Yearly Traffic Safety Analysis

963 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Clinton County recorded 963 total crashes, a 1.9% increase from the 945 crashes documented in 2015. Despite this slight rise in total incidents, the outcomes were less severe, with the number of people injured decreasing by 13.2% from 394 to 342. Additionally, the number of fatalities declined from 3 in the prior year to 2 in the current period.

963

▲ 1.9%was 945

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

342

▼ -13.2%was 394

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

Trend Summary

The overall trend in Clinton County shows a slight increase in the volume of crashes, which rose by 1.9% from 945 in 2015 to 963 in 2016. In contrast to the crash volume, key safety metrics showed improvement, as total injuries fell by 13.2% and fatalities decreased from 3 to 2 year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 3↓-33.3%

0

Other Killed

Prior: 0→0.0%

3

Pedestrians Injured

Prior: 7↓-57.1%

5

Cyclists Injured

Prior: 11↓-54.5%

332

Motorists Injured

Prior: 376↓-11.7%

2

Other Injured

Prior: 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 timing of crashes remained largely consistent between the two periods. Friday was the peak day for crashes in both 2016 (172 incidents) and 2015 (162 incidents). While the 5 p.m. hour was a peak time in both years, 2016 saw the 12 p.m. hour emerge as an equally high-frequency time slot, with both hours recording 76 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

Crash severity trended downward in 2016 compared to the previous year. The number of fatal crashes fell from 3 to 2, and the overall proportion of crashes resulting in any injury decreased from 31.4% in 2015 to 27.5% in 2016. Consequently, the share of crashes involving only property damage with no injuries increased from 68.3% to 72.3% of all incidents.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.2%
↓-33.3%prior 3
Serious Injury21serious injury crashes2.2%
↓-4.5%prior 22
Minor Injury90minor injury crashes9.3%
↓-6.3%prior 96
Possible Injury154possible injury crashes16%
↓-14.0%prior 179
No Injury696no injury crashes72.3%
↑7.9%prior 645

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 leading contributing factor, with the count of these crashes increasing by 18.1% from 127 in 2015 to 150 in 2016. A significant year-over-year shift was observed in crashes attributed to 'Ran Stop Sign', which doubled in count from 24 to 48 incidents. Conversely, crashes involving 'Failure to Yield Right of Way from a stop sign' became less frequent, with the count dropping by 22.7% from 88 to 68.

Officer-Reported Primary Contributing Cause

Animal150 (15.6%)↑18.1%prior 127
Other (explain in narrative): Other73 (7.6%)↓-6.4%prior 78
Followed too close69 (7.2%)↑9.5%prior 63
FTYROW: From stop sign68 (7.1%)↓-22.7%prior 88
Lost Control66 (6.9%)↓-13.2%prior 76
Ran Stop Sign48 (5%)↑100.0%prior 24
FTYROW: Making left turn44 (4.6%)↓-13.7%prior 51
Ran Traffic Signal34 (3.5%)↑54.5%prior 22
Ran off road - straight32 (3.3%)↓-5.9%prior 34
Driving too fast for conditions29 (3%)↓-6.5%prior 31

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 conditions under which crashes occurred were broadly similar year-over-year, with most incidents happening in daylight and on dry roads. However, there was a notable reduction in crashes on adverse road surfaces; incidents on wet, snow, or ice-covered roads collectively fell from 208 in 2015 to 161 in 2016. The proportion of crashes in clear weather decreased slightly from 56.1% to 52.8% of all crashes.

Weather

Clear508 (62.3%)
↓-4.2%prior 530
Cloudy220 (27.0%)
↑26.4%prior 174
Rain39 (4.8%)
↓-36.1%prior 61
Snow29 (3.6%)
→0.0%prior 29
Fog, smoke, smog6 (0.7%)
↓-14.3%prior 7
Freezing rain/drizzle4 (0.5%)
↓-55.6%prior 9
Blowing Snow3 (0.4%)
↓-50.0%prior 6
Sleet, hail3 (0.4%)
↓-57.1%prior 7
Severe Winds2 (0.2%)
Other (explain in narrative)2 (0.2%)

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

Lighting

Daylight578 (70.1%)
↓-2.5%prior 593
Dark - roadway lighted108 (13.1%)
↓-4.4%prior 113
Dark - roadway not lighted99 (12.0%)
↑2.1%prior 97
Dusk22 (2.7%)
↑4.8%prior 21
Dawn14 (1.7%)
↑27.3%prior 11
Dark - unknown roadway lighting3 (0.4%)

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

Road Surface

Dry634 (77.5%)
↑7.8%prior 588
Wet93 (11.4%)
↓-17.0%prior 112
Snow39 (4.8%)
↓-29.1%prior 55
Ice/frost29 (3.5%)
↓-29.3%prior 41
Gravel18 (2.2%)
↑5.9%prior 17
Slush2 (0.2%)
↓-89.5%prior 19
Mud, dirt2 (0.2%)
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

The most common vehicle makes involved in crashes, primarily Ford and Chevrolet models, remained consistent between 2015 and 2016. An analysis of persons involved in crashes shows a decrease in involvement across most age groups. The number of individuals aged 26-34 involved in crashes saw a significant drop from 290 in 2015 to 234 in 2016, while the 65+ age group also saw a reduction from 248 to 218 persons.

Top Vehicle Makes (1,632 vehicles)

1
FORD239 (14.6%)
↑1.3%prior 236
2
CHEVROLET213 (13.1%)
↑45.9%prior 146
3
CHEV165 (10.1%)
↓-24.3%prior 218
4
GMC70 (4.3%)
↑20.7%prior 58
5
DODG63 (3.9%)
↓-7.4%prior 68
6
DODGE58 (3.6%)
↓-6.5%prior 62
7
TOYOTA55 (3.4%)
↑14.6%prior 48
8
NR51 (3.1%)
↑30.8%prior 39
9
PONTIAC44 (2.7%)
↑76.0%prior 25
10
TOYT41 (2.5%)
↓-22.6%prior 53

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

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

Sex Distribution (1,142 persons with recorded sex)

Male611 (53.5%)
↓-15.8%prior 726
Female531 (46.5%)
↓-11.1%prior 597

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: 963
  • Total persons involved: 1,876
  • Total vehicles involved: 1,632

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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