Yearly Traffic Safety Analysis

316 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Buchanan County, total traffic crashes remained stable, with 316 incidents in 2018 compared to 320 in 2017, a decrease of 1.3%. While fatalities fell from three to one, total injuries rose by 25% from 75 to 94. The most significant year-over-year change was a 67% reduction in crashes involving a driver under the influence (DUI), which dropped from 12 incidents in 2017 to 4 in 2018.

316

▼ -1.3%was 320

Total Crash Events

1

▼ -66.7%was 3

Persons Killed

94

▲ 25.3%was 75

Persons Injured

1

▼ -66.7%was 3

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

Overall crash volume in Buchanan County saw a slight decline of 1.3% from 2017 to 2018. However, the severity of outcomes shifted, with total injuries increasing by 25.3% from 75 to 94, even as the number of fatalities decreased from 3 to 1. This suggests that while slightly fewer crashes occurred, they resulted in a higher number of non-fatal injuries.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 3↓-100.0%

1

Pedestrians Injured

Prior: 1→0.0%

2

Cyclists Injured

Prior: 1↑100.0%

91

Motorists Injured

Prior: 68↑33.8%

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 temporal patterns of crashes showed some shifts between the two periods. The peak day for crashes moved from Friday (53 incidents) in 2017 to Wednesday (54 incidents) in 2018. While the 4 p.m. hour was a peak time in both years, it became more pronounced in 2018 with 26 crashes, compared to 23 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

The severity of crashes shifted year-over-year. Fatal crashes decreased from 3 in 2017 to 1 in 2018, and serious injury crashes also saw a small reduction from 8 to 6. In contrast, crashes resulting in minor injuries more than doubled, increasing from 15 incidents (4.7% of all crashes) in 2017 to 32 (10.1% of all crashes) in 2018. The share of crashes with no injuries decreased from 81.3% to 76.3%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
↓-66.7%prior 3
Serious Injury6serious injury crashes1.9%
↓-25.0%prior 8
Minor Injury32minor injury crashes10.1%
↑113.3%prior 15
Possible Injury36possible injury crashes11.4%
↑5.9%prior 34
No Injury241no injury crashes76.3%
↓-7.3%prior 260

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 with animals were the leading contributing factor in both 2017 and 2018, with nearly identical counts of 108 and 107, respectively. There were significant changes in other top factors; crashes attributed to 'Driving too fast for conditions' more than doubled in count from 11 to 25, while 'Lost Control' incidents increased by 41% from 22 to 31. Conversely, crashes caused by 'Followed too close' saw a 42% decrease in count, falling from 26 incidents in 2017 to 15 in 2018.

Officer-Reported Primary Contributing Cause

Animal107 (33.9%)↓-0.9%prior 108
Lost Control31 (9.8%)↑40.9%prior 22
Driving too fast for conditions25 (7.9%)↑127.3%prior 11
Other (explain in narrative): Other17 (5.4%)↑13.3%prior 15
Ran off road - straight16 (5.1%)↑14.3%prior 14
Ran off road - left15 (4.7%)↑36.4%prior 11
Followed too close15 (4.7%)↓-42.3%prior 26
FTYROW: From stop sign10 (3.2%)→0.0%prior 10
Ran Stop Sign9 (2.8%)↑80.0%prior 5
Driver Distraction: Other interior distraction9 (2.8%)↑80.0%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

The proportion of crashes occurring under adverse conditions increased from 2017 to 2018. Crashes on adverse road surfaces like snow, ice, or wet pavement rose from 42 incidents in 2017 to 66 in 2018. Similarly, crashes in adverse weather such as snow or rain increased from 20 to 39 incidents year-over-year. The share of crashes occurring in daylight and on dry roads decreased proportionally.

Weather

Clear127 (58.0%)
↓-11.8%prior 144
Cloudy49 (22.4%)
↓-19.7%prior 61
Snow16 (7.3%)
↑33.3%prior 12
Rain9 (4.1%)
↑80.0%prior 5
Freezing rain/drizzle9 (4.1%)
Blowing Snow5 (2.3%)
Fog, smoke, smog3 (1.4%)
Severe Winds1 (0.5%)

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

Lighting

Daylight147 (66.8%)
↓-4.5%prior 154
Dark - roadway not lighted47 (21.4%)
↓-4.1%prior 49
Dusk11 (5.0%)
↑120.0%prior 5
Dark - roadway lighted10 (4.5%)
↓-33.3%prior 15
Dawn4 (1.8%)
↓-33.3%prior 6
Dark - unknown roadway lighting1 (0.5%)

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

Road Surface

Dry147 (66.8%)
↓-15.0%prior 173
Wet23 (10.5%)
↑27.8%prior 18
Ice/frost21 (9.5%)
↑250.0%prior 6
Snow21 (9.5%)
↑23.5%prior 17
Gravel6 (2.7%)
↓-50.0%prior 12
Other (explain in narrative)1 (0.5%)
Slush1 (0.5%)

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

Vehicles & Demographics

Ford and Chevrolet vehicles were the most common makes involved in crashes during both periods. An analysis of persons involved in crashes shows a shift in age demographics. The representation of the 16-20 age group increased from 14.0% of all persons in 2017 to 15.0% in 2018. Meanwhile, the share of individuals in the 35-44 age group decreased from 15.7% in the prior year to 12.8% in the current year.

Top Vehicle Makes (436 vehicles)

1
CHEV95 (21.8%)
↑48.4%prior 64
2
FORD70 (16.1%)
→0.0%prior 70
3
DODG34 (7.8%)
↑36.0%prior 25
4
CHEVROLET20 (4.6%)
↓-52.4%prior 42
5
CHRY17 (3.9%)
↑54.5%prior 11
6
TOYT15 (3.4%)
↑50.0%prior 10
7
PONT14 (3.2%)
↑40.0%prior 10
8
HOND12 (2.8%)
9
BUIC11 (2.5%)
↓-42.1%prior 19
10
NR10 (2.3%)

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

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

Sex Distribution (326 persons with recorded sex)

Male213 (65.3%)
↓-2.7%prior 219
Female113 (34.7%)
↓-28.0%prior 157

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 10, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 316
  • Total persons involved: 546
  • Total vehicles involved: 436

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 10, 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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