Monthly Traffic Safety Analysis

4,518 CRASHES IN
IOWA, IA
MAY 2019

All metrics benchmarked againstMay 2018

In May 2019, there were 4,518 total crashes, a 1.1% decrease from the 4,569 crashes recorded in May 2018. While overall crashes and injuries declined, the most notable year-over-year shift was a significant 30.4% increase in traffic fatalities, which rose from 23 to 30.

4,518

▼ -1.1%was 4,569

Total Crash Events

30

▲ 30.4%was 23

Persons Killed

1,580

▼ -4.8%was 1,659

Persons Injured

27

▲ 22.7%was 22

Fatal Crash Events

Note: "Persons Killed" (30) counts individual fatalities across all crash events. "Fatal" in the severity table below (27) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-05-01 to 2019-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends showed a slight decrease in volume year-over-year. Total crashes fell by 1.1% from 4,569 to 4,518, and total injuries decreased by 4.8% from 1,659 to 1,580. However, this was contrasted by a sharp 30.4% rise in fatalities, from 23 in May 2018 to 30 in May 2019.

Vulnerable Road User Casualties

3

Pedestrians Killed

Prior: 1↑200.0%

2

Cyclists Killed

Prior: 1↑100.0%

24

Motorists Killed

Prior: 21↑14.3%

1

Other Killed

Prior: 0→%

33

Pedestrians Injured

Prior: 23↑43.5%

30

Cyclists Injured

Prior: 40↓-25.0%

1,514

Motorists Injured

Prior: 1,591↓-4.8%

3

Other Injured

Prior: 5↓-40.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-05-01 to 2019-05-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted slightly between the two periods. In May 2019, the peak day for crashes was Friday with 908 incidents, a change from Thursday (785 crashes) in the prior year. The peak hour also shifted later, from 3 p.m. in 2018 (421 crashes) to 4 p.m. in 2019 (392 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2019-05-01 to 2019-05-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-05-01 to 2019-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes worsened year-over-year, with the fatal crash count increasing from 22 to 27 and the fatal crash rate rising from 0.48% to 0.60%. Conversely, crashes resulting in injuries saw a decline across all categories. Serious injury crashes fell from 114 to 94, and minor injury crashes decreased from 485 to 475.

Severity is per crash event (most severe injury). 27 fatal crash events resulted in 30 persons killed.

Outcome by Severity (Crash Events)

Fatal27fatal crashes0.6%
↑22.7%prior 22
Serious Injury94serious injury crashes2.1%
↓-17.5%prior 114
Minor Injury475minor injury crashes10.5%
↓-2.1%prior 485
Possible Injury724possible injury crashes16%
↓-1.4%prior 734
No Injury3,198no injury crashes70.8%
↓-0.5%prior 3,214

Source: Iowa Crash Data · ArcGIS Open Data · 2019-05-01 to 2019-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-05-01 to 2019-05-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors remained consistent, with "Animal" (611 crashes) and "Followed too close" (579 crashes) being the top two causes in May 2019. Compared to May 2018, the count of crashes involving an animal decreased from 676 to 611. Incidents of following too closely saw a slight increase in count from 567 to 579, while crashes from failure to yield from a stop sign decreased from 273 to 242.

Officer-Reported Primary Contributing Cause

Animal611 (13.5%)↓-9.6%prior 676
Followed too close579 (12.8%)↑2.1%prior 567
Other (explain in narrative): Other308 (6.8%)↑2.7%prior 300
FTYROW: From stop sign242 (5.4%)↓-11.4%prior 273
Ran off road - left239 (5.3%)↓-1.2%prior 242
FTYROW: Making left turn202 (4.5%)↑8.0%prior 187
Lost Control199 (4.4%)↓-14.2%prior 232
Driver Distraction: Other interior distraction139 (3.1%)↓-4.1%prior 145
Ran Traffic Signal137 (3%)↓-12.2%prior 156
Made improper turn134 (3%)↑21.8%prior 110

Source: Iowa Crash Data · ArcGIS Open Data · 2019-05-01 to 2019-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in May 2019 occurred more frequently in adverse weather compared to the previous year. The number of crashes in rain nearly doubled, rising from 218 to 432, and incidents on wet road surfaces increased from 400 to 757. Consequently, crashes in clear weather and on dry roads saw corresponding decreases. Lighting conditions remained stable, with the vast majority of crashes in both periods occurring during daylight hours.

Weather

Clear2,337 (58.1%)
↓-19.1%prior 2,889
Cloudy1,227 (30.5%)
↑41.4%prior 868
Rain432 (10.7%)
↑98.2%prior 218
Fog, smoke, smog9 (0.2%)
↓-65.4%prior 26
Severe Winds7 (0.2%)
↑16.7%prior 6
Freezing rain/drizzle7 (0.2%)
↓-30.0%prior 10
Other (explain in narrative)2 (0.0%)
Blowing sand, soil, dirt1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-05-01 to 2019-05-31 · Weather condition at time of crash

Lighting

Daylight3,239 (80.0%)
↓-0.5%prior 3,256
Dark - roadway lighted353 (8.7%)
↑9.6%prior 322
Dark - roadway not lighted314 (7.8%)
↑6.8%prior 294
Dusk72 (1.8%)
↓-18.2%prior 88
Dawn65 (1.6%)
↑12.1%prior 58
Dark - unknown roadway lighting7 (0.2%)
↓-61.1%prior 18

Source: Iowa Crash Data · ArcGIS Open Data · 2019-05-01 to 2019-05-31 · Lighting condition field

Road Surface

Dry3,179 (79.1%)
↓-9.4%prior 3,507
Wet757 (18.8%)
↑89.3%prior 400
Gravel62 (1.5%)
↓-42.6%prior 108
Mud, dirt17 (0.4%)
↑240.0%prior 5
Other (explain in narrative)4 (0.1%)
Sand2 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-05-01 to 2019-05-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes were consistent year-over-year, with Chevrolet and Ford vehicles leading in both periods. Analysis of persons involved shows a demographic shift, with the 16-20 age group's involvement decreasing from 1,405 individuals to 1,284. In contrast, the 26-34 age group saw an increase in persons involved, from 1,597 in May 2018 to 1,665 in May 2019.

Top Vehicle Makes (7,899 vehicles)

1
FORD1,231 (15.6%)
↓-2.3%prior 1,260
2
CHEV1,000 (12.7%)
↓-9.7%prior 1,108
3
CHEVROLET549 (7%)
↑13.9%prior 482
4
TOYT374 (4.7%)
↓-3.9%prior 389
5
DODG310 (3.9%)
↓-12.4%prior 354
6
HOND283 (3.6%)
↑5.2%prior 269
7
NR251 (3.2%)
↑29.4%prior 194
8
GMC247 (3.1%)
↑13.3%prior 218
9
JEEP247 (3.1%)
→0.0%prior 247
10
TOYOTA202 (2.6%)
↑27.8%prior 158

Source: Iowa Crash Data · ArcGIS Open Data · 2019-05-01 to 2019-05-31 · Vehicle unit records

1,431 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (7,065 persons with recorded sex)

Male3,933 (55.7%)
↓-0.4%prior 3,950
Female3,132 (44.3%)
↓-1.6%prior 3,183

Source: Iowa Crash Data · ArcGIS Open Data · 2019-05-01 to 2019-05-31 · 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: 2019-05-01 through 2019-05-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-05-01 through 2019-05-31 (31 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 4,518
  • Total persons involved: 10,656
  • Total vehicles involved: 7,899

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 2019." Published September 9, 2026. Reporting period: 2019-05-01 to 2019-05-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/may-2019-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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