Yearly Traffic Safety Analysis

164 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Monona County, total traffic crashes increased by 21.5% from 135 in 2015 to 164 in 2016. This rise in crash volume was accompanied by a significant increase in crash severity, with total fatalities climbing from 3 to 5 and the number of fatal crashes more than doubling from 2 to 5 year-over-year.

164

▲ 21.5%was 135

Total Crash Events

5

▲ 66.7%was 3

Persons Killed

56

▼ -21.1%was 71

Persons Injured

5

▲ 150.0%was 2

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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 Monona County shows a notable increase in traffic incidents from 2015 to 2016. Total crashes rose from 135 to 164, an increase of 21.5%. While the number of fatalities increased from 3 to 5, the total number of injuries reported decreased from 71 to 56.

Vulnerable Road User Casualties

4

Motorists Killed

Prior: 3↑33.3%

1

Other Killed

Prior: 0→%

56

Motorists Injured

Prior: 70↓-20.0%

0

Other Injured

Prior: 1↓-100.0%

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 shifted between the two periods. In 2016, the peak day for crashes was Thursday with 29 incidents, a change from Monday (27 incidents) in the prior year. The peak hour also shifted from the 1 p.m. hour in 2015 (11 crashes) to the 6 p.m. hour in 2016 (13 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 worsened significantly in 2016 compared to 2015. The number of fatal crashes increased from 2 to 5, and the fatal crash rate more than doubled, rising from 1.48% to 3.05% of all crashes. While fatal incidents increased, the total count of injury-related crashes (Serious, Minor, and Possible) decreased from 47 in 2015 to 43 in 2016.

Outcome by Severity (Crash Events)

Fatal5fatal crashes3%
↑150.0%prior 2
Serious Injury10serious injury crashes6.1%
↑11.1%prior 9
Minor Injury11minor injury crashes6.7%
↓-21.4%prior 14
Possible Injury22possible injury crashes13.4%
↓-8.3%prior 24
No Injury116no injury crashes70.7%
↑34.9%prior 86

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 in both years, increasing from 33 incidents in 2015 to 40 in 2016. The second most common factor, 'Lost Control', also saw a significant rise, increasing by 50% from 16 to 24 crashes. Incidents attributed to 'Driving too fast for conditions' more than doubled, jumping from 5 crashes in 2015 to 12 in 2016.

Officer-Reported Primary Contributing Cause

Animal40 (24.4%)↑21.2%prior 33
Lost Control24 (14.6%)↑50.0%prior 16
Driving too fast for conditions12 (7.3%)↑140.0%prior 5
Ran off road - straight11 (6.7%)↓-8.3%prior 12
Followed too close9 (5.5%)↑28.6%prior 7
Driver Distraction: Other interior distraction8 (4.9%)↑60.0%prior 5
Operating vehicle in an reckless, erratic, careless, negligent manner7 (4.3%)
Other (explain in narrative): No improper action6 (3.7%)
FTYROW: From stop sign6 (3.7%)↑20.0%prior 5
Ran off road - right5 (3%)

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

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with 'Clear' weather, 'Daylight' lighting, and 'Dry' road surfaces accounting for the majority of incidents in both 2015 and 2016. However, there was a notable increase in crashes occurring on icy or frosty roads, which rose from 6 incidents in 2015 to 10 in 2016. Crashes in snowy weather also saw a slight increase from 6 to 9 incidents.

Weather

Clear91 (65.5%)
↑9.6%prior 83
Cloudy27 (19.4%)
↑58.8%prior 17
Snow9 (6.5%)
↑50.0%prior 6
Fog, smoke, smog5 (3.6%)
Rain3 (2.2%)
↓-40.0%prior 5
Freezing rain/drizzle2 (1.4%)
Severe Winds1 (0.7%)
Blowing sand, soil, dirt1 (0.7%)

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

Lighting

Daylight83 (60.1%)
↑13.7%prior 73
Dark - roadway not lighted35 (25.4%)
↑20.7%prior 29
Dawn7 (5.1%)
Dark - roadway lighted6 (4.3%)
↓-25.0%prior 8
Dusk5 (3.6%)
→0.0%prior 5
Dark - unknown roadway lighting2 (1.4%)

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

Road Surface

Dry98 (70.5%)
↑15.3%prior 85
Ice/frost10 (7.2%)
↑66.7%prior 6
Snow10 (7.2%)
↓-9.1%prior 11
Wet9 (6.5%)
↑50.0%prior 6
Gravel6 (4.3%)
↑20.0%prior 5
Mud, dirt3 (2.2%)
Slush2 (1.4%)
Other (explain in narrative)1 (0.7%)

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

Vehicles & Demographics

Ford and Chevrolet vehicles were the most frequently involved in crashes in both years, though their numbers shifted; Chevrolet involvement rose from 45 to 61 vehicles, while Ford involvement decreased from 38 to 32. The 45-54 age group, while still highly represented, saw its involvement decrease from 64 persons in 2015 to 44 in 2016. Conversely, the number of persons aged 16-20 involved in crashes increased from 27 to 34.

Top Vehicle Makes (224 vehicles)

1
FORD32 (14.3%)
↓-15.8%prior 38
2
CHEVROLET32 (14.3%)
↑45.5%prior 22
3
CHEV29 (12.9%)
↑26.1%prior 23
4
GMC13 (5.8%)
↑62.5%prior 8
5
DODGE13 (5.8%)
↑30.0%prior 10
6
JEEP10 (4.5%)
7
PONTIAC9 (4%)
8
BUICK6 (2.7%)
9
KIA5 (2.2%)
10
CHRY5 (2.2%)

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

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

Sex Distribution (163 persons with recorded sex)

Male89 (54.6%)
↓-11.9%prior 101
Female74 (45.4%)
↑2.8%prior 72

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: 164
  • Total persons involved: 262
  • Total vehicles involved: 224

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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