Monthly Traffic Safety Analysis

4,149 CRASHES IN
IOWA, IA
AUGUST 2016

All metrics benchmarked againstAugust 2015

In August 2016, there were 4,149 total traffic crashes, a 2.2% increase from the 4,059 crashes recorded in August 2015. Despite the rise in total incidents, the period saw a significant year-over-year improvement in outcomes. Most notably, total fatalities decreased by 25.5%, falling from 51 in the prior year to 38 in the current period.

4,149

▲ 2.2%was 4,059

Total Crash Events

38

▼ -25.5%was 51

Persons Killed

1,609

▼ -8.8%was 1,765

Persons Injured

35

▼ -14.6%was 41

Fatal Crash Events

Note: "Persons Killed" (38) counts individual fatalities across all crash events. "Fatal" in the severity table below (35) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-08-01 to 2016-08-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crashes showed a slight upward trend, increasing by 90 incidents from 4,059 in August 2015 to 4,149 in August 2016. However, this increase in crash volume was accompanied by a notable decrease in severity. Total injuries fell by 8.8% from 1,765 to 1,609, and fatalities dropped from 51 to 38 year-over-year.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 3↓-66.7%

0

Cyclists Killed

Prior: 2↓-100.0%

37

Motorists Killed

Prior: 45↓-17.8%

0

Other Killed

Prior: 1↓-100.0%

37

Pedestrians Injured

Prior: 21↑76.2%

40

Cyclists Injured

Prior: 45↓-11.1%

1,527

Motorists Injured

Prior: 1,694↓-9.9%

5

Other Injured

Prior: 5→0.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-08-01 to 2016-08-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Monday (667 crashes) in the prior year to Wednesday (735 crashes) in the current period. The peak hour for collisions remained consistent at 4 PM in both August 2016 (380 crashes) and August 2015 (367 crashes). The afternoon commute hours continue to be the time with the highest volume of crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-08-01 to 2016-08-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-08-01 to 2016-08-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There was a notable shift towards less severe crashes compared to the previous year. Fatal crashes decreased from 41 to 35, and their share of all crashes fell from 1.0% to 0.8%. The proportion of serious injury crashes also declined from 3.2% to 2.6% of the total. Conversely, crashes resulting in no injury increased their share from 65.8% to 67.5% of all incidents.

Severity is per crash event (most severe injury). 35 fatal crash events resulted in 38 persons killed.

Outcome by Severity (Crash Events)

Fatal35fatal crashes0.8%
↓-14.6%prior 41
Serious Injury108serious injury crashes2.6%
↓-16.9%prior 130
Minor Injury436minor injury crashes10.5%
↓-10.1%prior 485
Possible Injury770possible injury crashes18.6%
↑5.2%prior 732
No Injury2,800no injury crashes67.5%
↑4.8%prior 2,671

Source: Iowa Crash Data · ArcGIS Open Data · 2016-08-01 to 2016-08-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-08-01 to 2016-08-31 · Most severe injury per crash record

Top Contributing Factors

Following too closely remained the leading contributing factor in both periods, with the count of such incidents increasing from 521 to 554. The second-ranked factor in the prior year, Animal-related crashes, saw its count decrease from 299 to 271. Notably, crashes attributed to running a traffic signal increased from 127 to 166, and those involving a failure to yield while making a left turn rose from 201 to 227.

Officer-Reported Primary Contributing Cause

Followed too close554 (13.4%)↑6.3%prior 521
Animal271 (6.5%)↓-9.4%prior 299
Other (explain in narrative): Other266 (6.4%)↑3.9%prior 256
Ran off road - left236 (5.7%)↑7.8%prior 219
Lost Control233 (5.6%)↓-10.0%prior 259
FTYROW: Making left turn227 (5.5%)↑12.9%prior 201
FTYROW: From stop sign224 (5.4%)↓-8.2%prior 244
Ran Traffic Signal166 (4%)↑30.7%prior 127
Ran off road - straight134 (3.2%)↓-3.6%prior 139
Operating vehicle in an reckless, erratic, careless, negligent manner126 (3%)↓-2.3%prior 129

Source: Iowa Crash Data · ArcGIS Open Data · 2016-08-01 to 2016-08-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions remained broadly similar between the two periods, with the majority of incidents occurring in favorable environments. In August 2016, 73.2% of crashes happened in daylight and 81.2% on dry roads, nearly identical to the 72.1% and 81.3% shares, respectively, in August 2015. Crashes during clear weather accounted for 63.6% of the total in the current period, a slight decrease from 67.8% in the prior year, with a corresponding proportional increase in crashes under cloudy conditions.

Weather

Clear2,640 (67.7%)
↓-4.1%prior 2,752
Cloudy957 (24.5%)
↑32.2%prior 724
Rain270 (6.9%)
↓-10.9%prior 303
Fog, smoke, smog25 (0.6%)
↑4.2%prior 24
Severe Winds4 (0.1%)
Freezing rain/drizzle1 (0.0%)
Sleet, hail1 (0.0%)
Blowing sand, soil, dirt1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-08-01 to 2016-08-31 · Weather condition at time of crash

Lighting

Daylight3,037 (77.4%)
↑3.7%prior 2,928
Dark - roadway lighted428 (10.9%)
↑3.9%prior 412
Dark - roadway not lighted317 (8.1%)
↓-0.9%prior 320
Dusk70 (1.8%)
↓-9.1%prior 77
Dawn59 (1.5%)
↓-7.8%prior 64
Dark - unknown roadway lighting11 (0.3%)
↓-38.9%prior 18

Source: Iowa Crash Data · ArcGIS Open Data · 2016-08-01 to 2016-08-31 · Lighting condition field

Road Surface

Dry3,368 (86.3%)
↑2.0%prior 3,301
Wet415 (10.6%)
↑8.4%prior 383
Gravel106 (2.7%)
↓-5.4%prior 112
Water (standing or moving)7 (0.2%)
Mud, dirt4 (0.1%)
↓-50.0%prior 8
Other (explain in narrative)3 (0.1%)
↓-62.5%prior 8
Sand1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-08-01 to 2016-08-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in collisions, including Ford and Chevrolet, remained stable in ranking and volume year-over-year. The distribution of persons involved by age group also showed broad consistency. The 26-34 age group represented the largest share of persons in both August 2016 (15.6%) and August 2015 (15.4%).

Top Vehicle Makes (7,511 vehicles)

1
FORD1,199 (16%)
↑1.5%prior 1,181
2
CHEVROLET784 (10.4%)
↑28.7%prior 609
3
CHEV680 (9.1%)
↓-18.9%prior 838
4
TOYT310 (4.1%)
↑14.0%prior 272
5
DODGE287 (3.8%)
↑25.9%prior 228
6
TOYOTA255 (3.4%)
↑30.8%prior 195
7
DODG233 (3.1%)
↓-19.1%prior 288
8
JEEP231 (3.1%)
↑6.5%prior 217
9
GMC218 (2.9%)
↑19.8%prior 182
10
HONDA191 (2.5%)
↑21.7%prior 157

Source: Iowa Crash Data · ArcGIS Open Data · 2016-08-01 to 2016-08-31 · Vehicle unit records

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

Sex Distribution (5,390 persons with recorded sex)

Male3,074 (57.0%)
↓-15.0%prior 3,615
Female2,316 (43.0%)
↓-15.0%prior 2,724

Source: Iowa Crash Data · ArcGIS Open Data · 2016-08-01 to 2016-08-31 · 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: 2016-08-01 through 2016-08-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-08-01 through 2016-08-31 (31 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 4,149
  • Total persons involved: 8,327
  • Total vehicles involved: 7,511

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: August 2016." Published September 9, 2026. Reporting period: 2016-08-01 to 2016-08-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/august-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

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