Yearly Traffic Safety Analysis

162 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Humboldt County, total crashes increased from 152 in 2016 to 162 in 2017, a rise of 6.6%. While total fatalities decreased from 3 to 2 and injuries fell from 74 to 65, the most significant change was in crashes involving a driver under the influence (DUI), which more than doubled from 5 to 11 incidents year-over-year.

162

▲ 6.6%was 152

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

65

▼ -12.2%was 74

Persons Injured

2

▼ -33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 traffic collisions in Humboldt County showed an upward trend, increasing by 6.6% from 152 in 2016 to 162 in 2017. Despite the rise in total crashes, the number of resulting injuries decreased by 12.2% (from 74 to 65), and the number of fatalities fell from 3 to 2.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 3↓-33.3%

0

Other Killed

Prior: 0→0.0%

64

Motorists Injured

Prior: 73↓-12.3%

1

Other Injured

Prior: 0→%

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 timing of crashes shifted significantly between the two periods. In 2017, the peak day for crashes was Friday with 34 incidents, a change from 2016 when Monday was the peak day with 27 crashes. The peak hour also moved from the afternoon at 3 PM (12 crashes) in 2016 to late evening at 10 PM (15 crashes) in 2017.

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

The severity of crashes generally lessened in 2017 compared to the prior year. The fatal crash rate decreased from 1.97% to 1.23%, with 2 fatal crashes in 2017 versus 3 in 2016. The proportion of crashes resulting in minor injuries also saw a notable drop, from 15.8% of all crashes in 2016 to 8.0% in 2017, while the number of serious injury crashes remained stable at 7.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.2%
↓-33.3%prior 3
Serious Injury7serious injury crashes4.3%
→0.0%prior 7
Minor Injury13minor injury crashes8%
↓-45.8%prior 24
Possible Injury21possible injury crashes13%
→0.0%prior 21
No Injury119no injury crashes73.5%
↑22.7%prior 97

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 an animal remained the top contributing factor in both years, with the count increasing by 27.6% from 29 incidents in 2016 to 37 in 2017. The second most common factor shifted from 'Lost Control' in 2016 (21 crashes) to 'Ran off road - straight' in 2017 (16 crashes). Notably, crashes attributed to 'Driving too fast for conditions' saw a 160% increase in count, rising from 5 to 13 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal37 (22.8%)↑27.6%prior 29
Ran off road - straight16 (9.9%)↑45.5%prior 11
Lost Control15 (9.3%)↓-28.6%prior 21
Driving too fast for conditions13 (8%)↑160.0%prior 5
Other (explain in narrative): Other12 (7.4%)↓-25.0%prior 16
Ran off road - left8 (4.9%)↑33.3%prior 6
FTYROW: At uncontrolled intersection6 (3.7%)
Driver Distraction: Other interior distraction6 (3.7%)↓-14.3%prior 7
Ran Stop Sign5 (3.1%)↓-37.5%prior 8
FTYROW: From stop sign5 (3.1%)↓-16.7%prior 6

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

Road & Environmental Conditions

Crashes in 2017 were more likely to occur in adverse conditions compared to 2016. The proportion of crashes happening in daylight decreased from 64.5% of all crashes in 2016 to 48.8% in 2017, while crashes in unlit dark conditions increased from 19 to 31 incidents. Similarly, the share of crashes on dry road surfaces fell from 56.6% to 46.9%, with incidents on icy or frosty roads increasing from 14 to 20.

Weather

Clear65 (50.4%)
↓-18.8%prior 80
Cloudy40 (31.0%)
↑33.3%prior 30
Freezing rain/drizzle8 (6.2%)
Rain7 (5.4%)
Snow5 (3.9%)
↓-28.6%prior 7
Fog, smoke, smog2 (1.6%)
Blowing Snow2 (1.6%)

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

Lighting

Daylight79 (61.2%)
↓-19.4%prior 98
Dark - roadway not lighted31 (24.0%)
↑63.2%prior 19
Dark - roadway lighted9 (7.0%)
↑28.6%prior 7
Dawn4 (3.1%)
↓-20.0%prior 5
Dusk4 (3.1%)
Dark - unknown roadway lighting2 (1.6%)

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

Road Surface

Dry76 (58.9%)
↓-11.6%prior 86
Ice/frost20 (15.5%)
↑42.9%prior 14
Wet18 (14.0%)
↑50.0%prior 12
Snow10 (7.8%)
↓-28.6%prior 14
Gravel4 (3.1%)
Slush1 (0.8%)

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

Vehicles & Demographics

The primary vehicle makes involved in crashes remained consistent year-over-year, with Chevrolet and Ford vehicles representing the top two in both 2016 and 2017. Regarding the age of persons involved, the 21-25 age group saw an increase from 20 to 26 individuals. Conversely, the 16-20 age group saw a decrease from 39 to 33 persons involved in crashes.

Top Vehicle Makes (226 vehicles)

1
FORD42 (18.6%)
↑16.7%prior 36
2
CHEV35 (15.5%)
↑59.1%prior 22
3
CHEVROLET21 (9.3%)
↓-41.7%prior 36
4
DODG19 (8.4%)
↑111.1%prior 9
5
GMC17 (7.5%)
↑30.8%prior 13
6
BUIC10 (4.4%)
↑66.7%prior 6
7
CHRY9 (4%)
↑80.0%prior 5
8
TOYT9 (4%)
↑50.0%prior 6
9
PONT5 (2.2%)
↓-16.7%prior 6
10
TOYOTA4 (1.8%)
↓-60.0%prior 10

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

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

Sex Distribution (161 persons with recorded sex)

Male86 (53.4%)
↓-20.4%prior 108
Female75 (46.6%)
↑23.0%prior 61

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: 162
  • Total persons involved: 278
  • Total vehicles involved: 226

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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