Yearly Traffic Safety Analysis

482 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Sioux County, total traffic crashes increased from 443 in 2017 to 482 in 2018, an 8.8% rise. Despite the increase in overall collisions, the number of people injured decreased by 12.5% from 224 to 196, and fatalities fell from 3 to 2. The most significant shift was a substantial increase in crashes attributed to 'driving too fast for conditions,' which nearly doubled from 28 to 53 incidents.

482

▲ 8.8%was 443

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

196

▼ -12.5%was 224

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 · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crashes in Sioux County trended upward, increasing by 39 incidents from 443 in 2017 to 482 in 2018. This represents an 8.8% year-over-year increase in collisions. In contrast, the outcomes of these crashes became less severe, with total injuries dropping from 224 to 196 and fatalities declining from 3 to 2.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

2

Cyclists Injured

Prior: 2→0.0%

194

Motorists Injured

Prior: 217↓-10.6%

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 temporal patterns of crashes showed some shifts between the two periods. Friday remained the most frequent day for crashes, with incidents on this day increasing from 76 in 2017 to 89 in 2018. However, the peak hour for collisions moved later in the afternoon, shifting from the 3 p.m. hour (45 crashes) in 2017 to the 5 p.m. hour (43 crashes) in 2018.

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 year-over-year. The number of fatal crashes fell from 3 in 2017 to 2 in 2018, and the fatal crash rate dropped from 0.68% to 0.41%. The proportion of crashes resulting in a serious injury saw a notable decline, falling from 5.4% (24 crashes) of all incidents in 2017 to just 1.7% (8 crashes) in 2018. Overall, crashes involving any level of injury made up a smaller share of the total, decreasing from 35.9% in the prior year to 32.6% in the current year.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.4%
↓-33.3%prior 3
Serious Injury8serious injury crashes1.7%
↓-66.7%prior 24
Minor Injury59minor injury crashes12.2%
↓-9.2%prior 65
Possible Injury90possible injury crashes18.7%
↑28.6%prior 70
No Injury323no injury crashes67%
↑14.9%prior 281

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

The leading contributing factors shifted between periods. While 'Animal' was a primary factor in both years, its count increased from 67 to 80. The most significant change was in crashes attributed to 'driving too fast for conditions,' which rose from 28 incidents in 2017 to 53 in 2018, becoming the second-leading factor. Conversely, crashes involving 'followed too close' decreased in count from 64 to 50, and 'FTYROW: From stop sign' dropped from the third-ranked cause in 2017 (31 crashes) out of the top three in 2018 (26 crashes).

Officer-Reported Primary Contributing Cause

Animal80 (16.6%)↑19.4%prior 67
Driving too fast for conditions53 (11%)↑89.3%prior 28
Followed too close50 (10.4%)↓-21.9%prior 64
Lost Control30 (6.2%)↑7.1%prior 28
FTYROW: From stop sign26 (5.4%)↓-16.1%prior 31
Ran off road - straight24 (5%)↑14.3%prior 21
Other (explain in narrative): Other22 (4.6%)↑120.0%prior 10
Ran off road - left20 (4.1%)↓-20.0%prior 25
FTYROW: At uncontrolled intersection17 (3.5%)↑6.3%prior 16
FTYROW: Making left turn17 (3.5%)↑240.0%prior 5

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

Road & Environmental Conditions

There was a discernible shift toward more crashes occurring in adverse conditions. The proportion of collisions on adverse road surfaces like snow, ice, or wet pavement increased from 24.8% (110 crashes) in 2017 to 31.7% (153 crashes) in 2018. Similarly, crashes in dark conditions (both lighted and unlighted roadways) grew from 19.2% of the total in the prior year to 25.3% in the current year. Consequently, the share of crashes happening in daylight and on dry roads decreased.

Weather

Clear221 (53.3%)
↑5.7%prior 209
Cloudy111 (26.7%)
↑6.7%prior 104
Snow29 (7.0%)
↑11.5%prior 26
Rain19 (4.6%)
↑46.2%prior 13
Freezing rain/drizzle14 (3.4%)
↑133.3%prior 6
Fog, smoke, smog11 (2.7%)
↑83.3%prior 6
Blowing Snow7 (1.7%)
↓-36.4%prior 11
Severe Winds3 (0.7%)

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

Lighting

Daylight256 (61.7%)
↓-5.5%prior 271
Dark - roadway not lighted85 (20.5%)
↑46.6%prior 58
Dark - roadway lighted37 (8.9%)
↑37.0%prior 27
Dawn22 (5.3%)
↑37.5%prior 16
Dusk15 (3.6%)
↑66.7%prior 9

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

Road Surface

Dry230 (55.3%)
↓-5.7%prior 244
Snow56 (13.5%)
↑36.6%prior 41
Ice/frost51 (12.3%)
↑96.2%prior 26
Wet42 (10.1%)
↑7.7%prior 39
Gravel30 (7.2%)
↑25.0%prior 24
Slush4 (1.0%)
Other (explain in narrative)1 (0.2%)
Mud, dirt1 (0.2%)
Water (standing or moving)1 (0.2%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained broadly consistent, with Ford and Chevrolet (including 'CHEV' and 'CHEVROLET' records) being the most common in both years. The number of Fords involved increased from 119 to 151. Analysis of person-level data shows a shift in the age demographics of those involved in crashes. The count of individuals in the 16-20 age group decreased from 178 to 137, while involvement increased for those in the 35-44 age group (from 109 to 132) and the 55-64 age group (from 88 to 114).

Top Vehicle Makes (750 vehicles)

1
FORD151 (20.1%)
↑26.9%prior 119
2
CHEV123 (16.4%)
↑21.8%prior 101
3
CHEVROLET50 (6.7%)
↓-27.5%prior 69
4
GMC42 (5.6%)
↑75.0%prior 24
5
DODG34 (4.5%)
↑3.0%prior 33
6
BUIC29 (3.9%)
↑38.1%prior 21
7
DODGE27 (3.6%)
↑35.0%prior 20
8
JEEP27 (3.6%)
↑28.6%prior 21
9
PETERBILT23 (3.1%)
↑27.8%prior 18
10
PONT22 (2.9%)
↓-33.3%prior 33

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

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

Sex Distribution (548 persons with recorded sex)

Male350 (63.9%)
↓-2.0%prior 357
Female198 (36.1%)
↓-1.5%prior 201

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: 482
  • Total persons involved: 910
  • Total vehicles involved: 750

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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