Yearly Traffic Safety Analysis

77 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Audubon County recorded 77 traffic crashes, a 37.5% increase from the 56 crashes reported in 2017. Despite the rise in overall collisions, the number of fatalities dropped from four in the prior year to zero in the current period. This decrease in fatalities occurred even as the total number of injuries remained relatively stable, with 19 in 2018 compared to 21 in 2017.

77

▲ 37.5%was 56

Total Crash Events

0

▼ -100.0%was 4

Persons Killed

19

▼ -9.5%was 21

Persons Injured

0

▼ -100.0%was 4

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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 crashes in Audubon County increased by 37.5% year-over-year, rising from 56 in 2017 to 77 in 2018. However, the severity of these crashes decreased notably. The number of total injuries saw a slight decline from 21 to 19, and fatalities fell from four to zero.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 4↓-100.0%

1

Cyclists Injured

Prior: 0→%

18

Motorists Injured

Prior: 21↓-14.3%

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 timing of crashes shifted between the two periods. In 2018, the peak day for crashes was Tuesday with 15 incidents, a change from 2017 when Thursday and Friday were the most frequent days with 12 crashes each. The peak hour also moved from the evening (9 p.m. in 2017 with 7 crashes) to the afternoon (2 p.m. in 2018 with 7 crashes).

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

Crash severity decreased significantly in 2018 compared to the prior year. Fatal crashes were eliminated, dropping from 4 incidents (7.1% of all crashes) in 2017 to zero in 2018. The number of serious injury crashes held steady at two, while their share of total crashes fell from 3.6% to 2.6%. Consequently, the proportion of crashes resulting in no injury rose from 66.1% in 2017 to 79.2% of all incidents in 2018.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes2.6%
→0.0%prior 2
Minor Injury7minor injury crashes9.1%
↑16.7%prior 6
Possible Injury7possible injury crashes9.1%
→0.0%prior 7
No Injury61no injury crashes79.2%
↑64.9%prior 37

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 involving an animal remained the top contributing factor in both periods, with the count increasing by 33% from 18 crashes in 2017 to 24 in 2018. The second most common factor, "Lost Control," also saw a significant rise, increasing by 80% from 5 to 9 incidents. While the rank order of the top two factors did not change, their raw counts grew, contributing to the overall increase in crashes.

Officer-Reported Primary Contributing Cause

Animal24 (31.2%)↑33.3%prior 18
Lost Control9 (11.7%)↑80.0%prior 5
Ran off road - straight5 (6.5%)
Ran off road - left5 (6.5%)
Other (explain in narrative): Other5 (6.5%)
FTYROW: From stop sign5 (6.5%)
Other (explain in narrative): No improper action3 (3.9%)
Driver Distraction: Other interior distraction2 (2.6%)
Driving too fast for conditions2 (2.6%)
Followed too close2 (2.6%)

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

Road & Environmental Conditions

While the number of crashes on dry roads and in clear weather increased in raw counts, their proportion of total crashes decreased year-over-year. In 2018, 55.8% of crashes occurred in clear weather, down from a 76.8% share in 2017, even though the count was identical at 43. Crashes during cloudy conditions tripled from 4 to 12, and incidents involving snow, ice, or freezing rain increased from a combined 4 in 2017 to 11 in 2018.

Weather

Clear43 (62.3%)
→0.0%prior 43
Cloudy12 (17.4%)
Blowing Snow4 (5.8%)
Freezing rain/drizzle4 (5.8%)
Snow3 (4.3%)
Severe Winds2 (2.9%)
Rain1 (1.4%)

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

Lighting

Daylight49 (71.0%)
↑53.1%prior 32
Dark - roadway not lighted11 (15.9%)
↓-35.3%prior 17
Dusk5 (7.2%)
Dawn3 (4.3%)
Dark - roadway lighted1 (1.4%)

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

Road Surface

Dry46 (67.6%)
↑15.0%prior 40
Snow5 (7.4%)
Gravel5 (7.4%)
↓-37.5%prior 8
Ice/frost4 (5.9%)
Wet3 (4.4%)
Slush3 (4.4%)
Other (explain in narrative)1 (1.5%)
Mud, dirt1 (1.5%)

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

Vehicles & Demographics

The demographic profile of persons involved in crashes showed some shifts, with the 65+ age group increasing its representation from 15 to 21 individuals. The 16-20 and 45-54 age groups also saw higher involvement in 2018, with 17 individuals each. The makes of vehicles involved remained consistent, with Chevrolet and Ford being the most common in both years, and their counts increasing alongside the overall rise in crashes.

Top Vehicle Makes (106 vehicles)

1
CHEV18 (17%)
2
CHEVROLET18 (17%)
↓-37.9%prior 29
3
FORD14 (13.2%)
↑27.3%prior 11
4
DODGE7 (6.6%)
5
DODG5 (4.7%)
6
PETERBILT4 (3.8%)
7
BUIC4 (3.8%)
8
FREIGHTLINER3 (2.8%)
9
CHRYSLER3 (2.8%)
10
PONT3 (2.8%)

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

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

Sex Distribution (87 persons with recorded sex)

Male55 (63.2%)
↑48.6%prior 37
Female32 (36.8%)
↑60.0%prior 20

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: 77
  • Total persons involved: 127
  • Total vehicles involved: 106

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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