Yearly Traffic Safety Analysis

153 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Monona County recorded 153 total vehicle crashes, a 6.7% decrease from the 164 crashes reported in 2016. Despite the overall decline in collisions, the single most notable shift was an increase in fatalities from 5 in 2016 to 7 in 2017. This represents a 40% rise in traffic-related deaths year-over-year.

153

▼ -6.7%was 164

Total Crash Events

7

▲ 40.0%was 5

Persons Killed

44

▼ -21.4%was 56

Persons Injured

7

▲ 40.0%was 5

Fatal Crash Events

Note: "Persons Killed" (7) counts individual fatalities across all crash events. "Fatal" in the severity table below (7) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic collisions in Monona County showed a downward trend, decreasing by 6.7% from 164 incidents in 2016 to 153 in 2017. The number of persons injured also fell by 21.4%, from 56 to 44. However, this was contrasted by a 40% increase in total fatalities, which rose from 5 to 7 year-over-year.

Vulnerable Road User Casualties

7

Motorists Killed

Prior: 4↑75.0%

44

Motorists Injured

Prior: 56↓-21.4%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes in Monona County shifted between 2016 and 2017. The peak day for collisions moved from Thursday (29 crashes) in the prior year to Tuesday (30 crashes) in the current year. Similarly, the peak hour for crashes shifted later into the evening, from the 6 p.m. hour in 2016 (13 crashes) to the 10 p.m. hour in 2017 (15 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes increased in 2017 compared to the previous year. The fatal crash rate rose from 3.05% in 2016 to 4.58% in 2017, with the count of fatal crashes increasing from 5 to 7. While the share of serious injury crashes decreased slightly from 6.1% to 5.9%, the proportion of crashes resulting in any injury (serious, minor, or possible) increased from 26.2% to 27.5%.

Outcome by Severity (Crash Events)

Fatal7fatal crashes4.6%
↑40.0%prior 5
Serious Injury9serious injury crashes5.9%
↓-10.0%prior 10
Minor Injury11minor injury crashes7.2%
→0.0%prior 11
Possible Injury22possible injury crashes14.4%
→0.0%prior 22
No Injury104no injury crashes68%
↓-10.3%prior 116

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor in both periods, with the count increasing from 40 incidents in 2016 to 47 in 2017. 'Lost Control' was the second-most cited factor in both years, though its count decreased from 24 to 16. Crashes attributed to 'Driving too fast for conditions' fell from 12 to 9, while 'Ran off road - straight' incidents also declined from 11 to 9.

Officer-Reported Primary Contributing Cause

Animal47 (30.7%)↑17.5%prior 40
Lost Control16 (10.5%)↓-33.3%prior 24
Ran off road - straight9 (5.9%)↓-18.2%prior 11
Driving too fast for conditions9 (5.9%)↓-25.0%prior 12
Operating vehicle in an reckless, erratic, careless, negligent manner8 (5.2%)↑14.3%prior 7
Ran off road - left6 (3.9%)
Exceeded authorized speed6 (3.9%)
Driver Distraction: Other interior distraction5 (3.3%)↓-37.5%prior 8
Followed too close4 (2.6%)↓-55.6%prior 9
Other (explain in narrative): Other4 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

In both 2016 and 2017, the majority of crashes occurred in clear weather and on dry road surfaces. There was a notable shift in lighting conditions, with the share of crashes occurring in daylight decreasing from 50.6% to 45.1%. Correspondingly, crashes in dark, unlighted conditions increased, accounting for 29.4% of all incidents in 2017, up from 21.3% in 2016. The proportion of crashes on adverse road surfaces (wet, ice, or snow) declined from 18.9% in 2016 to 12.4% in 2017.

Weather

Clear84 (68.3%)
↓-7.7%prior 91
Cloudy27 (22.0%)
→0.0%prior 27
Freezing rain/drizzle4 (3.3%)
Snow3 (2.4%)
↓-66.7%prior 9
Rain2 (1.6%)
Fog, smoke, smog1 (0.8%)
↓-80.0%prior 5
Other (explain in narrative)1 (0.8%)
Blowing Snow1 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight69 (56.1%)
↓-16.9%prior 83
Dark - roadway not lighted45 (36.6%)
↑28.6%prior 35
Dark - roadway lighted4 (3.3%)
↓-33.3%prior 6
Dawn4 (3.3%)
↓-42.9%prior 7
Dark - unknown roadway lighting1 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry92 (74.8%)
↓-6.1%prior 98
Gravel11 (8.9%)
↑83.3%prior 6
Wet7 (5.7%)
↓-22.2%prior 9
Ice/frost6 (4.9%)
↓-40.0%prior 10
Snow6 (4.9%)
↓-40.0%prior 10
Mud, dirt1 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

Chevrolet and Ford were the two most common vehicle makes involved in crashes in both years, though the number of vehicles from both makes decreased in 2017. The count of Chevrolet vehicles in crashes fell from 61 to 50, and Ford vehicles from 32 to 31. The age demographics of persons involved also shifted; the 55-64 age group (41 persons) was the most represented in 2017, compared to the 45-54 age group (44 persons) in 2016.

Top Vehicle Makes (208 vehicles)

1
FORD31 (14.9%)
↓-3.1%prior 32
2
CHEVROLET26 (12.5%)
↓-18.8%prior 32
3
CHEV24 (11.5%)
↓-17.2%prior 29
4
DODG10 (4.8%)
↑100.0%prior 5
5
DODGE9 (4.3%)
↓-30.8%prior 13
6
JEEP9 (4.3%)
↓-10.0%prior 10
7
HONDA8 (3.8%)
8
INTERNATIONA6 (2.9%)
9
TOYT6 (2.9%)
10
FREIGHTLINER6 (2.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (149 persons with recorded sex)

Male102 (68.5%)
↑14.6%prior 89
Female47 (31.5%)
↓-36.5%prior 74

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 153
  • Total persons involved: 245
  • Total vehicles involved: 208

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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