Monthly Traffic Safety Analysis

4,828 CRASHES IN
IOWA, IA
MAY 2017

All metrics benchmarked againstMay 2016

In May 2017, Iowa recorded 4,828 total vehicle crashes, representing a 5.9% increase from the 4,557 crashes in May 2016. While the overall number of crashes rose, the most notable year-over-year shift was a significant decrease in fatalities, which fell by 41% from 39 to 23.

4,828

▲ 5.9%was 4,557

Total Crash Events

23

▼ -41.0%was 39

Persons Killed

1,716

▼ -4.6%was 1,799

Persons Injured

20

▼ -42.9%was 35

Fatal Crash Events

Note: "Persons Killed" (23) 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 · 2017-05-01 to 2017-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash volumes in May 2017 increased compared to the same month in the prior year, rising by 271 incidents from 4,557 to 4,828. Despite this increase in total crashes, key severity metrics improved, with total fatalities dropping from 39 to 23 and total injuries seeing a slight decrease from 1,799 to 1,716.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 3↓-66.7%

0

Cyclists Killed

Prior: 0→0.0%

22

Motorists Killed

Prior: 36↓-38.9%

0

Other Killed

Prior: 0→0.0%

33

Pedestrians Injured

Prior: 33→0.0%

32

Cyclists Injured

Prior: 51↓-37.3%

1,644

Motorists Injured

Prior: 1,707↓-3.7%

7

Other Injured

Prior: 8↓-12.5%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-05-01 to 2017-05-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak hour for crashes was consistent year-over-year, occurring at 3 p.m. in both May 2016 (383 crashes) and May 2017 (412 crashes). However, the peak day for crashes shifted, moving from Friday in the prior period (776 crashes) to Tuesday in the current period (838 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2017-05-01 to 2017-05-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-05-01 to 2017-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes decreased in May 2017 compared to the previous year. The fatal crash rate, which measures the number of fatal crashes per 100 total crashes, fell from 0.77 to 0.41. The proportion of crashes resulting in any type of injury also declined, from 31.8% in the prior period to 29.8% in the current period, while the share of no-injury crashes grew from 67.5% to 69.8%.

Severity is per crash event (most severe injury). 20 fatal crash events resulted in 23 persons killed.

Outcome by Severity (Crash Events)

Fatal20fatal crashes0.4%
↓-42.9%prior 35
Serious Injury126serious injury crashes2.6%
↓-3.1%prior 130
Minor Injury491minor injury crashes10.2%
↑6.3%prior 462
Possible Injury821possible injury crashes17%
↓-4.0%prior 855
No Injury3,370no injury crashes69.8%
↑9.6%prior 3,075

Source: Iowa Crash Data · ArcGIS Open Data · 2017-05-01 to 2017-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-05-01 to 2017-05-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors remained consistent between both periods, with 'Animal' and 'Followed too close' ranking as the top two causes. The count of crashes involving an animal increased from 595 to 622, and incidents of following too closely rose from 535 to 617. The number of crashes attributed to 'Lost Control' was unchanged at 268 incidents in both May 2016 and May 2017.

Officer-Reported Primary Contributing Cause

Animal622 (12.9%)↑4.5%prior 595
Followed too close617 (12.8%)↑15.3%prior 535
Other (explain in narrative): Other301 (6.2%)↑24.4%prior 242
FTYROW: From stop sign276 (5.7%)↑2.2%prior 270
Lost Control268 (5.6%)→0.0%prior 268
Ran off road - left235 (4.9%)↑1.7%prior 231
FTYROW: Making left turn232 (4.8%)↑8.4%prior 214
Ran Stop Sign158 (3.3%)↑19.7%prior 132
Ran Traffic Signal154 (3.2%)↑1.3%prior 152
Ran off road - straight152 (3.1%)↓-9.5%prior 168

Source: Iowa Crash Data · ArcGIS Open Data · 2017-05-01 to 2017-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions remained largely stable year-over-year, with the majority of incidents in both periods occurring in clear weather and during daylight. In May 2017, 60.9% of crashes happened in clear weather and 72.4% in daylight, compared to 58.6% and 72.5% respectively in May 2016. The proportion of crashes on wet roads increased slightly from 12.0% to 13.4%, while crashes on dry roads made up 73.9% of the total, down from 74.9% a year prior.

Weather

Clear2,941 (68.0%)
↑10.1%prior 2,670
Cloudy975 (22.6%)
↓-6.9%prior 1,047
Rain370 (8.6%)
↑16.4%prior 318
Severe Winds17 (0.4%)
Fog, smoke, smog10 (0.2%)
↓-58.3%prior 24
Freezing rain/drizzle5 (0.1%)
Other (explain in narrative)2 (0.0%)
Blowing sand, soil, dirt1 (0.0%)
Snow1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-05-01 to 2017-05-31 · Weather condition at time of crash

Lighting

Daylight3,494 (80.4%)
↑5.8%prior 3,304
Dark - roadway lighted386 (8.9%)
↑9.7%prior 352
Dark - roadway not lighted297 (6.8%)
↑4.2%prior 285
Dusk83 (1.9%)
↑29.7%prior 64
Dawn67 (1.5%)
↑4.7%prior 64
Dark - unknown roadway lighting18 (0.4%)
↑38.5%prior 13

Source: Iowa Crash Data · ArcGIS Open Data · 2017-05-01 to 2017-05-31 · Lighting condition field

Road Surface

Dry3,566 (82.5%)
↑4.5%prior 3,413
Wet645 (14.9%)
↑18.1%prior 546
Gravel96 (2.2%)
↓-4.0%prior 100
Mud, dirt10 (0.2%)
↑25.0%prior 8
Sand4 (0.1%)
Other (explain in narrative)3 (0.1%)
Slush1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-05-01 to 2017-05-31 · Road surface condition field

Vehicles & Demographics

The ranking of the most common vehicle makes involved in crashes was consistent year-over-year, with Chevrolet and Ford leading in both periods. The number of persons involved in crashes increased across most age groups, with the 16-20 age group growing from 1,320 to 1,477 individuals. The 65+ age group also saw a notable increase in persons involved, rising from 876 in May 2016 to 1,090 in May 2017.

Top Vehicle Makes (8,472 vehicles)

1
FORD1,359 (16%)
↑11.0%prior 1,224
2
CHEV1,010 (11.9%)
↑36.7%prior 739
3
CHEVROLET637 (7.5%)
↓-25.5%prior 855
4
TOYT400 (4.7%)
↑36.5%prior 293
5
DODG329 (3.9%)
↑23.7%prior 266
6
HOND282 (3.3%)
↑31.8%prior 214
7
DODGE258 (3%)
↓-12.2%prior 294
8
JEEP253 (3%)
↑11.0%prior 228
9
TOYOTA234 (2.8%)
↓-14.0%prior 272
10
GMC227 (2.7%)
↑12.9%prior 201

Source: Iowa Crash Data · ArcGIS Open Data · 2017-05-01 to 2017-05-31 · Vehicle unit records

1,229 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (7,349 persons with recorded sex)

Male4,010 (54.6%)
↑16.8%prior 3,434
Female3,339 (45.4%)
↑20.2%prior 2,777

Source: Iowa Crash Data · ArcGIS Open Data · 2017-05-01 to 2017-05-31 · 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: 2017-05-01 through 2017-05-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-05-01 through 2017-05-31 (31 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 4,828
  • Total persons involved: 10,611
  • Total vehicles involved: 8,472

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 2017." Published September 9, 2026. Reporting period: 2017-05-01 to 2017-05-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/may-2017-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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