Yearly Traffic Safety Analysis

234 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Cass County, total traffic crashes increased by 7.3%, rising from 218 in 2019 to 234 in 2020. While total fatalities decreased from two to one, the number of injuries rose from 74 to 85. The most notable year-over-year shift was a significant increase in single-vehicle, non-collision incidents, which grew from 95 to 147, and a corresponding rise in crashes where an animal was the primary contributing factor.

234

▲ 7.3%was 218

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

85

▲ 14.9%was 74

Persons Injured

1

▼ -50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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

Overall traffic safety trends in Cass County showed a negative turn in 2020 compared to the prior year. The total number of crashes increased by 7.3% from 218 to 234, and the number of people injured grew by 14.9% from 74 to 85. However, the number of fatalities recorded in crashes decreased by 50%, falling from two in 2019 to one in 2020.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 2↓-50.0%

2

Pedestrians Injured

Prior: 1↑100.0%

1

Cyclists Injured

Prior: 3↓-66.7%

82

Motorists Injured

Prior: 70↑17.1%

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 periods. In 2020, Friday was the peak day for crashes with 47 incidents, a change from 2019 when Saturday saw the most crashes at 40. The peak hour for collisions also changed dramatically, moving from the afternoon commute at 2 p.m. (19 crashes) in 2019 to the early morning at 6 a.m. (17 crashes) in 2020.

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 total crashes increased, the severity profile showed mixed results year-over-year. The number of fatal crashes decreased from two to one, causing the fatal crash rate to fall from 0.92 to 0.43. Conversely, the count of serious injury crashes doubled from 5 to 10, and their share of total crashes increased from 2.3% in 2019 to 4.3% in 2020. Crashes resulting in no injuries decreased as a proportion of the total, from 74.3% to 72.6%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
↓-50.0%prior 2
Serious Injury10serious injury crashes4.3%
↑100.0%prior 5
Minor Injury25minor injury crashes10.7%
↓-10.7%prior 28
Possible Injury28possible injury crashes12%
↑33.3%prior 21
No Injury170no injury crashes72.6%
↑4.9%prior 162

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 involving an animal remained the top contributing factor in both years, with the count increasing by 63% from 38 incidents in 2019 to 62 in 2020. "Lost Control" also saw a significant 65% rise in count from 26 to 43 incidents, holding its position as the second-ranked factor. In contrast, crashes attributed to "Failure to Yield Right of Way from a stop sign" saw a notable decrease in count, falling from 19 in 2019 to 12 in 2020.

Officer-Reported Primary Contributing Cause

Animal62 (26.5%)↑63.2%prior 38
Lost Control43 (18.4%)↑65.4%prior 26
Ran off road - straight26 (11.1%)↑18.2%prior 22
Driving too fast for conditions15 (6.4%)↑7.1%prior 14
Ran off road - left14 (6%)→0.0%prior 14
Other (explain in narrative): Other12 (5.1%)↑50.0%prior 8
FTYROW: From stop sign12 (5.1%)↓-36.8%prior 19
Followed too close11 (4.7%)↓-26.7%prior 15
Driver Distraction: Other interior distraction5 (2.1%)
Other (explain in narrative): No improper action4 (1.7%)

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

Road & Environmental Conditions

There was a notable shift in lighting conditions for crashes year-over-year. The proportion of crashes occurring in daylight hours decreased from 55.0% in 2019 to 46.6% in 2020. Concurrently, the number of crashes in dark, unlighted conditions increased from 43 to 65, raising their share of the total from 19.7% to 27.8%. The distribution of crashes by weather and road surface conditions remained largely consistent, with clear weather and dry roads being the predominant conditions in both periods.

Weather

Clear121 (62.1%)
↑9.0%prior 111
Cloudy34 (17.4%)
↓-12.8%prior 39
Snow16 (8.2%)
↑33.3%prior 12
Rain10 (5.1%)
↑11.1%prior 9
Freezing rain/drizzle5 (2.6%)
↓-16.7%prior 6
Fog, smoke, smog4 (2.1%)
Blowing Snow3 (1.5%)
↓-72.7%prior 11
Severe Winds2 (1.0%)

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

Lighting

Daylight109 (55.1%)
↓-9.2%prior 120
Dark - roadway not lighted65 (32.8%)
↑51.2%prior 43
Dark - roadway lighted11 (5.6%)
↓-42.1%prior 19
Dawn8 (4.0%)
↑33.3%prior 6
Dusk4 (2.0%)
Dark - unknown roadway lighting1 (0.5%)

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

Road Surface

Dry135 (68.9%)
↑3.1%prior 131
Wet26 (13.3%)
↑44.4%prior 18
Snow16 (8.2%)
↓-11.1%prior 18
Ice/frost14 (7.1%)
↓-26.3%prior 19
Slush3 (1.5%)
Gravel2 (1.0%)

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 a significant shift, with Chevrolet vehicles (69) surpassing Ford (55) as the most common make in 2020, a reversal from 2019 when Ford led with 69 vehicles. Regarding driver demographics, the 26-34 age group was the most frequently involved group in both years, with its count increasing from 75 to 81 persons. Notably, the number of persons aged 16-20 involved in crashes more than doubled, rising from 25 in 2019 to 52 in 2020.

Top Vehicle Makes (313 vehicles)

1
FORD55 (17.6%)
↓-20.3%prior 69
2
CHEV42 (13.4%)
↑10.5%prior 38
3
CHEVROLET27 (8.6%)
↑50.0%prior 18
4
JEEP14 (4.5%)
↓-17.6%prior 17
5
GMC11 (3.5%)
↑22.2%prior 9
6
KIA11 (3.5%)
7
DODG11 (3.5%)
↓-42.1%prior 19
8
VOLVO10 (3.2%)
↑100.0%prior 5
9
CHRY9 (2.9%)
↑50.0%prior 6
10
FREIGHTLINER9 (2.9%)
↓-10.0%prior 10

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

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

Sex Distribution (290 persons with recorded sex)

Male190 (65.5%)
↓-2.1%prior 194
Female100 (34.5%)
↓-9.1%prior 110

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: 234
  • Total persons involved: 448
  • Total vehicles involved: 313

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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