Yearly Traffic Safety Analysis

347 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Fayette County, total traffic crashes increased by 5.8% from 328 in 2017 to 347 in 2018. While total crashes and fatalities (4, up from 3) rose, total injuries saw a slight decrease from 93 to 91. The most significant year-over-year change was a 71.4% reduction in crashes involving a driver under the influence of alcohol, which fell from 14 incidents in 2017 to 4 in 2018.

347

▲ 5.8%was 328

Total Crash Events

4

▲ 33.3%was 3

Persons Killed

91

▼ -2.2%was 93

Persons Injured

4

▲ 33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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

The overall trend indicates a modest rise in traffic collisions in Fayette County, with total crashes increasing by 5.8% from 328 to 347 year-over-year. This increase in crash volume was accompanied by one additional fatality, rising from 3 in 2017 to 4 in 2018. However, the number of people injured in these incidents saw a slight decline from 93 to 91.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 3↑33.3%

1

Pedestrians Injured

Prior: 1→0.0%

90

Motorists Injured

Prior: 92↓-2.2%

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 timing of crashes shifted between the two periods. In 2018, Monday was the peak day for crashes with 64 incidents, a change from 2017 when Tuesday and Saturday were the busiest days with 56 crashes each. The peak hour for collisions also moved slightly earlier, from 6 p.m. in 2017 (37 crashes) to 5 p.m. in 2018 (32 crashes), though the late afternoon commute remained the most frequent time for incidents in both years.

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 slightly in 2018 compared to the prior year. The number of fatal crashes increased from 3 to 4, and the corresponding share of fatal crashes rose from 0.9% to 1.2% of all incidents. While the count of serious injury crashes fell from 12 to 10, minor injury crashes increased from 27 to 33. The proportion of crashes resulting in no injuries grew from 77.7% in 2017 to 79.8% in 2018.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.2%
↑33.3%prior 3
Serious Injury10serious injury crashes2.9%
↓-16.7%prior 12
Minor Injury33minor injury crashes9.5%
↑22.2%prior 27
Possible Injury23possible injury crashes6.6%
↓-25.8%prior 31
No Injury277no injury crashes79.8%
↑8.6%prior 255

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 years, with the count of such incidents increasing by 28% from 114 in 2017 to 146 in 2018. "Lost Control" was the second-most cited factor in both periods, holding steady at 30 crashes. The number of crashes attributed to "Driving too fast for conditions" increased from 17 to 21, while incidents involving "Followed too close" decreased from 13 to 11.

Officer-Reported Primary Contributing Cause

Animal146 (42.1%)↑28.1%prior 114
Lost Control30 (8.6%)→0.0%prior 30
Ran off road - straight22 (6.3%)↓-4.3%prior 23
Driving too fast for conditions21 (6.1%)↑23.5%prior 17
Other (explain in narrative): Other19 (5.5%)↓-24.0%prior 25
Ran off road - left13 (3.7%)↑44.4%prior 9
Followed too close11 (3.2%)↓-15.4%prior 13
FTYROW: From stop sign8 (2.3%)↓-33.3%prior 12
Swerving/Evasive Action6 (1.7%)
FTYROW: Making left turn5 (1.4%)

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 in clear weather and on dry roads were the most frequent scenarios in both 2017 and 2018, with relatively stable numbers. There was a notable shift in crashes under adverse conditions; incidents on snowy surfaces increased from 10 to 19, and those in snowy weather rose from 7 to 12. Conversely, crashes on icy or frosty roads saw a significant decrease, falling from 31 in 2017 to 17 in 2018.

Weather

Clear150 (65.5%)
↑0.7%prior 149
Cloudy37 (16.2%)
↓-11.9%prior 42
Rain13 (5.7%)
→0.0%prior 13
Freezing rain/drizzle13 (5.7%)
↑44.4%prior 9
Snow12 (5.2%)
↑71.4%prior 7
Fog, smoke, smog3 (1.3%)
↓-50.0%prior 6
Blowing Snow1 (0.4%)

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

Lighting

Daylight153 (66.5%)
↑3.4%prior 148
Dark - roadway not lighted44 (19.1%)
↓-18.5%prior 54
Dark - roadway lighted16 (7.0%)
→0.0%prior 16
Dusk11 (4.8%)
↑83.3%prior 6
Dawn5 (2.2%)
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

Dry151 (65.7%)
↑4.1%prior 145
Wet22 (9.6%)
↓-31.3%prior 32
Snow19 (8.3%)
↑90.0%prior 10
Ice/frost17 (7.4%)
↓-45.2%prior 31
Gravel12 (5.2%)
↑50.0%prior 8
Slush7 (3.0%)
Mud, dirt1 (0.4%)
Sand1 (0.4%)

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 in both periods. The number of Fords involved increased from 79 to 106 year-over-year, while the combined count for Chevrolet vehicles remained nearly the same. Regarding persons involved, the 16-20 age group saw a significant increase from 73 individuals in 2017 to 96 in 2018. The 55-64 age group also saw a rise in involvement, from 68 to 80 persons.

Top Vehicle Makes (454 vehicles)

1
FORD106 (23.3%)
↑34.2%prior 79
2
CHEV95 (20.9%)
↑21.8%prior 78
3
CHEVROLET26 (5.7%)
↓-43.5%prior 46
4
DODG22 (4.8%)
↑29.4%prior 17
5
PONT15 (3.3%)
↓-16.7%prior 18
6
CHRY13 (2.9%)
↓-31.6%prior 19
7
GMC11 (2.4%)
↓-15.4%prior 13
8
JEEP11 (2.4%)
↑37.5%prior 8
9
BUIC10 (2.2%)
↓-50.0%prior 20
10
KIA8 (1.8%)
↑60.0%prior 5

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

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

Sex Distribution (331 persons with recorded sex)

Male193 (58.3%)
↑9.7%prior 176
Female138 (41.7%)
↓-2.1%prior 141

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: 347
  • Total persons involved: 571
  • Total vehicles involved: 454

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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