Yearly Traffic Safety Analysis

327 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Clayton County, total vehicle crashes increased from 306 in 2017 to 327 in 2018, a rise of 6.9%. While the number of injuries decreased from 80 to 67, the most significant year-over-year change was a sharp increase in fatalities, which rose from 1 in 2017 to 5 in 2018.

327

▲ 6.9%was 306

Total Crash Events

5

▲ 400.0%was 1

Persons Killed

67

▼ -16.3%was 80

Persons Injured

3

▲ 200.0%was 1

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

Overall, the trend in Clayton County shows an increase in the total number of crashes, rising by 6.9% from 306 in 2017 to 327 in 2018. This increase was accompanied by a concerning rise in fatalities from 1 to 5. However, the total number of people injured in these crashes decreased by 16.3%, from 80 in the prior year to 67 in the current year.

Vulnerable Road User Casualties

5

Motorists Killed

Prior: 1↑400.0%

67

Motorists Injured

Prior: 79↓-15.2%

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 peak time for crashes remained consistent, with the 5 p.m. hour having the highest frequency in both 2017 (29 crashes) and 2018 (28 crashes). Friday was the most common day for crashes in 2018, accounting for 61 incidents. This represents an increase from the prior year, when Friday was tied with Wednesday as the peak day with 48 crashes each.

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 significantly in 2018, with the number of fatal crashes increasing from 1 to 3, and total fatalities rising from 1 to 5. The fatal crash rate rose from 0.33 per 100 crashes in 2017 to 0.92 in 2018. Conversely, the proportion of crashes resulting in any level of injury (serious, minor, or possible) declined from 20.9% of all crashes in 2017 to 16.4% in 2018.

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

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.9%
↑200.0%prior 1
Serious Injury6serious injury crashes1.8%
↓-25.0%prior 8
Minor Injury24minor injury crashes7.3%
↓-17.2%prior 29
Possible Injury24possible injury crashes7.3%
↓-11.1%prior 27
No Injury270no injury crashes82.6%
↑12.0%prior 241

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, with the count of such incidents increasing from 124 in 2017 to 154 in 2018, a 24.2% rise. "Lost Control" was the second-most cited factor in both years, though its count decreased significantly from 36 incidents in 2017 to 20 in 2018. Crashes attributed to "Driving too fast for conditions" increased from 14 to 19, making it the third most common factor in 2018.

Officer-Reported Primary Contributing Cause

Animal154 (47.1%)↑24.2%prior 124
Lost Control20 (6.1%)↓-44.4%prior 36
Driving too fast for conditions19 (5.8%)↑35.7%prior 14
Other (explain in narrative): Other15 (4.6%)→0.0%prior 15
Ran off road - straight14 (4.3%)↓-22.2%prior 18
Ran off road - left13 (4%)→0.0%prior 13
Driver Distraction: Other interior distraction10 (3.1%)
Operating vehicle in an reckless, erratic, careless, negligent manner9 (2.8%)
Ran Stop Sign7 (2.1%)
FTYROW: From stop sign6 (1.8%)↓-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

Crashes on dry roads were the most frequent in both periods, with nearly identical counts of 126 in 2018 and 125 in 2017. There was a notable shift in crashes related to adverse winter conditions; incidents on snowy surfaces more than doubled from 10 to 21 year-over-year, while crashes on icy or frosty roads fell from 20 to 9. The proportion of crashes in daylight decreased from 42.5% in 2017 to 34.3% in 2018, while crashes on dark, unlit roadways increased from 44 to 51.

Weather

Clear135 (71.4%)
↓-2.2%prior 138
Cloudy25 (13.2%)
↓-7.4%prior 27
Rain12 (6.3%)
↑50.0%prior 8
Snow8 (4.2%)
→0.0%prior 8
Fog, smoke, smog4 (2.1%)
↓-20.0%prior 5
Blowing Snow2 (1.1%)
Freezing rain/drizzle2 (1.1%)
↓-66.7%prior 6
Sleet, hail1 (0.5%)

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

Lighting

Daylight112 (58.9%)
↓-13.8%prior 130
Dark - roadway not lighted51 (26.8%)
↑15.9%prior 44
Dawn11 (5.8%)
↑83.3%prior 6
Dusk8 (4.2%)
↓-11.1%prior 9
Dark - roadway lighted7 (3.7%)
↓-12.5%prior 8
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

Dry126 (66.7%)
↑0.8%prior 125
Snow21 (11.1%)
↑110.0%prior 10
Wet17 (9.0%)
↓-10.5%prior 19
Gravel14 (7.4%)
↓-33.3%prior 21
Ice/frost9 (4.8%)
↓-55.0%prior 20
Mud, dirt1 (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

The most common vehicle makes involved in crashes were consistent, with Ford and Chevrolet models having the highest counts in both 2017 and 2018. Analysis of persons involved shows a shift in age demographics; the 26-34 age group saw its involvement increase from 54 individuals in 2017 to 84 in 2018, becoming the largest group. In contrast, the 45-54 age group, which was the most represented in 2017 with 75 individuals, decreased to 69 individuals in 2018.

Top Vehicle Makes (411 vehicles)

1
FORD76 (18.5%)
↑4.1%prior 73
2
CHEV74 (18%)
↑13.8%prior 65
3
CHEVROLET50 (12.2%)
↓-12.3%prior 57
4
DODG21 (5.1%)
↑50.0%prior 14
5
DODGE17 (4.1%)
↓-26.1%prior 23
6
GMC16 (3.9%)
↑60.0%prior 10
7
CHRY13 (3.2%)
8
JEEP13 (3.2%)
↑116.7%prior 6
9
TOYT12 (2.9%)
↑20.0%prior 10
10
FREIGHTLINER10 (2.4%)

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

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

Sex Distribution (270 persons with recorded sex)

Male189 (70.0%)
↑9.2%prior 173
Female81 (30.0%)
↓-22.1%prior 104

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: 327
  • Total persons involved: 532
  • Total vehicles involved: 411

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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