Yearly Traffic Safety Analysis

161 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Allamakee County recorded 161 total vehicle crashes, a 19.9% decrease from the 201 crashes reported in 2017. This overall reduction was accompanied by a drop in fatalities from two to one. The most significant year-over-year shift was the overall decline in crash volume, with notable decreases in fatal, serious injury, and animal-related incidents.

161

▼ -19.9%was 201

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

53

▼ -1.9%was 54

Persons Injured

1

▼ -50.0%was 2

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

Traffic crashes in Allamakee County showed a clear downward trend year-over-year, falling from 201 in 2017 to 161 in 2018. Fatalities were halved from two to one, and total injuries remained nearly stable, decreasing from 54 to 53. This indicates a significant improvement in overall crash frequency.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 2↓-50.0%

1

Pedestrians Injured

Prior: 0→%

52

Motorists Injured

Prior: 54↓-3.7%

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 shifted between the two periods. In 2018, the peak day for crashes was Tuesday with 33 incidents, a change from 2017 when Friday was the peak day with 38 crashes. The peak hour for collisions remained consistent at 5 p.m. in both years, though the number of crashes during that hour decreased from 20 in 2017 to 14 in 2018.

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

Crash severity decreased notably from 2017 to 2018. The number of fatal crashes was halved from two to one, and the count of serious injury crashes fell from 13 to 6. Conversely, crashes resulting in minor injuries increased from 8 to 13. The proportion of crashes with no injuries saw a slight increase from 74.6% in 2017 to 76.4% in 2018.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.6%
↓-50.0%prior 2
Serious Injury6serious injury crashes3.7%
↓-53.8%prior 13
Minor Injury13minor injury crashes8.1%
↑62.5%prior 8
Possible Injury18possible injury crashes11.2%
↓-35.7%prior 28
No Injury123no injury crashes76.4%
↓-18.0%prior 150

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 primary contributing factors remained consistent, though their counts decreased. Collisions involving an animal were the top factor in both years, but the count dropped by 33.8% from 65 incidents in 2017 to 43 in 2018. 'Lost Control' remained the second-most cited factor, with its count decreasing slightly from 24 to 22. 'Driving too fast for conditions' also saw a small reduction in count from 13 to 12 incidents.

Officer-Reported Primary Contributing Cause

Animal43 (26.7%)↓-33.8%prior 65
Lost Control22 (13.7%)↓-8.3%prior 24
FTYROW: From stop sign13 (8.1%)→0.0%prior 13
Driving too fast for conditions12 (7.5%)↓-7.7%prior 13
Ran off road - straight11 (6.8%)↓-21.4%prior 14
Other (explain in narrative): Other9 (5.6%)↓-10.0%prior 10
Ran off road - left8 (5%)↓-20.0%prior 10
Ran Stop Sign4 (2.5%)
Followed too close4 (2.5%)
FTYROW: From parked position3 (1.9%)

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 the total number of crashes decreased, the proportion of incidents occurring in adverse conditions increased. Crashes on roads with ice, snow, slush, or water increased from 41 in 2017 to 50 in 2018, representing a proportional rise from 20.4% to 31.1% of all crashes. Similarly, crashes in dark, unlighted conditions increased in count from 31 to 37. The share of crashes in clear weather and daylight remained relatively stable.

Weather

Clear92 (63.0%)
↓-15.6%prior 109
Cloudy29 (19.9%)
↑16.0%prior 25
Snow8 (5.5%)
↓-20.0%prior 10
Rain6 (4.1%)
↓-25.0%prior 8
Freezing rain/drizzle6 (4.1%)
Blowing Snow2 (1.4%)
Other (explain in narrative)2 (1.4%)
Fog, smoke, smog1 (0.7%)

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

Lighting

Daylight91 (62.3%)
↓-20.2%prior 114
Dark - roadway not lighted37 (25.3%)
↑19.4%prior 31
Dawn7 (4.8%)
Dark - roadway lighted7 (4.8%)
→0.0%prior 7
Dusk4 (2.7%)
↓-55.6%prior 9

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

Road Surface

Dry91 (61.9%)
↓-17.3%prior 110
Wet17 (11.6%)
↑6.3%prior 16
Ice/frost16 (10.9%)
↑33.3%prior 12
Snow14 (9.5%)
↑16.7%prior 12
Gravel5 (3.4%)
↓-44.4%prior 9
Slush3 (2.0%)
Sand1 (0.7%)

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 were consistent year-over-year, with Chevrolet and Ford products being the most common in both 2017 and 2018. Regarding driver and occupant demographics, the representation of most age groups was stable. However, the share of persons aged 55-64 involved in crashes decreased from 17.3% in 2017 to 14.4% in 2018, while the share for those 65 and older saw a slight increase from 16.3% to 17.7%.

Top Vehicle Makes (222 vehicles)

1
FORD49 (22.1%)
↑25.6%prior 39
2
CHEV41 (18.5%)
→0.0%prior 41
3
CHEVROLET20 (9%)
↓-37.5%prior 32
4
DODG13 (5.9%)
↑62.5%prior 8
5
GMC12 (5.4%)
↓-7.7%prior 13
6
JEEP8 (3.6%)
↓-11.1%prior 9
7
DODGE7 (3.2%)
↓-56.3%prior 16
8
CHRY7 (3.2%)
9
TOYT5 (2.3%)
→0.0%prior 5
10
TOYOTA4 (1.8%)
↓-55.6%prior 9

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

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

Sex Distribution (188 persons with recorded sex)

Male115 (61.2%)
↓-8.0%prior 125
Female73 (38.8%)
↓-18.0%prior 89

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: 161
  • Total persons involved: 271
  • Total vehicles involved: 222

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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