Yearly Traffic Safety Analysis

163 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Grundy County recorded 163 total crashes, an 11.4% decrease from the 184 crashes documented in 2015. Despite the overall reduction in collisions, the county experienced a notable shift in crash severity, with two fatal crashes resulting in two deaths in 2016, compared to zero fatalities in the prior year. Additionally, crashes involving a driver under the influence increased from 1 to 10 year-over-year.

163

▼ -11.4%was 184

Total Crash Events

2

Persons Killed

45

▼ -30.8%was 65

Persons Injured

2

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 · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic safety trends in Grundy County were mixed. Total collisions decreased by 11.4% from 184 in 2015 to 163 in 2016, and the number of individuals injured fell by 30.8% from 65 to 45. However, this general improvement was offset by the occurrence of two traffic fatalities in 2016, a stark contrast to the zero fatalities recorded in the previous year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 0→%

1

Pedestrians Injured

Prior: 0→%

44

Motorists Injured

Prior: 65↓-32.3%

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 timing of crashes shifted between the two periods. In 2016, the peak day for crashes moved to Friday with 31 incidents, whereas Thursday was the peak day in 2015 with 34 incidents. The single busiest hour for crashes also shifted later, from 5 p.m. in 2015 (21 crashes) to 6 p.m. in 2016 (12 crashes). The afternoon commute period remained the highest-risk time of day in both years.

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 declined, the most severe outcomes increased. Grundy County registered two fatal crashes in 2016 after having none in 2015. The number of crashes involving serious injuries decreased slightly from four to three. The proportion of crashes resulting in no injuries remained stable, accounting for 74.2% of incidents in 2016 compared to 75.0% in 2015.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.2%
Serious Injury3serious injury crashes1.8%
↓-25.0%prior 4
Minor Injury21minor injury crashes12.9%
↑5.0%prior 20
Possible Injury16possible injury crashes9.8%
↓-27.3%prior 22
No Injury121no injury crashes74.2%
↓-12.3%prior 138

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 were the leading contributing factor in both years, though the count of such incidents decreased by 38%, from 73 in 2015 to 45 in 2016. 'Lost Control' remained the second-most cited factor, with its count dropping from 26 to 20. In contrast, crashes attributed to a vehicle running off a straight road increased from 13 to 17 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal45 (27.6%)↓-38.4%prior 73
Lost Control20 (12.3%)↓-23.1%prior 26
Ran off road - straight17 (10.4%)↑30.8%prior 13
Ran off road - left13 (8%)↑62.5%prior 8
Driving too fast for conditions9 (5.5%)↑28.6%prior 7
FTYROW: From stop sign6 (3.7%)↓-45.5%prior 11
Operating vehicle in an reckless, erratic, careless, negligent manner5 (3.1%)
Driver Distraction: Other interior distraction4 (2.5%)
Other (explain in narrative): Other4 (2.5%)↓-42.9%prior 7
FTYROW: Making left turn3 (1.8%)

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 distribution of crashes by weather condition changed notably, with the share of crashes in clear weather decreasing from 64.9% in 2015 to 46.3% in 2016. Concurrently, the proportion of crashes occurring in cloudy conditions more than doubled, rising from 14.0% to 33.3%. Proportions of crashes by lighting and road surface conditions, such as daylight hours and dry roads, remained largely consistent between the two years.

Weather

Clear57 (46.3%)
↓-23.0%prior 74
Cloudy41 (33.3%)
↑156.3%prior 16
Snow10 (8.1%)
↓-16.7%prior 12
Blowing Snow5 (4.1%)
Rain5 (4.1%)
↓-28.6%prior 7
Sleet, hail2 (1.6%)
Fog, smoke, smog2 (1.6%)
Freezing rain/drizzle1 (0.8%)

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

Lighting

Daylight89 (71.2%)
↑14.1%prior 78
Dark - roadway not lighted27 (21.6%)
↑12.5%prior 24
Dark - roadway lighted6 (4.8%)
→0.0%prior 6
Dawn2 (1.6%)
Dusk1 (0.8%)
↓-83.3%prior 6

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

Road Surface

Dry83 (67.5%)
↑6.4%prior 78
Snow13 (10.6%)
↓-27.8%prior 18
Wet12 (9.8%)
↑33.3%prior 9
Ice/frost11 (8.9%)
↑120.0%prior 5
Gravel2 (1.6%)
Other (explain in narrative)1 (0.8%)
Slush1 (0.8%)

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

Vehicles & Demographics

Ford and Chevrolet remained the two most common vehicle makes involved in crashes in both periods. The count of Fords involved increased from 36 to 40, while the combined count for Chevrolet vehicles rose from 46 to 52. The age distribution of persons involved in crashes was relatively stable, with a slight proportional increase in the 26-34 age group and minor decreases in the 16-20 and 65+ age groups.

Top Vehicle Makes (225 vehicles)

1
FORD40 (17.8%)
↑11.1%prior 36
2
CHEV27 (12%)
↑22.7%prior 22
3
CHEVROLET25 (11.1%)
↑4.2%prior 24
4
DODG10 (4.4%)
↑11.1%prior 9
5
PONT9 (4%)
↑28.6%prior 7
6
CHRYSLER9 (4%)
↑28.6%prior 7
7
JEEP8 (3.6%)
↑14.3%prior 7
8
TOYT6 (2.7%)
↓-33.3%prior 9
9
PONTIAC6 (2.7%)
→0.0%prior 6
10
BUIC6 (2.7%)
↓-25.0%prior 8

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

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

Sex Distribution (169 persons with recorded sex)

Male107 (63.3%)
↓-6.1%prior 114
Female62 (36.7%)
↓-42.6%prior 108

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 10, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 163
  • Total persons involved: 250
  • Total vehicles involved: 225

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 10, 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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