49
Cloudy12 (19.7%)
↓-7.7%prior 13
Snow4 (6.6%)
Rain2 (3.3%)
↓-60.0%prior 5
Freezing rain/drizzle1 (1.6%)
Fog, smoke, smog1 (1.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Weather condition at time of crash

Lighting

Daylight30 (49.2%)
↓-33.3%prior 45
Dark - roadway not lighted22 (36.1%)
↑46.7%prior 15
Dark - roadway lighted5 (8.2%)
Dawn2 (3.3%)
Dusk2 (3.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Lighting condition field

Road Surface

Dry46 (75.4%)
↓-11.5%prior 52
Snow5 (8.2%)
Wet4 (6.6%)
↓-55.6%prior 9
Gravel3 (4.9%)
↓-40.0%prior 5
Ice/frost1 (1.6%)
Mud, dirt1 (1.6%)
Slush1 (1.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Road surface condition field

Vehicles & Demographics

The types of vehicles involved in crashes remained consistent, with Chevrolet and Ford continuing as the top two most frequently involved makes in both 2017 and 2018. The age demographics of persons involved in crashes also showed stability. The proportional representation of different age groups, such as the 16-20 and 45-54 age brackets, experienced only minor changes between the two years.

Top Vehicle Makes (83 vehicles)

1
CHEV20 (24.1%)
↑25.0%prior 16
2
FORD16 (19.3%)
↑23.1%prior 13
3
DODG6 (7.2%)
4
GMC5 (6%)
5
CHEVROLET4 (4.8%)
↓-66.7%prior 12
6
PETERBILT3 (3.6%)
7
CHRY3 (3.6%)
8
LINC3 (3.6%)
9
HD3 (3.6%)
10
TOYT2 (2.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records

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

Sex Distribution (59 persons with recorded sex)

Male43 (72.9%)
↑19.4%prior 36
Female16 (27.1%)
↓-50.0%prior 32

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 71
  • Total persons involved: 103
  • Total vehicles involved: 83

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: 2018." Published September 9, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2018-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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