Yearly Traffic Safety Analysis

150 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Monona County, total crashes decreased from 172 in 2019 to 150 in 2020, a 12.8% reduction. While fatalities remained stable at two deaths in both years, the most significant change was a 69.2% decrease in crashes involving a driver under the influence (DUI), which fell from 13 incidents in 2019 to four in 2020.

150

▼ -12.8%was 172

Total Crash Events

2

Persons Killed

58

▲ 1.8%was 57

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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in traffic crashes in Monona County was downward from 2019 to 2020. Total collisions fell by 12.8%, from 172 to 150. Despite this decrease in total incidents, the number of injuries saw a slight increase from 57 to 58, and fatalities held steady at two for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 2→0.0%

1

Pedestrians Injured

Prior: 1→0.0%

57

Motorists Injured

Prior: 56↑1.8%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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 years. The peak day for crashes moved from Friday (31 incidents) in 2019 to a tie between Sunday and Wednesday (27 incidents each) in 2020. The peak hour for collisions also shifted later, from 6 p.m. in the prior year (15 crashes) to 8 p.m. in the current year (12 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the number of fatal crashes remained constant at two, the fatal crash rate increased from 1.16% in 2019 to 1.33% in 2020 due to the lower overall crash total. The proportion of crashes resulting in a possible injury increased from 8.7% of all crashes in 2019 to 14.0% in 2020. Correspondingly, the share of no-injury crashes decreased from 73.8% to 68.0% year-over-year.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.3%
→0.0%prior 2
Serious Injury10serious injury crashes6.7%
↓-9.1%prior 11
Minor Injury15minor injury crashes10%
↓-11.8%prior 17
Possible Injury21possible injury crashes14%
↑40.0%prior 15
No Injury102no injury crashes68%
↓-19.7%prior 127

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the top contributing factor in both periods, though the count of such incidents decreased by 23.1% from 52 in 2019 to 40 in 2020. "Lost Control" was the second-most cited factor in both years, with a nearly stable count of 20 and 19 crashes, respectively. A notable increase was observed in crashes attributed to running a stop sign, which rose from two incidents in 2019 to six in 2020.

Officer-Reported Primary Contributing Cause

Animal40 (26.7%)↓-23.1%prior 52
Lost Control19 (12.7%)↓-5.0%prior 20
Driving too fast for conditions13 (8.7%)↓-13.3%prior 15
Ran off road - straight11 (7.3%)↓-26.7%prior 15
Ran off road - left10 (6.7%)
Ran Stop Sign6 (4%)
Operating vehicle in an reckless, erratic, careless, negligent manner4 (2.7%)↓-55.6%prior 9
Other (explain in narrative): Other4 (2.7%)↓-55.6%prior 9
Followed too close4 (2.7%)↓-33.3%prior 6
FTYROW: From stop sign3 (2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions remained broadly consistent year-over-year, with a majority of incidents in both periods occurring in clear weather and on dry roads. Crashes on dry roads accounted for 54.1% of the total in 2019 and 56.7% in 2020. Incidents during daylight hours decreased from 84 to 67, while crashes in unlit dark conditions remained relatively steady, with 41 in 2019 and 38 in 2020.

Weather

Clear88 (73.3%)
↓-6.4%prior 94
Cloudy11 (9.2%)
↓-60.7%prior 28
Snow7 (5.8%)
→0.0%prior 7
Blowing Snow5 (4.2%)
Freezing rain/drizzle4 (3.3%)
Rain3 (2.5%)
Other (explain in narrative)1 (0.8%)
Severe Winds1 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Weather condition at time of crash

Lighting

Daylight67 (55.8%)
↓-20.2%prior 84
Dark - roadway not lighted38 (31.7%)
↓-7.3%prior 41
Dark - roadway lighted7 (5.8%)
↑40.0%prior 5
Dawn4 (3.3%)
↓-50.0%prior 8
Dusk4 (3.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Lighting condition field

Road Surface

Dry85 (70.2%)
↓-8.6%prior 93
Ice/frost10 (8.3%)
↓-16.7%prior 12
Snow10 (8.3%)
↑11.1%prior 9
Wet7 (5.8%)
↓-22.2%prior 9
Gravel4 (3.3%)
↓-63.6%prior 11
Mud, dirt3 (2.5%)
Slush2 (1.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes saw some shifts; Chevrolet and Ford vehicles, the top two makes in both years, saw their crash involvement counts decrease from 64 to 51 and from 38 to 25, respectively. The age demographics of persons involved in crashes also changed, highlighted by a 64% increase in the number of persons aged 16-20, from 28 in 2019 to 46 in 2020. Conversely, involvement of persons aged 21-25 decreased from 45 to 27.

Top Vehicle Makes (204 vehicles)

1
CHEV35 (17.2%)
↓-7.9%prior 38
2
FORD25 (12.3%)
↓-34.2%prior 38
3
CHEVROLET16 (7.8%)
↓-38.5%prior 26
4
DODGE10 (4.9%)
↑100.0%prior 5
5
DODG10 (4.9%)
↑42.9%prior 7
6
JEEP9 (4.4%)
7
FREIGHTLINER9 (4.4%)
8
HONDA8 (3.9%)
9
GMC7 (3.4%)
↓-22.2%prior 9
10
NISS6 (2.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Vehicle unit records

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

Sex Distribution (193 persons with recorded sex)

Male124 (64.2%)
↓-3.1%prior 128
Female69 (35.8%)
↓-13.8%prior 80

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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: 2020-01-01 through 2020-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 150
  • Total persons involved: 298
  • Total vehicles involved: 204

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: 2020." Published September 9, 2026. Reporting period: 2020-01-01 to 2020-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2020-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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