Monthly Traffic Safety Analysis

3,928 CRASHES IN
IOWA, IA
APRIL 2016

All metrics benchmarked againstApril 2015

In April 2016, Iowa recorded 3,928 total crashes, a 6.7% increase from the 3,680 crashes reported in April 2015. This rise was accompanied by an 11.3% increase in total injuries (from 1,389 to 1,546) and a 6.5% increase in fatalities (from 31 to 33). The most significant year-over-year change was a 24.0% increase in the number of fatal crash events, which grew from 25 to 31.

3,928

▲ 6.7%was 3,680

Total Crash Events

33

▲ 6.5%was 31

Persons Killed

1,546

▲ 11.3%was 1,389

Persons Injured

31

▲ 24.0%was 25

Fatal Crash Events

Note: "Persons Killed" (33) counts individual fatalities across all crash events. "Fatal" in the severity table below (31) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-04-01 to 2016-04-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for April 2016 indicates a rising trend in traffic incidents compared to the same month in the prior year. Total crashes increased by 6.7%, from 3,680 to 3,928. This increase in crash volume was accompanied by an 11.3% rise in injuries and a 6.5% increase in fatalities.

Vulnerable Road User Casualties

3

Pedestrians Killed

Prior: 1↑200.0%

1

Cyclists Killed

Prior: 0→%

29

Motorists Killed

Prior: 30↓-3.3%

0

Other Killed

Prior: 0→0.0%

41

Pedestrians Injured

Prior: 35↑17.1%

21

Cyclists Injured

Prior: 28↓-25.0%

1,481

Motorists Injured

Prior: 1,325↑11.8%

3

Other Injured

Prior: 1↑200.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-04-01 to 2016-04-30 · 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 Thursday (653 crashes) in April 2015 to Friday (769 crashes) in April 2016. The peak hour for incidents also shifted an hour earlier, moving from 4 PM in the prior year to 3 PM in the current period.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-04-01 to 2016-04-30 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-04-01 to 2016-04-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The overall severity of crashes increased in April 2016 compared to the previous year. The share of crashes resulting in a fatality rose from 0.7% to 0.8% of all incidents. Crashes involving serious injuries also increased as a proportion of the total, from 2.3% to 2.6%, while the share of no-injury crashes decreased from 68.8% to 68.0%.

Severity is per crash event (most severe injury). 31 fatal crash events resulted in 33 persons killed.

Outcome by Severity (Crash Events)

Fatal31fatal crashes0.8%
↑24.0%prior 25
Serious Injury104serious injury crashes2.6%
↑20.9%prior 86
Minor Injury411minor injury crashes10.5%
↑15.8%prior 355
Possible Injury710possible injury crashes18.1%
↑4.1%prior 682
No Injury2,672no injury crashes68%
↑5.5%prior 2,532

Source: Iowa Crash Data · ArcGIS Open Data · 2016-04-01 to 2016-04-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-04-01 to 2016-04-30 · Most severe injury per crash record

Top Contributing Factors

The primary contributing factors for crashes remained consistent year-over-year, with 'Followed too close' ranking first in both periods, increasing in count from 455 to 477 incidents. Collisions involving an 'Animal' were the second most common factor in both years, with a slight count decrease from 352 to 344. Notably, crashes where a vehicle 'Ran off road - left' increased in count by 16.8% from 191 to 223, moving this factor into the top five for the current period.

Officer-Reported Primary Contributing Cause

Followed too close477 (12.1%)↑4.8%prior 455
Animal344 (8.8%)↓-2.3%prior 352
Other (explain in narrative): Other230 (5.9%)↑8.5%prior 212
FTYROW: From stop sign229 (5.8%)↑7.0%prior 214
Ran off road - left223 (5.7%)↑16.8%prior 191
Lost Control217 (5.5%)↑0.9%prior 215
FTYROW: Making left turn199 (5.1%)↑15.7%prior 172
Ran Traffic Signal160 (4.1%)↑15.9%prior 138
Ran off road - straight147 (3.7%)↑16.7%prior 126
Ran Stop Sign118 (3%)↑11.3%prior 106

Source: Iowa Crash Data · ArcGIS Open Data · 2016-04-01 to 2016-04-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring under adverse conditions shifted year-over-year. Although most crashes in both periods happened in clear weather, the share of incidents during cloudy conditions grew from 19.0% in April 2015 to 28.0% in April 2016. Similarly, the proportion of crashes on wet road surfaces increased from 12.0% to 16.4% of all incidents.

Weather

Clear2,123 (58.3%)
↓-7.8%prior 2,303
Cloudy1,098 (30.1%)
↑57.3%prior 698
Rain376 (10.3%)
↑28.3%prior 293
Severe Winds19 (0.5%)
→0.0%prior 19
Freezing rain/drizzle13 (0.4%)
↓-35.0%prior 20
Fog, smoke, smog6 (0.2%)
↓-73.9%prior 23
Snow4 (0.1%)
↓-66.7%prior 12
Other (explain in narrative)3 (0.1%)
Sleet, hail1 (0.0%)
↓-80.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2016-04-01 to 2016-04-30 · Weather condition at time of crash

Lighting

Daylight2,867 (78.4%)
↑8.8%prior 2,634
Dark - roadway lighted357 (9.8%)
↓-1.7%prior 363
Dark - roadway not lighted265 (7.3%)
↓-5.0%prior 279
Dusk99 (2.7%)
↑59.7%prior 62
Dawn59 (1.6%)
↑15.7%prior 51
Dark - unknown roadway lighting8 (0.2%)
↓-38.5%prior 13

Source: Iowa Crash Data · ArcGIS Open Data · 2016-04-01 to 2016-04-30 · Lighting condition field

Road Surface

Dry2,919 (80.0%)
↑3.2%prior 2,829
Wet644 (17.7%)
↑45.4%prior 443
Gravel69 (1.9%)
↓-16.9%prior 83
Ice/frost6 (0.2%)
↓-40.0%prior 10
Mud, dirt4 (0.1%)
↓-50.0%prior 8
Other (explain in narrative)3 (0.1%)
Sand3 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-04-01 to 2016-04-30 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford and Chevrolet leading in both periods. The number of Fords in crashes rose from 1,003 to 1,097, while Chevrolets increased from a combined 1,282 to 1,394. An analysis of persons involved shows a slight shift in age demographics, with a decrease in individuals aged 16-20 (from 1,184 to 1,115) and an increase in the 26-34 age group (from 1,225 to 1,309).

Top Vehicle Makes (7,028 vehicles)

1
FORD1,097 (15.6%)
↑9.4%prior 1,003
2
CHEV725 (10.3%)
↓-6.2%prior 773
3
CHEVROLET669 (9.5%)
↑31.4%prior 509
4
TOYT300 (4.3%)
↑0.7%prior 298
5
DODG261 (3.7%)
↓-7.8%prior 283
6
DODGE236 (3.4%)
↑12.9%prior 209
7
JEEP220 (3.1%)
↑26.4%prior 174
8
TOYOTA218 (3.1%)
↑40.6%prior 155
9
GMC197 (2.8%)
↑13.2%prior 174
10
HOND167 (2.4%)
↓-14.4%prior 195

Source: Iowa Crash Data · ArcGIS Open Data · 2016-04-01 to 2016-04-30 · Vehicle unit records

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

Sex Distribution (5,587 persons with recorded sex)

Male3,139 (56.2%)
↑1.3%prior 3,100
Female2,448 (43.8%)
↓-4.9%prior 2,575

Source: Iowa Crash Data · ArcGIS Open Data · 2016-04-01 to 2016-04-30 · 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: 2016-04-01 through 2016-04-30
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-04-01 through 2016-04-30 (30 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 3,928
  • Total persons involved: 8,406
  • Total vehicles involved: 7,028

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 2016." Published September 9, 2026. Reporting period: 2016-04-01 to 2016-04-30. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/april-2016-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

ThatCarHitMe.com · An Injuria.ai Company