Monthly Traffic Safety Analysis

2,368 CRASHES IN
IOWA, IA
APRIL 2020

All metrics benchmarked againstApril 2019

In April 2020, there were 2,368 total crashes, a 41.2% decrease from the 4,026 crashes recorded in April 2019. This substantial drop in overall crash volume was the most notable year-over-year change. Despite the decrease in total incidents, the fatal crash rate saw an increase from 0.52% to 0.68%.

2,368

▼ -41.2%was 4,026

Total Crash Events

17

▼ -22.7%was 22

Persons Killed

790

▼ -41.7%was 1,356

Persons Injured

16

▼ -23.8%was 21

Fatal Crash Events

Note: "Persons Killed" (17) counts individual fatalities across all crash events. "Fatal" in the severity table below (16) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2020-04-01 to 2020-04-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data shows a significant downward trend year-over-year, with total crashes falling by 41.2% from 4,026 to 2,368. This decline was mirrored in casualties, as total injuries decreased from 1,356 to 790. Fatalities also saw a reduction, dropping from 22 in April 2019 to 17 in April 2020.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 2→0.0%

0

Cyclists Killed

Prior: 1↓-100.0%

15

Motorists Killed

Prior: 19↓-21.1%

0

Other Killed

Prior: 0→0.0%

11

Pedestrians Injured

Prior: 26↓-57.7%

11

Cyclists Injured

Prior: 29↓-62.1%

765

Motorists Injured

Prior: 1,300↓-41.2%

3

Other Injured

Prior: 1↑200.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-04-01 to 2020-04-30 · 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. The peak day for crashes moved from Friday (699 incidents) in 2019 to Wednesday (421 incidents) in 2020. The peak hour also shifted slightly earlier from 4 p.m. in the prior year to 3 p.m. in the current period, with a significantly lower volume of crashes during this peak.

Source: Iowa Crash Data · ArcGIS Open Data · 2020-04-01 to 2020-04-30 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-04-01 to 2020-04-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the absolute number of fatal crashes decreased from 21 to 16, the fatal crash rate increased from 0.52% to 0.68% year-over-year. The proportion of crashes resulting in a serious injury also grew, rising from 2.0% in April 2019 to 2.8% in April 2020. The share of crashes with no injuries saw a slight decrease from 70.9% to 69.8%.

Severity is per crash event (most severe injury). 16 fatal crash events resulted in 17 persons killed.

Outcome by Severity (Crash Events)

Fatal16fatal crashes0.7%
↓-23.8%prior 21
Serious Injury66serious injury crashes2.8%
↓-17.5%prior 80
Minor Injury249minor injury crashes10.5%
↓-41.7%prior 427
Possible Injury385possible injury crashes16.3%
↓-40.1%prior 643
No Injury1,652no injury crashes69.8%
↓-42.1%prior 2,855

Source: Iowa Crash Data · ArcGIS Open Data · 2020-04-01 to 2020-04-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-04-01 to 2020-04-30 · Most severe injury per crash record

Top Contributing Factors

The ranking of top contributing factors changed notably year-over-year. 'Followed too close' incidents dropped by 70.3% in count from 511 to 152, falling from the primary cause to the third-ranked. 'Animal'-related collisions became the top-ranked factor with 315 incidents, even as their total count decreased from 470. 'Lost Control' crashes, with a stable count of 197 versus 199 previously, rose in rank from seventh to second.

Officer-Reported Primary Contributing Cause

Animal315 (13.3%)↓-33.0%prior 470
Lost Control197 (8.3%)↓-1.0%prior 199
Followed too close152 (6.4%)↓-70.3%prior 511
Ran off road - left151 (6.4%)↓-31.1%prior 219
Other (explain in narrative): Other148 (6.3%)↓-40.6%prior 249
Ran off road - straight117 (4.9%)→0.0%prior 117
FTYROW: From stop sign115 (4.9%)↓-48.0%prior 221
Driving too fast for conditions114 (4.8%)↓-6.6%prior 122
Operating vehicle in an reckless, erratic, careless, negligent manner95 (4%)↓-21.5%prior 121
Ran Traffic Signal93 (3.9%)↓-35.9%prior 145

Source: Iowa Crash Data · ArcGIS Open Data · 2020-04-01 to 2020-04-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes by environmental conditions showed some shifts. The proportion of crashes occurring in rainy conditions decreased from 8.1% to 3.1%, and those on wet roads fell from 13.4% to 7.7%. Conversely, the share of crashes happening in dark conditions (both lighted and unlighted roadways) increased from 16.2% in April 2019 to 21.4% in April 2020.

Weather

Clear1,395 (66.6%)
↓-37.3%prior 2,226
Cloudy429 (20.5%)
↓-53.7%prior 927
Freezing rain/drizzle77 (3.7%)
↑22.2%prior 63
Rain74 (3.5%)
↓-77.2%prior 325
Snow54 (2.6%)
↑200.0%prior 18
Fog, smoke, smog30 (1.4%)
↓-21.1%prior 38
Severe Winds13 (0.6%)
↓-45.8%prior 24
Sleet, hail11 (0.5%)
↓-8.3%prior 12
Blowing Snow9 (0.4%)
Other (explain in narrative)2 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-04-01 to 2020-04-30 · Weather condition at time of crash

Lighting

Daylight1,497 (71.0%)
↓-47.1%prior 2,832
Dark - roadway lighted258 (12.2%)
↓-29.1%prior 364
Dark - roadway not lighted249 (11.8%)
↓-14.1%prior 290
Dawn51 (2.4%)
↓-28.2%prior 71
Dusk42 (2.0%)
↓-44.7%prior 76
Dark - unknown roadway lighting11 (0.5%)
↓-15.4%prior 13

Source: Iowa Crash Data · ArcGIS Open Data · 2020-04-01 to 2020-04-30 · Lighting condition field

Road Surface

Dry1,679 (79.8%)
↓-43.1%prior 2,950
Wet183 (8.7%)
↓-66.2%prior 541
Ice/frost113 (5.4%)
↑135.4%prior 48
Gravel68 (3.2%)
↑33.3%prior 51
Snow31 (1.5%)
↑287.5%prior 8
Slush23 (1.1%)
↓-17.9%prior 28
Mud, dirt2 (0.1%)
↓-77.8%prior 9
Sand2 (0.1%)
↓-60.0%prior 5
Oil1 (0.0%)
Other (explain in narrative)1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-04-01 to 2020-04-30 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes, Ford and Chevrolet, remained consistent in ranking between both periods, though their total counts decreased significantly along with overall crashes. The age demographics of persons involved showed slight changes, with the 26-34 age group's representation increasing from 15.5% to 16.7% of all individuals. The share of most other age groups, like the 16-20 cohort, remained stable.

Top Vehicle Makes (3,758 vehicles)

1
FORD585 (15.6%)
↓-47.9%prior 1,122
2
CHEV486 (12.9%)
↓-45.2%prior 887
3
CHEVROLET275 (7.3%)
↓-46.5%prior 514
4
DODG162 (4.3%)
↓-37.0%prior 257
5
JEEP142 (3.8%)
↓-43.4%prior 251
6
TOYT139 (3.7%)
↓-59.4%prior 342
7
NR119 (3.2%)
↓-40.5%prior 200
8
GMC116 (3.1%)
↓-49.3%prior 229
9
HOND104 (2.8%)
↓-55.4%prior 233
10
DODGE93 (2.5%)
↓-47.2%prior 176

Source: Iowa Crash Data · ArcGIS Open Data · 2020-04-01 to 2020-04-30 · Vehicle unit records

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

Sex Distribution (3,294 persons with recorded sex)

Male2,090 (63.4%)
↓-41.1%prior 3,550
Female1,204 (36.6%)
↓-57.5%prior 2,833

Source: Iowa Crash Data · ArcGIS Open Data · 2020-04-01 to 2020-04-30 · 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: 2020-04-01 through 2020-04-30
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-04-01 through 2020-04-30 (30 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 2,368
  • Total persons involved: 5,220
  • Total vehicles involved: 3,758

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: April 2020." Published September 9, 2026. Reporting period: 2020-04-01 to 2020-04-30. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/april-2020-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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