Yearly Traffic Safety Analysis

65 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Audubon County, total traffic crashes decreased by 15.6% from 77 in 2018 to 65 in 2019. Despite the overall reduction in collisions, the most significant change was the occurrence of one fatal crash in 2019, whereas none were recorded in the prior year. Total injuries remained relatively stable, decreasing from 19 to 17.

65

▼ -15.6%was 77

Total Crash Events

1

Persons Killed

17

▼ -10.5%was 19

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

The overall trend in Audubon County shows a decrease in traffic incidents, with total crashes falling from 77 in 2018 to 65 in 2019. The number of injuries also saw a slight decline from 19 to 17. However, this downward trend was contrasted by the registration of one fatality in 2019, compared to zero in the previous year.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 0→%

17

Motorists Injured

Prior: 18↓-5.6%

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 timing of crashes shifted between the two periods. In 2018, the peak day for crashes was Tuesday with 15 incidents, which shifted in 2019 to a tie between Monday and Wednesday, each with 13 crashes. The peak hour for collisions also moved from a three-way tie at 7 a.m., 11 a.m., and 2 p.m. in 2018 to 7 p.m. in 2019.

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

While overall crashes declined, 2019 saw the county's first fatal crash in this two-year period, resulting in a fatality rate of 1.54 per 100 crashes compared to zero in 2018. The number of serious injury crashes decreased from two in 2018 to one in 2019. The proportion of crashes resulting in no injuries remained largely stable, accounting for 76.9% of incidents in 2019 versus 79.2% in 2018.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.5%
Serious Injury1serious injury crashes1.5%
↓-50.0%prior 2
Minor Injury8minor injury crashes12.3%
↑14.3%prior 7
Possible Injury5possible injury crashes7.7%
↓-28.6%prior 7
No Injury50no injury crashes76.9%
↓-18.0%prior 61

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 animals remained the top contributing factor in both years, but their count dropped significantly from 24 in 2018 to 13 in 2019. Crashes attributed to drivers losing control also decreased, falling from 9 incidents to 4. Conversely, incidents where drivers ran off a straight road increased slightly from 5 to 6. Failure to yield from a stop sign was a factor in 5 crashes in 2018, while failure to yield from a yield sign was cited in 3 crashes in 2019.

Officer-Reported Primary Contributing Cause

Animal13 (20%)↓-45.8%prior 24
Other (explain in narrative): Other7 (10.8%)↑40.0%prior 5
Ran off road - straight6 (9.2%)↑20.0%prior 5
Lost Control4 (6.2%)↓-55.6%prior 9
Driving too fast for conditions4 (6.2%)
Ran off road - left4 (6.2%)↓-20.0%prior 5
FTYROW: From yield sign3 (4.6%)
Made improper turn3 (4.6%)
Driver Distraction: Other interior distraction2 (3.1%)
Driver Distraction: Exterior distraction2 (3.1%)

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 proportion of crashes occurring in daylight decreased, from 63.6% of all crashes in 2018 to 52.3% in 2019. Correspondingly, crashes in dark, unlighted conditions increased in count from 11 to 15. The share of crashes happening in clear weather increased from 55.8% in 2018 to 61.5% in 2019, while crashes on dry roads made up a slightly smaller proportion of the total.

Weather

Clear40 (69.0%)
↓-7.0%prior 43
Freezing rain/drizzle4 (6.9%)
Snow4 (6.9%)
Cloudy4 (6.9%)
↓-66.7%prior 12
Rain3 (5.2%)
Blowing Snow2 (3.4%)
Severe Winds1 (1.7%)

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

Lighting

Daylight34 (58.6%)
↓-30.6%prior 49
Dark - roadway not lighted15 (25.9%)
↑36.4%prior 11
Dark - roadway lighted5 (8.6%)
Dawn3 (5.2%)
Dusk1 (1.7%)
↓-80.0%prior 5

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

Road Surface

Dry36 (62.1%)
↓-21.7%prior 46
Snow7 (12.1%)
↑40.0%prior 5
Wet7 (12.1%)
Ice/frost6 (10.3%)
Gravel2 (3.4%)
↓-60.0%prior 5

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, Chevrolet and Ford, remained consistent across both years, though both saw a decrease in total crash involvement in 2019. Analysis of persons involved shows a notable shift in age demographics; the number of people in the 65+ age group involved in crashes increased from 21 to 26. Conversely, involvement for the 45-54 age group decreased from 17 persons in 2018 to 7 in 2019.

Top Vehicle Makes (94 vehicles)

1
CHEV18 (19.1%)
→0.0%prior 18
2
CHEVROLET14 (14.9%)
↓-22.2%prior 18
3
DODG9 (9.6%)
↑80.0%prior 5
4
FORD8 (8.5%)
↓-42.9%prior 14
5
GMC6 (6.4%)
6
BUIC3 (3.2%)
7
CHRY3 (3.2%)
8
FREIGHTLINER3 (3.2%)
9
JEEP2 (2.1%)
10
DODGE2 (2.1%)
↓-71.4%prior 7

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

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

Sex Distribution (79 persons with recorded sex)

Male48 (60.8%)
↓-12.7%prior 55
Female31 (39.2%)
↓-3.1%prior 32

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: 65
  • Total persons involved: 127
  • Total vehicles involved: 94

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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