Monthly Traffic Safety Analysis

3,938 CRASHES IN
IOWA, IA
APRIL 2017

All metrics benchmarked againstApril 2016

In April 2017, there were 3,938 total traffic crashes, a marginal 0.3% increase from the 3,928 crashes recorded in April 2016. While overall crash volume remained stable, the most notable year-over-year shift was a 37.1% increase in crashes involving a driver under the influence of alcohol, which rose from 124 to 170 incidents. Despite this, total fatalities decreased by 21.2% from 33 to 26.

3,938

▲ 0.3%was 3,928

Total Crash Events

26

▼ -21.2%was 33

Persons Killed

1,502

▼ -2.8%was 1,546

Persons Injured

21

▼ -32.3%was 31

Fatal Crash Events

Note: "Persons Killed" (26) counts individual fatalities across all crash events. "Fatal" in the severity table below (21) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-04-01 to 2017-04-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends remained relatively stable year-over-year, with total collisions increasing by only 10 incidents from 3,928 in April 2016 to 3,938 in April 2017. However, the outcomes of these crashes showed improvement, as total fatalities decreased from 33 to 26 and total injuries fell from 1,546 to 1,502.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 3↓-33.3%

0

Cyclists Killed

Prior: 1↓-100.0%

24

Motorists Killed

Prior: 29↓-17.2%

0

Other Killed

Prior: 0→0.0%

32

Pedestrians Injured

Prior: 41↓-22.0%

14

Cyclists Injured

Prior: 21↓-33.3%

1,454

Motorists Injured

Prior: 1,481↓-1.8%

2

Other Injured

Prior: 3↓-33.3%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-04-01 to 2017-04-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The daily and hourly patterns of crashes were consistent between the two periods. Friday was the day with the most crashes in both April 2017 (668 crashes) and April 2016 (769 crashes). The afternoon commute hour of 3 p.m. also remained the peak time for collisions in both years, accounting for 364 crashes in the current period and 370 in the prior period.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-04-01 to 2017-04-30 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-04-01 to 2017-04-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes lessened in April 2017 compared to the prior year. The number of fatal crashes decreased from 31 to 21, causing the fatal crash rate to fall from 0.79% to 0.53%. The proportion of crashes resulting in serious or minor injuries also saw a slight decline, while the share of crashes involving only possible injury or no injury increased from 86.1% to 87.5%.

Severity is per crash event (most severe injury). 21 fatal crash events resulted in 26 persons killed.

Outcome by Severity (Crash Events)

Fatal21fatal crashes0.5%
↓-32.3%prior 31
Serious Injury96serious injury crashes2.4%
↓-7.7%prior 104
Minor Injury377minor injury crashes9.6%
↓-8.3%prior 411
Possible Injury728possible injury crashes18.5%
↑2.5%prior 710
No Injury2,716no injury crashes69%
↑1.6%prior 2,672

Source: Iowa Crash Data · ArcGIS Open Data · 2017-04-01 to 2017-04-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-04-01 to 2017-04-30 · Most severe injury per crash record

Top Contributing Factors

The primary contributing factors to crashes remained unchanged year-over-year. "Followed too close" was the leading cause in both April 2017 (485 crashes) and April 2016 (477 crashes), representing a 1.7% increase in count. The second-ranked factor, collisions involving an animal, saw a more significant 9.6% increase in count, rising from 344 to 377 incidents. The top three factors retained their respective ranks across both periods.

Officer-Reported Primary Contributing Cause

Followed too close485 (12.3%)↑1.7%prior 477
Animal377 (9.6%)↑9.6%prior 344
Other (explain in narrative): Other260 (6.6%)↑13.0%prior 230
FTYROW: From stop sign245 (6.2%)↑7.0%prior 229
Ran off road - left228 (5.8%)↑2.2%prior 223
Lost Control218 (5.5%)↑0.5%prior 217
FTYROW: Making left turn196 (5%)↓-1.5%prior 199
Ran off road - straight148 (3.8%)↑0.7%prior 147
Ran Traffic Signal148 (3.8%)↓-7.5%prior 160
Operating vehicle in an reckless, erratic, careless, negligent manner120 (3%)↑9.1%prior 110

Source: Iowa Crash Data · ArcGIS Open Data · 2017-04-01 to 2017-04-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

A higher proportion of crashes occurred in adverse conditions in April 2017 compared to the previous year. The share of crashes on wet road surfaces increased from 16.4% to 21.1% of all incidents. Correspondingly, collisions during rainy weather rose from 9.6% to 13.2% of the total. While daylight remained the predominant lighting condition, its share of crashes fell from 73.0% to 69.1% year-over-year.

Weather

Clear1,909 (52.8%)
↓-10.1%prior 2,123
Cloudy1,151 (31.9%)
↑4.8%prior 1,098
Rain520 (14.4%)
↑38.3%prior 376
Freezing rain/drizzle12 (0.3%)
↓-7.7%prior 13
Fog, smoke, smog7 (0.2%)
↑16.7%prior 6
Severe Winds6 (0.2%)
↓-68.4%prior 19
Sleet, hail5 (0.1%)
Other (explain in narrative)3 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-04-01 to 2017-04-30 · Weather condition at time of crash

Lighting

Daylight2,720 (74.8%)
↓-5.1%prior 2,867
Dark - roadway lighted420 (11.6%)
↑17.6%prior 357
Dark - roadway not lighted328 (9.0%)
↑23.8%prior 265
Dusk84 (2.3%)
↓-15.2%prior 99
Dawn68 (1.9%)
↑15.3%prior 59
Dark - unknown roadway lighting15 (0.4%)
↑87.5%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2017-04-01 to 2017-04-30 · Lighting condition field

Road Surface

Dry2,707 (74.8%)
↓-7.3%prior 2,919
Wet831 (22.9%)
↑29.0%prior 644
Gravel73 (2.0%)
↑5.8%prior 69
Mud, dirt5 (0.1%)
Water (standing or moving)2 (0.1%)
Ice/frost1 (0.0%)
↓-83.3%prior 6
Sand1 (0.0%)
Other (explain in narrative)1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-04-01 to 2017-04-30 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes showed high consistency, with Ford and Chevrolet models representing the top makes in both April 2017 and April 2016. Demographically, there was a notable increase in the number of persons aged 16-20 involved in crashes, with their count rising from 1,115 to 1,287. The total number of individuals involved in crashes also grew from 8,406 in the prior period to 9,222 in the current period.

Top Vehicle Makes (7,042 vehicles)

1
FORD1,118 (15.9%)
↑1.9%prior 1,097
2
CHEV867 (12.3%)
↑19.6%prior 725
3
CHEVROLET536 (7.6%)
↓-19.9%prior 669
4
TOYT362 (5.1%)
↑20.7%prior 300
5
DODG292 (4.1%)
↑11.9%prior 261
6
HOND221 (3.1%)
↑32.3%prior 167
7
JEEP221 (3.1%)
↑0.5%prior 220
8
GMC215 (3.1%)
↑9.1%prior 197
9
NR181 (2.6%)
↑13.1%prior 160
10
TOYOTA180 (2.6%)
↓-17.4%prior 218

Source: Iowa Crash Data · ArcGIS Open Data · 2017-04-01 to 2017-04-30 · Vehicle unit records

1,263 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (6,139 persons with recorded sex)

Male3,347 (54.5%)
↑6.6%prior 3,139
Female2,792 (45.5%)
↑14.1%prior 2,448

Source: Iowa Crash Data · ArcGIS Open Data · 2017-04-01 to 2017-04-30 · 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: 2017-04-01 through 2017-04-30
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-04-01 through 2017-04-30 (30 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 3,938
  • Total persons involved: 9,222
  • Total vehicles involved: 7,042

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 2017." Published September 9, 2026. Reporting period: 2017-04-01 to 2017-04-30. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/april-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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