Yearly Traffic Safety Analysis

271 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Buena Vista County recorded 271 total crashes, a 3.9% decrease from the 282 crashes reported in 2017. While overall crashes and fatalities (1 in each year) remained stable, the number of crashes resulting in serious injuries increased significantly, rising from 3 in 2017 to 8 in 2018.

271

▼ -3.9%was 282

Total Crash Events

1

Persons Killed

85

▼ -1.2%was 86

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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

Crash trends in Buena Vista County showed a slight decline from 2017 to 2018. Total crashes decreased by 3.9%, from 282 to 271. The number of people injured (85 in 2018 vs. 86 in 2017) and killed (1 in both years) remained nearly unchanged year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

1

Pedestrians Injured

Prior: 2↓-50.0%

1

Cyclists Injured

Prior: 2↓-50.0%

83

Motorists Injured

Prior: 82↑1.2%

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. In 2018, the peak day for crashes was Monday with 55 incidents, compared to Friday (53 incidents) in 2017. The peak hour also changed, moving from 3 p.m. in 2017 (25 crashes) to 7 a.m. in 2018 (26 crashes), indicating a shift from afternoon to morning commute times.

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

While the number of fatal crashes remained constant at one in both 2017 and 2018, the distribution of injury severity changed. Crashes resulting in serious injuries more than doubled, increasing from 3 in 2017 to 8 in 2018. Conversely, crashes involving possible injuries saw a substantial decrease, dropping from 43 incidents in 2017 to 28 in 2018.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
→0.0%prior 1
Serious Injury8serious injury crashes3%
↑166.7%prior 3
Minor Injury33minor injury crashes12.2%
↑10.0%prior 30
Possible Injury28possible injury crashes10.3%
↓-34.9%prior 43
No Injury201no injury crashes74.2%
↓-2.0%prior 205

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

The leading contributing factors for crashes shifted between 2017 and 2018. Collisions involving animals, the top factor in 2017 with 42 incidents, decreased by 21% in count to 33 incidents in 2018. "Driving too fast for conditions" saw a significant 86% increase in count, rising from 14 crashes in 2017 to 26 in 2018. Meanwhile, crashes attributed to "Failure to yield from a stop sign" fell by 45% in count, from 31 incidents to 17.

Officer-Reported Primary Contributing Cause

Other (explain in narrative): Other35 (12.9%)↑34.6%prior 26
Animal33 (12.2%)↓-21.4%prior 42
Driving too fast for conditions26 (9.6%)↑85.7%prior 14
Lost Control19 (7%)→0.0%prior 19
FTYROW: From stop sign17 (6.3%)↓-45.2%prior 31
Ran off road - straight14 (5.2%)→0.0%prior 14
Followed too close12 (4.4%)↓-25.0%prior 16
Ran off road - left10 (3.7%)↓-28.6%prior 14
Other (explain in narrative): No improper action8 (3%)
Improper Backing8 (3%)↑60.0%prior 5

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

Road & Environmental Conditions

Crash conditions showed a notable shift toward more hazardous environments in 2018 compared to 2017. The number of crashes occurring on icy or frosty road surfaces more than doubled, increasing from 14 to 36 incidents. Similarly, crashes in darkness on unlit roadways rose from 37 in 2017 to 60 in 2018. The proportion of crashes in clear weather and on dry roads decreased in 2018.

Weather

Clear184 (71.9%)
↓-6.6%prior 197
Cloudy25 (9.8%)
↓-16.7%prior 30
Rain14 (5.5%)
↑133.3%prior 6
Snow13 (5.1%)
↓-18.8%prior 16
Freezing rain/drizzle8 (3.1%)
Blowing Snow5 (2.0%)
Fog, smoke, smog5 (2.0%)
Sleet, hail1 (0.4%)
Severe Winds1 (0.4%)

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

Lighting

Daylight160 (62.3%)
↓-13.5%prior 185
Dark - roadway not lighted60 (23.3%)
↑62.2%prior 37
Dark - roadway lighted23 (8.9%)
↓-17.9%prior 28
Dawn7 (2.7%)
↑16.7%prior 6
Dusk6 (2.3%)
Dark - unknown roadway lighting1 (0.4%)

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

Road Surface

Dry158 (61.2%)
↓-15.5%prior 187
Ice/frost36 (14.0%)
↑157.1%prior 14
Wet30 (11.6%)
↑66.7%prior 18
Snow22 (8.5%)
↑4.8%prior 21
Gravel7 (2.7%)
↓-36.4%prior 11
Slush4 (1.6%)
Mud, dirt1 (0.4%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent year-over-year, with Ford and Chevrolet being the most frequent in both 2017 and 2018. An analysis of persons involved shows the 26-34 age group was the most represented in both periods, with 89 individuals in 2018 compared to 87 in 2017. The number of people aged 35-44 involved in crashes increased from 62 to 72, while involvement for the 16-20 and 55-64 age groups saw a slight decline.

Top Vehicle Makes (430 vehicles)

1
FORD82 (19.1%)
↑2.5%prior 80
2
CHEV61 (14.2%)
↓-1.6%prior 62
3
DODG24 (5.6%)
↓-17.2%prior 29
4
CHEVROLET23 (5.3%)
↓-39.5%prior 38
5
TOYT15 (3.5%)
→0.0%prior 15
6
GMC15 (3.5%)
↓-21.1%prior 19
7
HOND14 (3.3%)
↑75.0%prior 8
8
KIA14 (3.3%)
↑100.0%prior 7
9
BUIC13 (3%)
↓-23.5%prior 17
10
NR10 (2.3%)
↑25.0%prior 8

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

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

Sex Distribution (340 persons with recorded sex)

Male202 (59.4%)
↓-1.9%prior 206
Female138 (40.6%)
↓-8.6%prior 151

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: 271
  • Total persons involved: 514
  • Total vehicles involved: 430

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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