Yearly Traffic Safety Analysis

496 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Muscatine County recorded 496 total crashes, a 10.9% decrease from the 557 crashes documented in 2019. While total fatalities remained constant at four, total injuries decreased by 9.7% from 186 to 168. The most notable year-over-year shift was the overall reduction in crash volume across most categories, accompanied by a change in the time of day when crashes were most frequent.

496

▼ -11.0%was 557

Total Crash Events

4

Persons Killed

168

▼ -9.7%was 186

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 Muscatine County shows a year-over-year decrease in traffic collisions. Total crashes fell by 61, from 557 in 2019 to 496 in 2020. Similarly, the number of people injured in these incidents declined from 186 to 168, while the number of fatalities held steady at four.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 2↓-100.0%

3

Motorists Killed

Prior: 2↑50.0%

2

Pedestrians Injured

Prior: 4↓-50.0%

3

Cyclists Injured

Prior: 9↓-66.7%

163

Motorists Injured

Prior: 173↓-5.8%

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

Temporal crash patterns shifted between the two periods. The peak day for crashes moved from Wednesday (93 crashes) in 2019 to Monday (88 crashes) in 2020. A more significant change occurred in the peak hour, which shifted from 2 p.m. in the prior period (45 crashes) to 7 p.m. in the current period (34 crashes), indicating a move from afternoon to evening for the highest crash frequency.

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 total number of fatal crashes was unchanged at four in both 2019 and 2020, the fatal crash rate increased slightly from 0.72% to 0.81% due to the lower overall crash total in 2020. The proportion of crashes involving any injury remained stable at approximately 28% for both years. Crashes categorized as 'Possible Injury' decreased from 90 to 70, while 'Minor Injury' crashes saw a small increase from 55 to 58.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.8%
→0.0%prior 4
Serious Injury11serious injury crashes2.2%
↓-8.3%prior 12
Minor Injury58minor injury crashes11.7%
↑5.5%prior 55
Possible Injury70possible injury crashes14.1%
↓-22.2%prior 90
No Injury353no injury crashes71.2%
↓-10.9%prior 396

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 were the leading contributing factor in both years, with counts remaining stable at 123 in 2019 and 126 in 2020. The count for crashes attributed to 'Followed too close' decreased substantially, from 37 incidents to 22. Incidents involving 'Failure to yield right of way from a stop sign' also decreased in count from 38 to 34, while 'Lost Control' crashes remained nearly the same, with 32 in 2019 and 33 in 2020.

Officer-Reported Primary Contributing Cause

Animal126 (25.4%)↑2.4%prior 123
FTYROW: From stop sign34 (6.9%)↓-10.5%prior 38
Lost Control33 (6.7%)↑3.1%prior 32
Other (explain in narrative): Other31 (6.3%)↓-29.5%prior 44
Ran off road - left22 (4.4%)↓-29.0%prior 31
Followed too close22 (4.4%)↓-40.5%prior 37
FTYROW: Making left turn20 (4%)↓-9.1%prior 22
Driving too fast for conditions20 (4%)↑42.9%prior 14
Ran Traffic Signal14 (2.8%)↑16.7%prior 12
Ran Stop Sign13 (2.6%)↑8.3%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

In both periods, most crashes occurred in clear weather on dry roads. However, there was a shift in the proportion of crashes under adverse conditions. The share of crashes on wet roads increased from 7.2% of all crashes in 2019 to 10.7% in 2020. Correspondingly, crashes during daylight hours decreased as a share of the total, from 56.6% to 50.2%, while crashes in dark, lighted conditions increased from 9.5% to 11.9%.

Weather

Clear275 (71.1%)
↓-16.9%prior 331
Cloudy47 (12.1%)
↓-17.5%prior 57
Rain24 (6.2%)
→0.0%prior 24
Snow17 (4.4%)
↓-29.2%prior 24
Freezing rain/drizzle9 (2.3%)
↓-10.0%prior 10
Other (explain in narrative)6 (1.6%)
Blowing Snow4 (1.0%)
Sleet, hail2 (0.5%)
Severe Winds1 (0.3%)
Fog, smoke, smog1 (0.3%)

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

Lighting

Daylight249 (64.0%)
↓-21.0%prior 315
Dark - roadway not lighted61 (15.7%)
↓-12.9%prior 70
Dark - roadway lighted59 (15.2%)
↑11.3%prior 53
Dawn11 (2.8%)
↑22.2%prior 9
Dusk7 (1.8%)
↑16.7%prior 6
Dark - unknown roadway lighting2 (0.5%)

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

Road Surface

Dry290 (74.6%)
↓-18.3%prior 355
Wet53 (13.6%)
↑32.5%prior 40
Ice/frost18 (4.6%)
↑5.9%prior 17
Snow14 (3.6%)
↓-56.3%prior 32
Gravel7 (1.8%)
→0.0%prior 7
Slush5 (1.3%)
Mud, dirt1 (0.3%)
Other (explain in narrative)1 (0.3%)

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

Vehicles & Demographics

The age distribution of persons involved in crashes shifted, with the 35-44 age group's representation increasing from 16.3% in 2019 to 18.2% in 2020. The top vehicle makes involved in crashes remained consistent, led by Chevrolet and Ford in both years. The number of vehicles from these top makes involved in crashes decreased, with Chevrolet vehicles dropping from 164 to 152 and Ford vehicles from 154 to 124.

Top Vehicle Makes (790 vehicles)

1
FORD124 (15.7%)
↓-19.5%prior 154
2
CHEVROLET78 (9.9%)
↑34.5%prior 58
3
CHEV74 (9.4%)
↓-30.2%prior 106
4
DODG40 (5.1%)
↓-20.0%prior 50
5
GMC39 (4.9%)
↑30.0%prior 30
6
TOYT38 (4.8%)
↓-50.0%prior 76
7
JEEP28 (3.5%)
↓-15.2%prior 33
8
DODGE28 (3.5%)
↑33.3%prior 21
9
NISS26 (3.3%)
↑4.0%prior 25
10
HONDA25 (3.2%)
↑78.6%prior 14

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

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

Sex Distribution (696 persons with recorded sex)

Male414 (59.5%)
↓-10.2%prior 461
Female282 (40.5%)
↓-17.1%prior 340

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: 496
  • Total persons involved: 1,144
  • Total vehicles involved: 790

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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