Yearly Traffic Safety Analysis

204 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Madison County, total traffic crashes increased by 2%, from 200 in 2019 to 204 in 2020. The most significant year-over-year change was a sharp rise in crash severity, with total fatalities increasing from one to seven. Correspondingly, crashes involving a driver under the influence (DUI) increased from 3 in the prior year to 14 in the current year.

204

▲ 2.0%was 200

Total Crash Events

7

▲ 600.0%was 1

Persons Killed

56

▲ 5.7%was 53

Persons Injured

7

▲ 600.0%was 1

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

Trend Summary

Overall traffic collisions in Madison County saw a minor increase of 2% from 200 in 2019 to 204 in 2020. However, the severity of these incidents worsened significantly. Total injuries rose by 5.7% from 53 to 56, and total fatalities increased from one to seven year-over-year.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

6

Motorists Killed

Prior: 1↑500.0%

0

Pedestrians Injured

Prior: 1↓-100.0%

1

Cyclists Injured

Prior: 2↓-50.0%

55

Motorists Injured

Prior: 50↑10.0%

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 timing of crashes shifted between the two periods. In 2020, the peak day for crashes was Wednesday with 35 incidents, a change from Monday in 2019 which saw 34 crashes. The peak hour also moved from the evening commute at 5 p.m. in the prior year (19 crashes) to the morning at 8 a.m. in the current year (18 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

Crash severity notably increased in 2020 compared to the previous year. The number of fatal crashes rose from one to seven, and the fatal crash rate increased from 0.5% to 3.4% of all collisions. While the count of serious injury crashes decreased slightly from 8 to 7, the number of minor injury crashes grew from 17 to 30. Consequently, the share of crashes resulting in no injuries fell from 80.5% in 2019 to 73% in 2020.

Outcome by Severity (Crash Events)

Fatal7fatal crashes3.4%
↑600.0%prior 1
Serious Injury7serious injury crashes3.4%
↓-12.5%prior 8
Minor Injury30minor injury crashes14.7%
↑76.5%prior 17
Possible Injury11possible injury crashes5.4%
↓-15.4%prior 13
No Injury149no injury crashes73%
↓-7.5%prior 161

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 leading contributing factor in both years, though the count decreased from 59 in 2019 to 54 in 2020. A significant shift occurred with 'Driving too fast for conditions,' which fell from the second-ranked factor with 19 crashes in 2019 to just 6 crashes in 2020. Conversely, 'Failure to yield from a stop sign' saw its crash count increase from 8 to 15, making it the third most common factor in 2020.

Officer-Reported Primary Contributing Cause

Animal54 (26.5%)↓-8.5%prior 59
Lost Control17 (8.3%)↑13.3%prior 15
FTYROW: From stop sign15 (7.4%)↑87.5%prior 8
Followed too close13 (6.4%)↑62.5%prior 8
Ran off road - straight10 (4.9%)→0.0%prior 10
Ran off road - left10 (4.9%)↑66.7%prior 6
Other (explain in narrative): No improper action7 (3.4%)
Operating vehicle in an reckless, erratic, careless, negligent manner7 (3.4%)
Driving too fast for conditions6 (2.9%)↓-68.4%prior 19
Driver Distraction: Other interior distraction6 (2.9%)↓-50.0%prior 12

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

Road & Environmental Conditions

The proportion of crashes occurring on dry road surfaces increased from 39% in 2019 to 56.4% in 2020. A significant change was observed in lighting conditions; the share of crashes in daylight decreased from 56.5% to 52.5%, while crashes in dark, unlighted conditions rose substantially, accounting for 21.1% of all incidents in 2020 compared to 11.5% in 2019. The share of crashes in clear weather also increased from 49.5% to 56.9%.

Weather

Clear116 (69.9%)
↑17.2%prior 99
Cloudy31 (18.7%)
↑63.2%prior 19
Snow6 (3.6%)
↓-40.0%prior 10
Fog, smoke, smog5 (3.0%)
Freezing rain/drizzle4 (2.4%)
Rain4 (2.4%)
↓-42.9%prior 7

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

Lighting

Daylight107 (64.1%)
↓-5.3%prior 113
Dark - roadway not lighted43 (25.7%)
↑87.0%prior 23
Dawn9 (5.4%)
Dark - roadway lighted5 (3.0%)
↓-44.4%prior 9
Dusk3 (1.8%)

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

Road Surface

Dry115 (69.3%)
↑47.4%prior 78
Ice/frost18 (10.8%)
↑28.6%prior 14
Wet11 (6.6%)
↓-45.0%prior 20
Snow11 (6.6%)
↓-47.6%prior 21
Gravel8 (4.8%)
↓-38.5%prior 13
Mud, dirt2 (1.2%)
Slush1 (0.6%)

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

Vehicles & Demographics

The demographic profile of persons involved in crashes shifted, with the 16-20 age group being the most represented in 2019 (68 persons), while the 21-25 and 26-34 age groups were most common in 2020 (60 persons each). The top two vehicle makes involved in crashes remained consistent year-over-year. Chevrolet and Ford vehicles were the most frequently involved in both periods, with involvement counts increasing for both makes from 2019 to 2020.

Top Vehicle Makes (298 vehicles)

1
FORD54 (18.1%)
↑12.5%prior 48
2
CHEV54 (18.1%)
→0.0%prior 54
3
CHEVROLET27 (9.1%)
↑42.1%prior 19
4
JEEP17 (5.7%)
→0.0%prior 17
5
DODG14 (4.7%)
↑7.7%prior 13
6
GMC8 (2.7%)
↑33.3%prior 6
7
NISS8 (2.7%)
↑60.0%prior 5
8
HOND8 (2.7%)
↑14.3%prior 7
9
TOYT8 (2.7%)
↓-27.3%prior 11
10
DODGE8 (2.7%)

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 (279 persons with recorded sex)

Male174 (62.4%)
↑13.7%prior 153
Female105 (37.6%)
↓-9.5%prior 116

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: 204
  • Total persons involved: 414
  • Total vehicles involved: 298

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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