Yearly Traffic Safety Analysis

211 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Wright County, total vehicle crashes increased by 32.7% from 159 in 2015 to 211 in 2016. Despite this significant rise in collisions, the number of resulting injuries remained stable at 51 compared to 52 in the prior year, and fatalities fell from one to zero. The most notable shift was the increase in crashes attributed to animal involvement, which rose by 43.3% from 30 to 43 incidents.

211

32.7%was 159

Total Crash Events

0

-100.0%was 1

Persons Killed

51

-1.9%was 52

Persons Injured

0

-100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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

The overall trend in traffic collisions shows a significant year-over-year increase. The total number of crashes rose from 159 in 2015 to 211 in 2016, a 32.7% increase. However, this increase was primarily in non-injury incidents, as total injuries were nearly unchanged (51 vs. 52) and fatalities decreased from one to zero.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 1-100.0%

1

Cyclists Injured

Prior: 0%

50

Motorists Injured

Prior: 52-3.8%

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

Temporal patterns shifted between the two periods. The peak day for crashes moved from Monday (32 crashes) in the prior year to Wednesday (36 crashes) in the current year. The peak hour also changed, shifting from 9 p.m. (14 crashes) in 2015 to the 5 p.m. evening commute hour (18 crashes) 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

While total crashes rose, their overall severity decreased year-over-year. The county recorded zero fatal crashes in 2016, down from one in 2015. The number of serious injury crashes remained unchanged at six in both periods, but their share of total crashes fell from 3.8% to 2.8%. Correspondingly, the proportion of crashes resulting in no injuries increased from 75.5% to 78.2% of all incidents.

Outcome by Severity (Crash Events)

Serious Injury6serious injury crashes2.8%
0.0%prior 6
Minor Injury16minor injury crashes7.6%
-11.1%prior 18
Possible Injury24possible injury crashes11.4%
71.4%prior 14
No Injury165no injury crashes78.2%
37.5%prior 120

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 with animals remained the top contributing factor in both years, with the count of such incidents increasing by 43.3% from 30 to 43. 'Lost Control' was the second most common factor in both periods, also seeing an increase in count from 17 to 22 crashes. The top two primary causes maintained their rank year-over-year, but both grew in absolute numbers, contributing to the overall rise in crashes.

Officer-Reported Primary Contributing Cause

Animal43 (20.4%)43.3%prior 30
Lost Control22 (10.4%)29.4%prior 17
Other (explain in narrative): Other14 (6.6%)133.3%prior 6
Driving too fast for conditions13 (6.2%)30.0%prior 10
Ran off road - straight11 (5.2%)57.1%prior 7
Driver Distraction: Other interior distraction10 (4.7%)100.0%prior 5
Improper Backing8 (3.8%)
Followed too close8 (3.8%)
FTYROW: From stop sign7 (3.3%)0.0%prior 7
Ran off road - left6 (2.8%)20.0%prior 5

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

Road & Environmental Conditions

While most crashes in both years occurred in clear weather on dry roads, there was a notable increase in incidents under adverse conditions. Crashes on roads with ice or frost increased by 71.4%, from 14 to 24 incidents year-over-year. Similarly, crashes during snowfall doubled from 8 to 16. The share of crashes occurring in daylight also increased from 52.8% to 56.9% of the total.

Weather

Clear107 (62.6%)
23.0%prior 87
Cloudy29 (17.0%)
70.6%prior 17
Snow16 (9.4%)
100.0%prior 8
Rain8 (4.7%)
60.0%prior 5
Blowing Snow4 (2.3%)
Freezing rain/drizzle3 (1.8%)
Fog, smoke, smog3 (1.8%)
Severe Winds1 (0.6%)

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

Lighting

Daylight120 (70.2%)
42.9%prior 84
Dark - roadway not lighted28 (16.4%)
75.0%prior 16
Dark - roadway lighted13 (7.6%)
-13.3%prior 15
Dawn5 (2.9%)
0.0%prior 5
Dusk5 (2.9%)
-16.7%prior 6

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

Road Surface

Dry102 (59.3%)
24.4%prior 82
Ice/frost24 (14.0%)
71.4%prior 14
Snow18 (10.5%)
80.0%prior 10
Wet16 (9.3%)
14.3%prior 14
Gravel9 (5.2%)
12.5%prior 8
Slush2 (1.2%)
Mud, dirt1 (0.6%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Ford and Chevrolet brands being the most common in both 2015 and 2016. An analysis of persons involved shows a demographic shift, with a notable increase in the 55-64 age group, which grew from 28 individuals in 2015 to 48 in 2016. This represents an increase in their share of persons involved from 9.5% to 13.6%.

Top Vehicle Makes (318 vehicles)

1
FORD56 (17.6%)
36.6%prior 41
2
CHEVROLET36 (11.3%)
50.0%prior 24
3
CHEV26 (8.2%)
-29.7%prior 37
4
DODGE15 (4.7%)
36.4%prior 11
5
GMC11 (3.5%)
83.3%prior 6
6
CHRY10 (3.1%)
7
DODG10 (3.1%)
-33.3%prior 15
8
TOYOTA10 (3.1%)
9
INTERNATIONA9 (2.8%)
80.0%prior 5
10
FREIGHTLINER8 (2.5%)

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

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

Sex Distribution (228 persons with recorded sex)

Male149 (65.4%)
49.0%prior 100
Female79 (34.6%)
-20.2%prior 99

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: 211
  • Total persons involved: 354
  • Total vehicles involved: 318

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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Wright County, IA Crash Report — 2016 | ThatCarHitMe.com