Yearly Traffic Safety Analysis

910 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Webster County recorded 910 total crashes, a 15.5% increase from the 788 crashes reported in 2015. While total fatalities decreased from 7 to 6, one of the most notable shifts was a 63.6% rise in crashes involving a driver under the influence (DUI), which increased from 22 incidents in 2015 to 36 in 2016.

910

▲ 15.5%was 788

Total Crash Events

6

▼ -14.3%was 7

Persons Killed

225

▲ 0.4%was 224

Persons Injured

6

▲ 20.0%was 5

Fatal Crash Events

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (6) 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

Crash incidents in Webster County showed an upward trend from 2015 to 2016. The total number of crashes rose by 15.5%, from 788 to 910. Despite this increase in total collisions, the number of resulting injuries remained nearly identical at 225 compared to 224 the previous year, and fatalities decreased from 7 to 6.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

6

Motorists Killed

Prior: 7↓-14.3%

6

Pedestrians Injured

Prior: 7↓-14.3%

5

Cyclists Injured

Prior: 4↑25.0%

214

Motorists Injured

Prior: 211↑1.4%

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 primary temporal patterns for crashes in Webster County were consistent year-over-year. Friday was the peak day for crashes in both 2016 (159 crashes) and 2015 (144 crashes), and the 3 PM hour was the peak time in both periods. However, the volume of crashes on Saturdays increased substantially, rising from 85 incidents in 2015 to 143 in 2016.

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 overall severity of crashes shifted slightly towards less severe outcomes, with the proportion of non-injury crashes increasing from 74.0% in 2015 to 76.3% in 2016. The share of crashes involving possible injuries decreased from 17.0% to 14.5% over the same period. However, the number of fatal crashes increased from 5 to 6, and the corresponding fatal crash rate rose from 0.63% to 0.66%.

Outcome by Severity (Crash Events)

Fatal6fatal crashes0.7%
↑20.0%prior 5
Serious Injury18serious injury crashes2%
↑12.5%prior 16
Minor Injury60minor injury crashes6.6%
↑20.0%prior 50
Possible Injury132possible injury crashes14.5%
↓-1.5%prior 134
No Injury694no injury crashes76.3%
↑19.0%prior 583

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

Collisions involving animals remained the leading contributing factor in both periods, with the count increasing from 82 in 2015 to 109 in 2016. 'Ran off road - left' saw a significant rise in incidents, more than doubling from 24 to 53, making it the fourth most common factor in 2016. In contrast, crashes attributed to 'Followed too close' decreased in count from 61 to 52, while incidents of 'Failure to yield from a stop sign' increased from 58 to 61.

Officer-Reported Primary Contributing Cause

Animal109 (12%)↑32.9%prior 82
Other (explain in narrative): Other82 (9%)↑15.5%prior 71
FTYROW: From stop sign61 (6.7%)↑5.2%prior 58
Ran off road - left53 (5.8%)↑120.8%prior 24
Followed too close52 (5.7%)↓-14.8%prior 61
Driving too fast for conditions50 (5.5%)→0.0%prior 50
Lost Control35 (3.8%)↑12.9%prior 31
FTYROW: Making left turn34 (3.7%)↑6.3%prior 32
Ran off road - straight26 (2.9%)↑36.8%prior 19
Driver Distraction: Other interior distraction25 (2.7%)↑4.2%prior 24

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

Road & Environmental Conditions

The majority of crashes in both 2016 and 2015 occurred in clear weather and during daylight hours, with the proportions remaining largely stable. However, there was a notable increase in the number of crashes occurring in adverse road conditions. Crashes on roads with ice or frost increased from 53 to 66 year-over-year, and collisions on wet surfaces rose from 78 in 2015 to 104 in 2016.

Weather

Clear505 (62.3%)
↑11.2%prior 454
Cloudy196 (24.2%)
↑13.3%prior 173
Snow40 (4.9%)
↑37.9%prior 29
Rain36 (4.4%)
↑2.9%prior 35
Freezing rain/drizzle18 (2.2%)
↑100.0%prior 9
Fog, smoke, smog6 (0.7%)
Blowing Snow6 (0.7%)
Other (explain in narrative)2 (0.2%)
Severe Winds1 (0.1%)
Blowing sand, soil, dirt1 (0.1%)

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

Lighting

Daylight553 (68.3%)
↑11.7%prior 495
Dark - roadway lighted140 (17.3%)
↑29.6%prior 108
Dark - roadway not lighted66 (8.1%)
↑3.1%prior 64
Dusk27 (3.3%)
↑17.4%prior 23
Dawn16 (2.0%)
↑220.0%prior 5
Dark - unknown roadway lighting8 (1.0%)
↑33.3%prior 6

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

Road Surface

Dry555 (68.6%)
↑11.9%prior 496
Wet104 (12.9%)
↑33.3%prior 78
Ice/frost66 (8.2%)
↑24.5%prior 53
Snow60 (7.4%)
→0.0%prior 60
Slush11 (1.4%)
↑22.2%prior 9
Gravel11 (1.4%)
↑37.5%prior 8
Oil1 (0.1%)
Water (standing or moving)1 (0.1%)

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

Vehicles & Demographics

Ford and Chevrolet were the most common vehicle makes involved in crashes in both periods, with their counts remaining relatively stable. Analysis of persons involved shows the 16-20 age group saw a notable increase in crash involvement, rising from 234 individuals in 2015 to 271 in 2016. Conversely, the number of persons aged 65 and older involved in crashes decreased from 200 to 177.

Top Vehicle Makes (1,611 vehicles)

1
FORD232 (14.4%)
↓-0.9%prior 234
2
CHEV207 (12.8%)
↓-4.2%prior 216
3
CHEVROLET142 (8.8%)
↑46.4%prior 97
4
NR75 (4.7%)
↑47.1%prior 51
5
DODG70 (4.3%)
↓-10.3%prior 78
6
GMC64 (4%)
↑56.1%prior 41
7
TOYO64 (4%)
↑166.7%prior 24
8
BUIC62 (3.8%)
↑26.5%prior 49
9
DODGE50 (3.1%)
↑19.0%prior 42
10
PONTIAC45 (2.8%)
↑181.3%prior 16

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

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

Sex Distribution (1,141 persons with recorded sex)

Male620 (54.3%)
↑2.6%prior 604
Female521 (45.7%)
↓-3.2%prior 538

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: 910
  • Total persons involved: 1,795
  • Total vehicles involved: 1,611

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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