Yearly Traffic Safety Analysis

56 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Audubon County recorded 56 total crashes, an 8.2% decrease from the 61 crashes reported in 2016. Despite the overall reduction in collisions, the number of fatalities quadrupled, rising from one in the prior year to four in the current period. This increase in fatalities occurred alongside a slight rise in total injuries from 19 to 21.

56

▼ -8.2%was 61

Total Crash Events

4

▲ 300.0%was 1

Persons Killed

21

▲ 10.5%was 19

Persons Injured

4

▲ 300.0%was 1

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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash volume in Audubon County saw a modest decline, with total crashes falling by 8.2% from 61 in 2016 to 56 in 2017. However, this downward trend in crash frequency was contrasted by a sharp rise in severity. Fatalities increased from one to four, and total injuries rose by 10.5% from 19 to 21 year-over-year.

Vulnerable Road User Casualties

4

Motorists Killed

Prior: 1↑300.0%

21

Motorists Injured

Prior: 19↑10.5%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed some shifts between 2016 and 2017. While Friday remained a peak day for collisions with 12 incidents in both years, Thursday also emerged as a joint peak day in 2017 with 12 crashes. The peak hour for crashes moved later into the evening, shifting from 5 p.m. in 2016 (6 crashes) to 9 p.m. in 2017 (7 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity worsened significantly in 2017 compared to the previous year. The proportion of fatal crashes increased from 1.6% to 7.1% of all incidents, with fatal crashes rising from one to four. The share of crashes resulting in any form of injury (fatal, serious, minor, or possible) grew from 24.5% in 2016 to 33.9% in 2017. Consequently, the proportion of crashes with no injuries decreased from 75.4% to 66.1%.

Outcome by Severity (Crash Events)

Fatal4fatal crashes7.1%
↑300.0%prior 1
Serious Injury2serious injury crashes3.6%
↑100.0%prior 1
Minor Injury6minor injury crashes10.7%
↓-25.0%prior 8
Possible Injury7possible injury crashes12.5%
↑40.0%prior 5
No Injury37no injury crashes66.1%
↓-19.6%prior 46

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the top contributing factor in both periods, with the count increasing by 50% from 12 crashes in 2016 to 18 crashes in 2017. This factor's share of total crashes also rose from 19.7% to 32.1%. Conversely, crashes attributed to 'Lost Control' decreased from 7 to 5 incidents, and those involving 'Ran off road - straight' fell from 6 to 4. The factor 'Driving too fast for conditions,' which accounted for 5 crashes in 2016, was not among the primary factors in 2017.

Officer-Reported Primary Contributing Cause

Animal18 (32.1%)↑50.0%prior 12
Lost Control5 (8.9%)↓-28.6%prior 7
Ran off road - straight4 (7.1%)↓-33.3%prior 6
Failed to keep in proper lane3 (5.4%)
Passing: Other passing (explain in narrative)3 (5.4%)
Ran off road - left3 (5.4%)
Other (explain in narrative): Other2 (3.6%)
FTYROW: From driveway1 (1.8%)
FTYROW: From stop sign1 (1.8%)
FTYROW: From yield sign1 (1.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions remained largely consistent between 2016 and 2017, with the majority of incidents in both years occurring in clear weather and during daylight hours. Crashes in clear weather were unchanged at 43, while those in daylight decreased slightly from 37 to 32. Similarly, most crashes happened on dry roads, with the count increasing from 38 to 40. There were no significant shifts in the proportion of crashes occurring in adverse weather, lighting, or road surface conditions.

Weather

Clear43 (81.1%)
→0.0%prior 43
Cloudy4 (7.5%)
↓-42.9%prior 7
Snow3 (5.7%)
Fog, smoke, smog1 (1.9%)
Rain1 (1.9%)
Sleet, hail1 (1.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight32 (60.4%)
↓-13.5%prior 37
Dark - roadway not lighted17 (32.1%)
↓-5.6%prior 18
Dark - roadway lighted2 (3.8%)
Dawn1 (1.9%)
Dusk1 (1.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry40 (75.5%)
↑5.3%prior 38
Gravel8 (15.1%)
↑14.3%prior 7
Ice/frost2 (3.8%)
Wet2 (3.8%)
Snow1 (1.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

Chevrolet and Ford were the top two vehicle makes involved in crashes in both years, though their counts shifted; Chevrolet involvement rose from 22 to 29 vehicles, while Ford involvement fell from 16 to 11. Jeep-involved crashes saw a notable increase, rising from one vehicle in 2016 to eight in 2017. Regarding driver and passenger demographics, the number of individuals in the 55-64 age group involved in crashes increased from 9 to 13, and the 65+ group grew from 13 to 15.

Top Vehicle Makes (79 vehicles)

1
CHEVROLET29 (36.7%)
↑31.8%prior 22
2
FORD11 (13.9%)
↓-31.3%prior 16
3
JEEP8 (10.1%)
4
PONTIAC4 (5.1%)
5
KIA3 (3.8%)
6
DODG3 (3.8%)
7
CHEV3 (3.8%)
8
BUICK2 (2.5%)
9
LEXUS2 (2.5%)
10
DODGE2 (2.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (57 persons with recorded sex)

Male37 (64.9%)
↓-19.6%prior 46
Female20 (35.1%)
→0.0%prior 20

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 56
  • Total persons involved: 86
  • Total vehicles involved: 79

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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