Yearly Traffic Safety Analysis

722 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Marshall County, total vehicle crashes increased by 4.0% from 694 in 2015 to 722 in 2016. While total fatalities remained unchanged at 4, the number of injuries rose from 214 to 226. The most significant year-over-year change was a 131% increase in crashes involving driving under the influence (DUI), which grew from 16 incidents in 2015 to 37 in 2016.

722

▲ 4.0%was 694

Total Crash Events

4

Persons Killed

226

▲ 5.6%was 214

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Marshall County showed an upward trend from 2015 to 2016. The total number of crashes increased by 4.0% from 694 to 722. This was accompanied by a 5.6% increase in total injuries, from 214 to 226, while the number of fatalities held steady at four for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 1↓-100.0%

4

Motorists Killed

Prior: 3↑33.3%

9

Pedestrians Injured

Prior: 5↑80.0%

5

Cyclists Injured

Prior: 3↑66.7%

212

Motorists Injured

Prior: 204↑3.9%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes saw a slight shift year-over-year. The peak day for crashes moved from Thursday (116 crashes) in 2015 to Friday (116 crashes) in 2016. Similarly, the peak hour for incidents shifted earlier in the afternoon, from 5 p.m. (59 crashes) in the prior year to 3 p.m. (61 crashes) in the current year, though the afternoon commute period remained the most common time for crashes in both periods.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity distributions remained relatively stable between the two periods. The number of fatal crashes was identical at four, with the fatal crash rate per 100 crashes decreasing slightly from 0.58 to 0.55. The count of serious injury crashes increased from 12 to 14, and minor injury crashes rose from 58 to 70. Conversely, crashes resulting in possible injuries decreased from 98 to 86.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.6%
→0.0%prior 4
Serious Injury14serious injury crashes1.9%
↑16.7%prior 12
Minor Injury70minor injury crashes9.7%
↑20.7%prior 58
Possible Injury86possible injury crashes11.9%
↓-12.2%prior 98
No Injury548no injury crashes75.9%
↑5.0%prior 522

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors were consistent across both years, with collisions involving an animal being the most common cause in both 2015 (143 crashes) and 2016 (141 crashes). While the top factor counts were stable, the number of crashes attributed to a driver running a stop sign increased by 54.5%, from 22 incidents in 2015 to 34 in 2016. Crashes caused by following too closely also grew in count from 33 to 41 during the same period.

Officer-Reported Primary Contributing Cause

Animal141 (19.5%)↓-1.4%prior 143
Lost Control59 (8.2%)↑7.3%prior 55
FTYROW: From stop sign55 (7.6%)↑5.8%prior 52
Followed too close41 (5.7%)↑24.2%prior 33
FTYROW: Making left turn38 (5.3%)↑11.8%prior 34
Other (explain in narrative): Other37 (5.1%)↑15.6%prior 32
Driving too fast for conditions36 (5%)↓-7.7%prior 39
Ran Stop Sign34 (4.7%)↑54.5%prior 22
Ran Traffic Signal26 (3.6%)↑18.2%prior 22
Ran off road - straight25 (3.5%)↑8.7%prior 23

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

In both 2015 and 2016, the majority of crashes occurred in clear weather and daylight conditions on dry roads. The number of crashes on dry surfaces increased from 405 to 447 year-over-year. While crashes during rain and snow events decreased, incidents on roads with ice or frost showed a notable increase, rising from 21 in 2015 to 32 in 2016.

Weather

Clear377 (63.3%)
↑7.1%prior 352
Cloudy156 (26.2%)
↑21.9%prior 128
Snow23 (3.9%)
↓-36.1%prior 36
Rain21 (3.5%)
↓-50.0%prior 42
Freezing rain/drizzle7 (1.2%)
↑16.7%prior 6
Blowing Snow5 (0.8%)
Severe Winds3 (0.5%)
Fog, smoke, smog3 (0.5%)
Sleet, hail1 (0.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight399 (66.8%)
→0.0%prior 399
Dark - roadway lighted85 (14.2%)
↑32.8%prior 64
Dark - roadway not lighted77 (12.9%)
↓-16.3%prior 92
Dusk20 (3.4%)
↑300.0%prior 5
Dawn14 (2.3%)
↑7.7%prior 13
Dark - unknown roadway lighting2 (0.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry447 (74.9%)
↑10.4%prior 405
Wet58 (9.7%)
↓-7.9%prior 63
Snow35 (5.9%)
↓-37.5%prior 56
Ice/frost32 (5.4%)
↑52.4%prior 21
Gravel14 (2.3%)
↓-6.7%prior 15
Slush10 (1.7%)
→0.0%prior 10
Mud, dirt1 (0.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes, led by Ford and Chevrolet, remained consistent between 2015 and 2016. Demographically, the number of persons involved in crashes from the 16-20 age group decreased from 201 to 173. Similarly, involvement for the 26-34 age group declined from 214 to 184 persons.

Top Vehicle Makes (1,168 vehicles)

1
FORD198 (17%)
↑7.6%prior 184
2
CHEV147 (12.6%)
↑2.1%prior 144
3
CHEVROLET102 (8.7%)
↑7.4%prior 95
4
JEEP53 (4.5%)
↑82.8%prior 29
5
DODGE51 (4.4%)
↑59.4%prior 32
6
DODG49 (4.2%)
↓-24.6%prior 65
7
HOND48 (4.1%)
↑14.3%prior 42
8
GMC35 (3%)
→0.0%prior 35
9
HONDA32 (2.7%)
↑39.1%prior 23
10
TOYOTA32 (2.7%)
↑52.4%prior 21

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (876 persons with recorded sex)

Male507 (57.9%)
↓-12.1%prior 577
Female369 (42.1%)
↓-14.8%prior 433

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 722
  • Total persons involved: 1,313
  • Total vehicles involved: 1,168

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: 2016." Published September 9, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-annual-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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