Yearly Traffic Safety Analysis

397 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Winneshiek County, total crashes increased by 10.6% from 359 in 2017 to 397 in 2018. While total fatalities remained unchanged at two, the number of serious injury crashes more than doubled, rising from 6 in the prior year to 13 in the current year.

397

▲ 10.6%was 359

Total Crash Events

2

Persons Killed

89

▲ 6.0%was 84

Persons Injured

1

▼ -50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (2) 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, traffic crashes in Winneshiek County trended upward in 2018 compared to the previous year. The total number of incidents rose by 38, from 359 to 397, representing a 10.6% increase. This was accompanied by a 6% increase in injuries, from 84 to 89, while fatalities held steady at two for both periods.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 2→0.0%

1

Pedestrians Injured

Prior: 1→0.0%

1

Cyclists Injured

Prior: 0→%

87

Motorists Injured

Prior: 83↑4.8%

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 minor shifts between the two years. The peak day for crashes moved from Saturday (61 crashes) in 2017 to Friday (66 crashes) in 2018. The peak hour also shifted slightly earlier, from 6 p.m. in the prior year (32 crashes) to 5 p.m. in the current year, which saw a notable concentration of 41 crashes during that hour.

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 worsened in 2018, despite a drop in fatal incidents from two to one. Crashes resulting in serious injuries increased significantly, more than doubling from 6 incidents in 2017 to 13 in 2018, and their share of all crashes grew from 1.7% to 3.3%. Conversely, crashes involving possible injuries decreased from 33 to 24.

Severity is per crash event (most severe injury). 1 fatal crash events resulted in 2 persons killed.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
↓-50.0%prior 2
Serious Injury13serious injury crashes3.3%
↑116.7%prior 6
Minor Injury30minor injury crashes7.6%
→0.0%prior 30
Possible Injury24possible injury crashes6%
↓-27.3%prior 33
No Injury329no injury crashes82.9%
↑14.2%prior 288

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 remained the leading contributing factor in both periods, with the count increasing by 17.6% from 153 in 2017 to 180 in 2018. 'Lost Control' also held its rank as the second most common factor, with its count rising by 51.9% from 27 to 41 incidents. Notably, crashes attributed to 'Driving too fast for conditions' increased by 93.3%, from 15 to 29, moving it from the fifth to the third most frequent cause year-over-year.

Officer-Reported Primary Contributing Cause

Animal180 (45.3%)↑17.6%prior 153
Lost Control41 (10.3%)↑51.9%prior 27
Driving too fast for conditions29 (7.3%)↑93.3%prior 15
Other (explain in narrative): Other18 (4.5%)↑38.5%prior 13
Followed too close17 (4.3%)↑88.9%prior 9
Ran off road - straight13 (3.3%)↑8.3%prior 12
FTYROW: From stop sign10 (2.5%)↓-41.2%prior 17
Driver Distraction: Other interior distraction7 (1.8%)
Ran off road - left7 (1.8%)↓-56.3%prior 16
FTYROW: Making left turn6 (1.5%)↓-25.0%prior 8

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 most crashes in both years occurred on dry roads in clear daylight, there was a notable shift in incidents related to winter weather. Crashes on snow-covered road surfaces more than doubled, increasing from 11 in 2017 to 26 in 2018. In contrast, crashes on icy or frosty roads decreased from 21 to 15 over the same period.

Weather

Clear161 (63.4%)
↑2.5%prior 157
Cloudy57 (22.4%)
↑26.7%prior 45
Snow13 (5.1%)
↑18.2%prior 11
Rain8 (3.1%)
Blowing Snow6 (2.4%)
Freezing rain/drizzle5 (2.0%)
↓-28.6%prior 7
Severe Winds1 (0.4%)
Sleet, hail1 (0.4%)
Other (explain in narrative)1 (0.4%)
Fog, smoke, smog1 (0.4%)
↓-90.9%prior 11

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

Lighting

Daylight170 (66.1%)
↑7.6%prior 158
Dark - roadway not lighted52 (20.2%)
↑20.9%prior 43
Dark - roadway lighted16 (6.2%)
↓-30.4%prior 23
Dusk13 (5.1%)
↑18.2%prior 11
Dawn5 (1.9%)
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

Dry173 (67.6%)
↓-0.6%prior 174
Snow26 (10.2%)
↑136.4%prior 11
Wet18 (7.0%)
↓-5.3%prior 19
Gravel17 (6.6%)
↑41.7%prior 12
Ice/frost15 (5.9%)
↓-28.6%prior 21
Slush5 (2.0%)
Mud, dirt1 (0.4%)
Other (explain in narrative)1 (0.4%)

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

Vehicles & Demographics

Year-over-year, the number of persons aged 55-64 involved in crashes saw a notable increase, rising from 60 in 2017 to 95 in 2018, and their share of total persons involved grew from 11.1% to 14.4%. Among vehicle makes, Chevrolet and Ford remained the most frequently involved. While Ford's involvement was stable (70 vehicles in 2017 vs. 69 in 2018), the count for Chevrolet vehicles increased from 98 to 126.

Top Vehicle Makes (524 vehicles)

1
CHEV70 (13.4%)
↑59.1%prior 44
2
FORD69 (13.2%)
↓-1.4%prior 70
3
CHEVROLET56 (10.7%)
↑3.7%prior 54
4
GMC24 (4.6%)
↑26.3%prior 19
5
DODG22 (4.2%)
↑57.1%prior 14
6
JEEP21 (4%)
↓-25.0%prior 28
7
BUIC19 (3.6%)
↑5.6%prior 18
8
BUICK15 (2.9%)
↑36.4%prior 11
9
DODGE14 (2.7%)
↑16.7%prior 12
10
TOYT13 (2.5%)
↑116.7%prior 6

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

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

Sex Distribution (398 persons with recorded sex)

Male233 (58.5%)
↑30.9%prior 178
Female165 (41.5%)
↑13.0%prior 146

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: 397
  • Total persons involved: 660
  • Total vehicles involved: 524

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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