Yearly Traffic Safety Analysis

565 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Marion County, total traffic crashes increased by 8.2% from 522 in 2015 to 565 in 2016. While the number of fatalities remained unchanged at three, total injuries rose slightly from 160 to 163. The most notable year-over-year shift was a 36% increase in non-collision, single-vehicle incidents, which grew from 225 to 306 crashes.

565

▲ 8.2%was 522

Total Crash Events

3

Persons Killed

163

▲ 1.9%was 160

Persons Injured

3

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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, Marion County experienced an upward trend in traffic collisions, with total crashes rising from 522 in 2015 to 565 in 2016, an 8.2% increase. The number of people injured in these incidents saw a marginal increase of 1.9%, from 160 to 163. Fatalities held steady year-over-year, with three individuals killed in both periods.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 3→0.0%

4

Pedestrians Injured

Prior: 1↑300.0%

2

Cyclists Injured

Prior: 6↓-66.7%

157

Motorists Injured

Prior: 152↑3.3%

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 showed some changes between the two years, though Friday remained the peak day for crashes in both 2015 and 2016 with 97 incidents. The peak hour for collisions shifted earlier, from 5 p.m. in 2015 (45 crashes) to 3 p.m. in 2016 (47 crashes). Crashes occurring on Mondays also saw a notable increase, rising from 63 in the prior year to 90 in the current year.

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

The severity of crashes shifted year-over-year, with a notable decrease in the most severe non-fatal category. The number of serious injury crashes fell from 17 in 2015 to 10 in 2016, a drop from 3.3% to 1.8% of all crashes. Conversely, crashes resulting in minor injuries increased from 45 to 53, and property-damage-only crashes rose from 387 to 427. The number of fatal crashes was unchanged at three in both years.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.5%
→0.0%prior 3
Serious Injury10serious injury crashes1.8%
↓-41.2%prior 17
Minor Injury53minor injury crashes9.4%
↑17.8%prior 45
Possible Injury72possible injury crashes12.7%
↑2.9%prior 70
No Injury427no injury crashes75.6%
↑10.3%prior 387

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

While collisions involving an animal remained the top contributing factor in both periods, their count slightly decreased from 171 to 167. A significant shift occurred in intersection-related factors; crashes attributed to 'Failure to Yield Right of Way from a stop sign' dropped by 51% from 39 to 19 incidents. In contrast, crashes where a driver 'Ran Stop Sign' increased from 6 to 25. 'Lost Control' also became more prevalent, with crash counts rising 50% from 32 to 48.

Officer-Reported Primary Contributing Cause

Animal167 (29.6%)↓-2.3%prior 171
Lost Control48 (8.5%)↑50.0%prior 32
Other (explain in narrative): Other35 (6.2%)↓-2.8%prior 36
Followed too close34 (6%)↑78.9%prior 19
Driving too fast for conditions32 (5.7%)↑60.0%prior 20
Ran off road - straight30 (5.3%)↑100.0%prior 15
Ran Stop Sign25 (4.4%)↑316.7%prior 6
FTYROW: From stop sign19 (3.4%)↓-51.3%prior 39
Ran off road - left19 (3.4%)↑5.6%prior 18
Driver Distraction: Other interior distraction15 (2.7%)↑114.3%prior 7

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

Road & Environmental Conditions

Crashes occurred more frequently in adverse weather in 2016 compared to 2015. Incidents during snowy conditions more than doubled, increasing from 12 to 29. The proportion of crashes happening in daylight also increased, accounting for 53.5% of all crashes (302 incidents) in 2016, up from 48.7% (254 incidents) in 2015. Crashes on dry road surfaces remained the most common scenario in both years, rising from 288 to 326.

Weather

Clear286 (66.2%)
↑6.7%prior 268
Cloudy90 (20.8%)
↑38.5%prior 65
Snow29 (6.7%)
↑141.7%prior 12
Rain16 (3.7%)
↓-20.0%prior 20
Freezing rain/drizzle4 (0.9%)
Fog, smoke, smog3 (0.7%)
↓-66.7%prior 9
Other (explain in narrative)1 (0.2%)
Blowing Snow1 (0.2%)
Severe Winds1 (0.2%)
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

Daylight302 (69.6%)
↑18.9%prior 254
Dark - roadway not lighted66 (15.2%)
↑22.2%prior 54
Dark - roadway lighted35 (8.1%)
↓-10.3%prior 39
Dusk19 (4.4%)
↑11.8%prior 17
Dawn12 (2.8%)
↑9.1%prior 11

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

Road Surface

Dry326 (75.8%)
↑13.2%prior 288
Wet37 (8.6%)
↑5.7%prior 35
Snow25 (5.8%)
↑13.6%prior 22
Gravel19 (4.4%)
↑35.7%prior 14
Ice/frost16 (3.7%)
↑6.7%prior 15
Slush5 (1.2%)
Mud, dirt2 (0.5%)

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 remained consistent, with Ford, Chevrolet, and Dodge leading in both years; each make saw an increase in crash involvement in 2016. Analysis of persons involved shows a demographic shift, with a decrease in the 21-25 age group (from 115 to 95 individuals) and an increase in the 45-54 age group (from 124 to 135 individuals). The total number of people involved in crashes decreased slightly from 943 to 931.

Top Vehicle Makes (824 vehicles)

1
FORD155 (18.8%)
↑9.9%prior 141
2
CHEVROLET93 (11.3%)
↑20.8%prior 77
3
CHEV89 (10.8%)
↓-8.2%prior 97
4
DODG47 (5.7%)
↑11.9%prior 42
5
DODGE34 (4.1%)
↑47.8%prior 23
6
GMC29 (3.5%)
→0.0%prior 29
7
PONT26 (3.2%)
↓-3.7%prior 27
8
JEEP25 (3%)
↑31.6%prior 19
9
TOYT18 (2.2%)
↓-28.0%prior 25
10
PONTIAC17 (2.1%)
↑21.4%prior 14

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

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

Sex Distribution (656 persons with recorded sex)

Male386 (58.8%)
↓-1.3%prior 391
Female270 (41.2%)
↓-17.7%prior 328

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: 565
  • Total persons involved: 931
  • Total vehicles involved: 824

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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