Yearly Traffic Safety Analysis

258 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Floyd County, total traffic crashes increased by 15.7% from 223 in 2017 to 258 in 2018. While total fatalities remained constant at five, the number of reported injuries decreased from 71 to 62. The most significant year-over-year shift was a 39.7% increase in the count of crashes involving an animal, which rose from 68 to 95 incidents.

258

▲ 15.7%was 223

Total Crash Events

5

Persons Killed

62

▼ -12.7%was 71

Persons Injured

3

▼ -25.0%was 4

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 safety trends in Floyd County showed a notable increase in the total number of crashes, which rose from 223 in 2017 to 258 in 2018. Despite this 15.7% rise in collisions, the outcomes were less severe on average. Total injuries fell by 12.7% from 71 to 62, and the number of fatalities held steady at five in both years.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 5→0.0%

1

Cyclists Injured

Prior: 3↓-66.7%

61

Motorists Injured

Prior: 68↓-10.3%

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. The peak day for crashes moved from Monday in 2017 (36 incidents) to Wednesday in 2018, which saw a higher peak of 50 incidents. While 7 p.m. remained a peak hour for collisions in both years, the number of crashes during that hour increased from 16 to 21.

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 total crashes increased, the severity of those crashes generally lessened year-over-year. The fatal crash rate decreased from 1.79% in 2017 to 1.16% in 2018, with fatal crashes falling from 4 to 3. The proportion of crashes resulting in a minor injury also dropped from 10.3% to 7.4%, although the share of possible injury crashes increased from 8.5% to 10.9%.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.2%
↓-25.0%prior 4
Serious Injury3serious injury crashes1.2%
→0.0%prior 3
Minor Injury19minor injury crashes7.4%
↓-17.4%prior 23
Possible Injury28possible injury crashes10.9%
↑47.4%prior 19
No Injury205no injury crashes79.5%
↑17.8%prior 174

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 an animal remained the top contributing factor in both years, and the count of these incidents grew by 39.7%, from 68 in 2017 to 95 in 2018. "Driving too fast for conditions" more than doubled as a factor, increasing from 8 crashes to 18. Conversely, incidents attributed to a driver losing control were less frequent, decreasing from 16 crashes in 2017 to 7 in 2018.

Officer-Reported Primary Contributing Cause

Animal95 (36.8%)↑39.7%prior 68
Driving too fast for conditions18 (7%)↑125.0%prior 8
FTYROW: From stop sign13 (5%)↑18.2%prior 11
Ran off road - left11 (4.3%)↑22.2%prior 9
Followed too close11 (4.3%)↑120.0%prior 5
FTYROW: Making left turn10 (3.9%)↑66.7%prior 6
FTYROW: At uncontrolled intersection10 (3.9%)
Made improper turn9 (3.5%)
Ran off road - straight8 (3.1%)↓-33.3%prior 12
Ran Stop Sign7 (2.7%)↓-36.4%prior 11

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

Road & Environmental Conditions

Crashes were more likely to occur in adverse conditions in 2018 compared to the prior year. The proportion of collisions on dry road surfaces fell from 51.6% in 2017 to 43.0% in 2018. Concurrently, the count of crashes on snow, ice, or wet pavement increased from a combined 44 incidents to 62. The share of crashes occurring in daylight also decreased from 55.2% to 50.0%.

Weather

Clear121 (68.4%)
↑8.0%prior 112
Cloudy28 (15.8%)
↓-26.3%prior 38
Snow8 (4.5%)
↑33.3%prior 6
Blowing Snow7 (4.0%)
Freezing rain/drizzle5 (2.8%)
Rain4 (2.3%)
↓-20.0%prior 5
Fog, smoke, smog3 (1.7%)
Blowing sand, soil, dirt1 (0.6%)

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

Lighting

Daylight129 (71.7%)
↑4.9%prior 123
Dark - roadway not lighted27 (15.0%)
↑12.5%prior 24
Dark - roadway lighted14 (7.8%)
→0.0%prior 14
Dawn5 (2.8%)
Dusk4 (2.2%)
Dark - unknown roadway lighting1 (0.6%)

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

Road Surface

Dry111 (62.0%)
↓-3.5%prior 115
Snow22 (12.3%)
↑57.1%prior 14
Ice/frost17 (9.5%)
↑13.3%prior 15
Wet16 (8.9%)
↑6.7%prior 15
Slush7 (3.9%)
Gravel5 (2.8%)
↓-16.7%prior 6
Mud, dirt1 (0.6%)

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 saw a shift, with Chevrolet (87 vehicles) surpassing Ford (60 vehicles) as the most common make in 2018, a reversal from 2017. An analysis of persons involved shows a change in age distribution; the share of individuals in the 26-34 and 55-64 age groups increased, while the proportion of those aged 65 and older decreased from 14.2% to 12.6%. The proportion of males involved in crashes also rose slightly from 61.7% to 65.4%.

Top Vehicle Makes (390 vehicles)

1
CHEV61 (15.6%)
↑48.8%prior 41
2
FORD60 (15.4%)
↑17.6%prior 51
3
CHEVROLET26 (6.7%)
↓-21.2%prior 33
4
DODG24 (6.2%)
↑100.0%prior 12
5
GMC14 (3.6%)
↑16.7%prior 12
6
TOYT14 (3.6%)
↑133.3%prior 6
7
CHRY13 (3.3%)
↑44.4%prior 9
8
DODGE13 (3.3%)
↑8.3%prior 12
9
FREIGHTLINER12 (3.1%)
↑33.3%prior 9
10
TOYOTA11 (2.8%)
↑83.3%prior 6

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

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

Sex Distribution (301 persons with recorded sex)

Male197 (65.4%)
↑31.3%prior 150
Female104 (34.6%)
↑11.8%prior 93

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: 258
  • Total persons involved: 485
  • Total vehicles involved: 390

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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