Yearly Traffic Safety Analysis

2,354 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Woodbury County, total crashes remained nearly stable, with 2,354 incidents in 2016 compared to 2,358 in 2015, a decrease of less than 1%. While the overall crash count was steady, there was a notable 13.9% increase in the number of people injured, which rose from 820 to 934 year-over-year. This increase in injuries occurred even as total fatalities decreased from 13 to 10.

2,354

▼ -0.2%was 2,358

Total Crash Events

10

▼ -23.1%was 13

Persons Killed

934

▲ 13.9%was 820

Persons Injured

9

▼ -25.0%was 12

Fatal Crash Events

Note: "Persons Killed" (10) counts individual fatalities across all crash events. "Fatal" in the severity table below (9) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crash volume in Woodbury County was stable year-over-year, decreasing by just 4 incidents from 2,358 in 2015 to 2,354 in 2016. However, the outcomes of these crashes shifted, with total injuries increasing by 13.9% (from 820 to 934). In contrast, total fatalities fell by 23.1% (from 13 to 10).

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

10

Motorists Killed

Prior: 12↓-16.7%

0

Other Killed

Prior: 0→0.0%

24

Pedestrians Injured

Prior: 26↓-7.7%

21

Cyclists Injured

Prior: 28↓-25.0%

885

Motorists Injured

Prior: 762↑16.1%

4

Other Injured

Prior: 4→0.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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 consistency year-over-year, with the afternoon commute hours being the most frequent time for incidents in both periods. Friday remained the peak day for crashes in both 2015 (411 crashes) and 2016 (433 crashes). The peak hour shifted slightly later, from the 4 p.m. hour in 2015 (207 crashes) to the 5 p.m. hour in 2016 (199 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes shifted towards more injury-involved incidents in 2016. While the proportion of fatal crashes decreased from 0.5% to 0.4% of all crashes, the share of crashes resulting in a possible injury grew from 20.6% in 2015 to 24.0% in 2016. Consequently, the proportion of no-injury crashes decreased from 69.0% of the total in 2015 to 65.6% in 2016.

Severity is per crash event (most severe injury). 9 fatal crash events resulted in 10 persons killed.

Outcome by Severity (Crash Events)

Fatal9fatal crashes0.4%
↓-25.0%prior 12
Serious Injury32serious injury crashes1.4%
↓-17.9%prior 39
Minor Injury205minor injury crashes8.7%
↑6.2%prior 193
Possible Injury564possible injury crashes24%
↑16.0%prior 486
No Injury1,544no injury crashes65.6%
↓-5.2%prior 1,628

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record

Top Contributing Factors

'Followed too close' remained the leading contributing factor in both periods, with its count increasing from 271 in 2015 to 306 in 2016. The ranking of other top factors shifted; 'Ran off road - left' moved from the fifth-ranked factor to the second, as its count increased from 147 to 184. Conversely, crashes attributed to 'FTYROW: From stop sign' decreased in count from 204 to 150, and 'Ran Traffic Signal' incidents fell from 149 to 118.

Officer-Reported Primary Contributing Cause

Followed too close306 (13%)↑12.9%prior 271
Ran off road - left184 (7.8%)↑25.2%prior 147
FTYROW: From stop sign150 (6.4%)↓-26.5%prior 204
Other (explain in narrative): Other145 (6.2%)↓-10.5%prior 162
FTYROW: Making left turn134 (5.7%)↑28.8%prior 104
Driving too fast for conditions122 (5.2%)↓-12.9%prior 140
Animal120 (5.1%)↓-11.1%prior 135
Ran Traffic Signal118 (5%)↓-20.8%prior 149
Lost Control102 (4.3%)↓-11.3%prior 115
Ran Stop Sign92 (3.9%)↑8.2%prior 85

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in 2016 occurred under generally more favorable road conditions compared to 2015, despite a stable total crash volume. The proportion of crashes on dry road surfaces increased from 64.6% to 70.2%, while incidents on wet roads decreased from a 14.9% share to a 10.8% share. Similarly, crashes during rain or snow represented a smaller share of the total in 2016 (7.6%) than in 2015 (13.4%).

Weather

Clear1,446 (65.2%)
↑5.7%prior 1,368
Cloudy544 (24.5%)
↑12.2%prior 485
Rain92 (4.1%)
↓-46.5%prior 172
Snow88 (4.0%)
↓-38.9%prior 144
Freezing rain/drizzle24 (1.1%)
↓-11.1%prior 27
Fog, smoke, smog10 (0.5%)
Blowing Snow10 (0.5%)
↓-37.5%prior 16
Sleet, hail4 (0.2%)
Severe Winds1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight1,542 (69.1%)
↓-2.7%prior 1,585
Dark - roadway lighted402 (18.0%)
↑3.3%prior 389
Dark - roadway not lighted153 (6.9%)
↑26.4%prior 121
Dusk73 (3.3%)
↑9.0%prior 67
Dawn53 (2.4%)
↓-15.9%prior 63
Dark - unknown roadway lighting7 (0.3%)
↑40.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry1,652 (74.3%)
↑8.4%prior 1,524
Wet253 (11.4%)
↓-28.1%prior 352
Snow159 (7.2%)
↓-9.1%prior 175
Ice/frost82 (3.7%)
↓-6.8%prior 88
Slush41 (1.8%)
↓-25.5%prior 55
Gravel30 (1.3%)
↑57.9%prior 19
Mud, dirt3 (0.1%)
↓-75.0%prior 12
Other (explain in narrative)2 (0.1%)
Water (standing or moving)1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Chevrolet, Ford, and Dodge vehicles being the top three in both years. An analysis of persons involved in crashes shows a shift in age representation; the proportion of individuals in the 16-20 age group decreased from 12.8% of all persons in 2015 to 11.3% in 2016. Similarly, the 65+ age group's share fell from 9.7% to 8.1%.

Top Vehicle Makes (4,371 vehicles)

1
FORD662 (15.1%)
↓-3.9%prior 689
2
CHEVROLET488 (11.2%)
↑17.9%prior 414
3
CHEV386 (8.8%)
↓-19.9%prior 482
4
JEEP172 (3.9%)
↑33.3%prior 129
5
DODGE168 (3.8%)
↓-2.3%prior 172
6
GMC167 (3.8%)
↓-2.3%prior 171
7
TOYOTA150 (3.4%)
↑70.5%prior 88
8
NR146 (3.3%)
↑15.0%prior 127
9
DODG144 (3.3%)
↓-14.8%prior 169
10
HONDA141 (3.2%)
↑36.9%prior 103

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (3,001 persons with recorded sex)

Male1,712 (57.0%)
↓-13.5%prior 1,980
Female1,289 (43.0%)
↓-16.6%prior 1,546

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 2,354
  • Total persons involved: 5,127
  • Total vehicles involved: 4,371

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: 2016." Published September 9, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-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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