Monthly Traffic Safety Analysis

2,992 CRASHES IN
IOWA, IA
MAY 2020

All metrics benchmarked againstMay 2019

In May 2020, Iowa recorded 2,992 total crashes, a 33.8% decrease from the 4,518 crashes reported in May 2019. This downward trend was also reflected in fatalities, which fell from 30 to 21, and total injuries, which dropped from 1,580 to 1,018. Despite the overall reduction in collisions, the rate of crashes involving a driver under the influence (DUI) increased from 3.4% to 4.1% of all incidents.

2,992

▼ -33.8%was 4,518

Total Crash Events

21

▼ -30.0%was 30

Persons Killed

1,018

▼ -35.6%was 1,580

Persons Injured

20

▼ -25.9%was 27

Fatal Crash Events

Note: "Persons Killed" (21) counts individual fatalities across all crash events. "Fatal" in the severity table below (20) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2020-05-01 to 2020-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Iowa saw a significant year-over-year decline in May 2020. Total crashes fell by 33.8%, from 4,518 in May 2019 to 2,992 in May 2020. Similarly, the number of people injured decreased by 35.6% and the number of fatalities dropped by 30.0% over the same period.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 3↓-66.7%

1

Cyclists Killed

Prior: 2↓-50.0%

19

Motorists Killed

Prior: 24↓-20.8%

13

Pedestrians Injured

Prior: 33↓-60.6%

17

Cyclists Injured

Prior: 30↓-43.3%

988

Motorists Injured

Prior: 1,514↓-34.7%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-05-01 to 2020-05-31 · 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 May 2019 and May 2020. While Friday remained the peak day for crashes in both periods, the peak hour for collisions moved two hours earlier, from 4 p.m. in 2019 (392 crashes) to 2 p.m. in 2020 (233 crashes). The afternoon rush hour concentration of crashes was less pronounced in May 2020, with a more distributed pattern throughout the afternoon compared to the prior year.

Source: Iowa Crash Data · ArcGIS Open Data · 2020-05-01 to 2020-05-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-05-01 to 2020-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the total number of fatal crashes decreased from 27 to 20 year-over-year, the fatal crash rate as a percentage of all crashes slightly increased from 0.6% in May 2019 to 0.7% in May 2020. The proportion of crashes resulting in serious injuries also saw an increase, rising from 2.1% of all incidents in the prior year to 3.1% in the current period. The share of crashes involving minor or possible injuries remained stable.

Severity is per crash event (most severe injury). 20 fatal crash events resulted in 21 persons killed.

Outcome by Severity (Crash Events)

Fatal20fatal crashes0.7%
↓-25.9%prior 27
Serious Injury93serious injury crashes3.1%
↓-1.1%prior 94
Minor Injury304minor injury crashes10.2%
↓-36.0%prior 475
Possible Injury475possible injury crashes15.9%
↓-34.4%prior 724
No Injury2,100no injury crashes70.2%
↓-34.3%prior 3,198

Source: Iowa Crash Data · ArcGIS Open Data · 2020-05-01 to 2020-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-05-01 to 2020-05-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the leading contributing factor in May 2020, accounting for 534 incidents, a 12.6% decrease in count from 611 in May 2019. However, as a share of all factors, animal-related crashes increased from 13.5% to 17.8%. Crashes attributed to 'Followed too close' saw a substantial drop of 56.1% in count, from 579 to 254. 'Lost Control' also saw a decrease in raw count from 199 to 169, but it represented a slightly larger share of total contributing factors, growing from 4.4% to 5.6%.

Officer-Reported Primary Contributing Cause

Animal534 (17.8%)↓-12.6%prior 611
Followed too close254 (8.5%)↓-56.1%prior 579
Ran off road - left178 (5.9%)↓-25.5%prior 239
Lost Control169 (5.6%)↓-15.1%prior 199
Other (explain in narrative): Other149 (5%)↓-51.6%prior 308
FTYROW: From stop sign136 (4.5%)↓-43.8%prior 242
FTYROW: Making left turn116 (3.9%)↓-42.6%prior 202
Ran Traffic Signal104 (3.5%)↓-24.1%prior 137
Ran Stop Sign103 (3.4%)↓-16.9%prior 124
Ran off road - straight100 (3.3%)↓-25.4%prior 134

Source: Iowa Crash Data · ArcGIS Open Data · 2020-05-01 to 2020-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The majority of crashes in both periods occurred in clear weather and on dry roads, with proportions remaining relatively stable year-over-year. There was a notable shift in lighting conditions, as crashes during daylight hours accounted for 65.8% of incidents in May 2020, down from 71.7% in May 2019. Correspondingly, the share of crashes occurring after dark increased, with crashes on both lighted and unlighted dark roadways rising from a combined 14.7% to 16.6% of the total.

Weather

Clear1,505 (58.8%)
↓-35.6%prior 2,337
Cloudy780 (30.5%)
↓-36.4%prior 1,227
Rain253 (9.9%)
↓-41.4%prior 432
Freezing rain/drizzle13 (0.5%)
↑85.7%prior 7
Fog, smoke, smog5 (0.2%)
↓-44.4%prior 9
Severe Winds3 (0.1%)
↓-57.1%prior 7
Other (explain in narrative)2 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-05-01 to 2020-05-31 · Weather condition at time of crash

Lighting

Daylight1,969 (76.4%)
↓-39.2%prior 3,239
Dark - roadway not lighted249 (9.7%)
↓-20.7%prior 314
Dark - roadway lighted247 (9.6%)
↓-30.0%prior 353
Dusk72 (2.8%)
→0.0%prior 72
Dawn34 (1.3%)
↓-47.7%prior 65
Dark - unknown roadway lighting6 (0.2%)
↓-14.3%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2020-05-01 to 2020-05-31 · Lighting condition field

Road Surface

Dry2,070 (80.6%)
↓-34.9%prior 3,179
Wet430 (16.7%)
↓-43.2%prior 757
Gravel56 (2.2%)
↓-9.7%prior 62
Other (explain in narrative)4 (0.2%)
Water (standing or moving)3 (0.1%)
Sand2 (0.1%)
Mud, dirt2 (0.1%)
↓-88.2%prior 17
Snow1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-05-01 to 2020-05-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Ford and Chevrolet models topping the list in both May 2019 and May 2020, albeit with lower total counts in the latter period. An analysis of persons involved in crashes shows a shift in age demographics. The proportion of individuals aged 65 and older decreased from 11.1% of all persons involved in May 2019 to 9.4% in May 2020. Conversely, the 16-20 age group saw a slight increase in their proportional involvement, from 12.0% to 12.8%.

Top Vehicle Makes (4,917 vehicles)

1
FORD815 (16.6%)
↓-33.8%prior 1,231
2
CHEV618 (12.6%)
↓-38.2%prior 1,000
3
CHEVROLET381 (7.7%)
↓-30.6%prior 549
4
JEEP191 (3.9%)
↓-22.7%prior 247
5
DODG189 (3.8%)
↓-39.0%prior 310
6
TOYT181 (3.7%)
↓-51.6%prior 374
7
GMC169 (3.4%)
↓-31.6%prior 247
8
HOND143 (2.9%)
↓-49.5%prior 283
9
NISS132 (2.7%)
↓-34.3%prior 201
10
NR130 (2.6%)
↓-48.2%prior 251

Source: Iowa Crash Data · ArcGIS Open Data · 2020-05-01 to 2020-05-31 · Vehicle unit records

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

Sex Distribution (4,374 persons with recorded sex)

Male2,676 (61.2%)
↓-32.0%prior 3,933
Female1,698 (38.8%)
↓-45.8%prior 3,132

Source: Iowa Crash Data · ArcGIS Open Data · 2020-05-01 to 2020-05-31 · 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: 2020-05-01 through 2020-05-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-05-01 through 2020-05-31 (31 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 2,992
  • Total persons involved: 6,864
  • Total vehicles involved: 4,917

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: May 2020." Published September 9, 2026. Reporting period: 2020-05-01 to 2020-05-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/may-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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