Yearly Traffic Safety Analysis

74 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Wayne County, total crashes increased from 64 in 2018 to 74 in 2019, a 15.6% rise. The most significant change was the occurrence of one fatal crash in 2019, resulting in one fatality, whereas there were no fatal crashes or fatalities in the prior year. The total number of injuries also increased from 29 to 32 over the same period.

74

▲ 15.6%was 64

Total Crash Events

1

Persons Killed

32

▲ 10.3%was 29

Persons Injured

1

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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Wayne County indicates a rising trend year-over-year. Total crashes increased by 15.6%, from 64 incidents in 2018 to 74 in 2019. This upward trend is also reflected in the number of persons injured, which grew by 10.3% from 29 to 32, and the registration of one fatality in 2019 compared to zero in the previous year.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

31

Motorists Injured

Prior: 29↑6.9%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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 2019, the peak day for crashes was Monday with 17 incidents, and the peak hour was 2 p.m. with 7 incidents. This contrasts with 2018, when Thursday was the peak day with 18 crashes and 6 p.m. was the peak hour with 7 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity saw a notable shift with the introduction of a fatal crash in 2019, which accounted for 1.4% of all incidents, compared to zero fatal crashes in 2018. While the number of serious injury crashes remained stable at 6 for both years, their share of total crashes decreased from 9.4% to 8.1%. Conversely, minor injury crashes increased in both count, from 8 to 12, and as a share of total crashes, from 12.5% to 16.2%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.4%
Serious Injury6serious injury crashes8.1%
→0.0%prior 6
Minor Injury12minor injury crashes16.2%
↑50.0%prior 8
Possible Injury9possible injury crashes12.2%
↑12.5%prior 8
No Injury46no injury crashes62.2%
↑9.5%prior 42

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor in both years, though the count of such incidents decreased from 20 in 2018 to 15 in 2019. The factor labeled 'Other' saw a significant increase in count, rising from 3 incidents in 2018 to 8 in 2019. Meanwhile, the count of crashes where 'Ran Stop Sign' was a factor decreased from 5 to 3.

Officer-Reported Primary Contributing Cause

Animal15 (20.3%)↓-25.0%prior 20
Other (explain in narrative): Other8 (10.8%)
Lost Control7 (9.5%)↑16.7%prior 6
Ran off road - straight4 (5.4%)↓-20.0%prior 5
Operating vehicle in an reckless, erratic, careless, negligent manner3 (4.1%)
Ran off road - left3 (4.1%)
Driver Distraction: Other interior distraction3 (4.1%)
Swerving/Evasive Action3 (4.1%)
Ran Stop Sign3 (4.1%)↓-40.0%prior 5
FTYROW: From stop sign2 (2.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The majority of crashes in both periods occurred in clear weather on dry roads, with the proportion remaining stable. In 2019, crashes during daylight hours increased to 41 from 34 in the prior year, representing 55.4% of all incidents. Crashes under cloudy conditions doubled in count from 6 to 12, while incidents on icy or frosty roads also saw an increase, rising from 3 in 2018 to 5 in 2019.

Weather

Clear49 (72.1%)
↑19.5%prior 41
Cloudy12 (17.6%)
↑100.0%prior 6
Rain3 (4.4%)
Freezing rain/drizzle2 (2.9%)
Fog, smoke, smog1 (1.5%)
Snow1 (1.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight41 (60.3%)
↑20.6%prior 34
Dark - roadway not lighted18 (26.5%)
↓-5.3%prior 19
Dusk5 (7.4%)
Dark - roadway lighted2 (2.9%)
Dawn2 (2.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry45 (66.2%)
↑21.6%prior 37
Wet8 (11.8%)
→0.0%prior 8
Gravel7 (10.3%)
↓-12.5%prior 8
Ice/frost5 (7.4%)
Snow3 (4.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

Comparing vehicles involved, Chevrolet-branded vehicles (including 'CHEV' and 'CHEVROLET') became the most common, with 28 involved in 2019 crashes, up from 18 in 2018. Ford and Dodge remained in the top three, with their involvement also increasing from 19 to 24 and 15 to 19, respectively. Analysis of persons involved shows a notable shift in age demographics; the share of individuals aged 65 and older increased from 7.8% in 2018 to 14.6% in 2019, while the share for the 45-54 age group decreased from 18.1% to 10.6%.

Top Vehicle Makes (107 vehicles)

1
FORD24 (22.4%)
↑26.3%prior 19
2
CHEV21 (19.6%)
↑162.5%prior 8
3
DODG12 (11.2%)
↑9.1%prior 11
4
CHEVROLET7 (6.5%)
↓-30.0%prior 10
5
DODGE7 (6.5%)
6
JEEP4 (3.7%)
7
TOYT3 (2.8%)
8
BUIC2 (1.9%)
↓-66.7%prior 6
9
CHRYSLER2 (1.9%)
10
GMC2 (1.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

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

Sex Distribution (101 persons with recorded sex)

Male59 (58.4%)
↑25.5%prior 47
Female42 (41.6%)
↑50.0%prior 28

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 74
  • Total persons involved: 151
  • Total vehicles involved: 107

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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