Yearly Traffic Safety Analysis

157 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Union County, traffic crashes increased from 154 in 2015 to 157 in 2016, a change of approximately 2%. While the overall crash volume remained relatively stable, the number of people injured in these incidents rose from 50 to 73, a 46% increase year-over-year. Fatalities resulting from these crashes also increased from one in 2015 to two in 2016.

157

▲ 1.9%was 154

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

73

▲ 46.0%was 50

Persons Injured

2

▲ 100.0%was 1

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 crash trends in Union County showed a slight increase in 2016 compared to the prior year, with total incidents rising from 154 to 157. This small increase in crash volume was accompanied by a more significant rise in negative outcomes, as total injuries increased by 46% from 50 to 73. The number of fatal crashes doubled from one to two.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 1↑100.0%

1

Pedestrians Injured

Prior: 2↓-50.0%

1

Cyclists Injured

Prior: 0→%

71

Motorists Injured

Prior: 48↑47.9%

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 in Union County shifted between 2015 and 2016. The peak day for crashes moved from Wednesday, with 30 incidents in 2015, to Friday, with 31 incidents in 2016. Similarly, the peak hour for incidents shifted an hour earlier, from 3 PM (15 crashes) in the prior year to 2 PM (18 crashes) 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

Crash severity increased in 2016 compared to the previous year. The number of fatal crashes doubled from one to two, and the corresponding fatal crash rate rose from 0.65 to 1.27 per 100 crashes. The proportion of crashes resulting in any level of injury (Serious, Minor, or Possible) also grew, accounting for 36.3% of all incidents in 2016, up from 26.6% in 2015. This included an increase in serious injury crashes from two to six.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.3%
↑100.0%prior 1
Serious Injury6serious injury crashes3.8%
↑200.0%prior 2
Minor Injury13minor injury crashes8.3%
↑8.3%prior 12
Possible Injury38possible injury crashes24.2%
↑40.7%prior 27
No Injury98no injury crashes62.4%
↓-12.5%prior 112

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 for crashes shifted between 2015 and 2016. In 2016, 'Lost Control' became the top factor with 20 crashes, an increase from 13 crashes in the prior year. Crashes attributed to 'Failure to Yield Right of Way: Making left turn' also saw a notable increase, rising from 10 to 16 incidents. Conversely, 'Failure to Yield Right of Way: From stop sign,' which was the leading factor in 2015 with 17 crashes, decreased to 10 crashes in 2016.

Officer-Reported Primary Contributing Cause

Lost Control20 (12.7%)↑53.8%prior 13
FTYROW: Making left turn16 (10.2%)↑60.0%prior 10
Followed too close13 (8.3%)↑62.5%prior 8
FTYROW: From stop sign10 (6.4%)↓-41.2%prior 17
Animal9 (5.7%)↓-25.0%prior 12
Ran off road - straight8 (5.1%)↑14.3%prior 7
Ran Stop Sign7 (4.5%)↑16.7%prior 6
FTYROW: Other (explain in narrative)6 (3.8%)→0.0%prior 6
Driver Distraction: Other interior distraction6 (3.8%)
Ran off road - left6 (3.8%)→0.0%prior 6

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

Road & Environmental Conditions

The majority of crashes in both 2015 and 2016 occurred in clear weather and on dry road surfaces. However, the proportion of crashes happening during adverse weather conditions like rain or snow decreased, accounting for 8.9% of all incidents in 2016 compared to 14.9% in 2015. The distribution of crashes by lighting and road surface conditions remained largely consistent year-over-year, with about 29% of crashes occurring in non-daylight conditions in both periods.

Weather

Clear99 (63.5%)
↑6.5%prior 93
Cloudy42 (26.9%)
↑23.5%prior 34
Rain6 (3.8%)
↓-57.1%prior 14
Snow6 (3.8%)
→0.0%prior 6
Freezing rain/drizzle1 (0.6%)
Fog, smoke, smog1 (0.6%)
Blowing sand, soil, dirt1 (0.6%)

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

Lighting

Daylight110 (70.5%)
↑3.8%prior 106
Dark - roadway not lighted26 (16.7%)
↓-3.7%prior 27
Dark - roadway lighted11 (7.1%)
↑10.0%prior 10
Dusk5 (3.2%)
Dawn4 (2.6%)

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

Road Surface

Dry114 (73.1%)
↓-0.9%prior 115
Wet14 (9.0%)
↓-41.7%prior 24
Snow12 (7.7%)
↑100.0%prior 6
Gravel9 (5.8%)
Ice/frost3 (1.9%)
Slush2 (1.3%)
Other (explain in narrative)1 (0.6%)
Mud, dirt1 (0.6%)

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

Vehicles & Demographics

Ford and Chevrolet vehicles were the most frequently involved in crashes in both years, with their relative rankings shifting slightly. In 2016, Chevrolet vehicles were involved in 66 crashes and Fords in 61, while in 2015 they were tied at 67 vehicles each. Regarding persons involved, the 16-20 age group was the most represented in both periods, though their numbers decreased from 63 individuals in 2015 to 55 in 2016. The number of individuals aged 65 and older involved in crashes also saw a decrease from 44 to 34.

Top Vehicle Makes (276 vehicles)

1
FORD61 (22.1%)
↓-9.0%prior 67
2
CHEV40 (14.5%)
↓-13.0%prior 46
3
CHEVROLET26 (9.4%)
↑23.8%prior 21
4
DODG16 (5.8%)
↓-15.8%prior 19
5
JEEP15 (5.4%)
↑50.0%prior 10
6
DODGE13 (4.7%)
↑44.4%prior 9
7
PONT11 (4%)
↓-45.0%prior 20
8
PONTIAC7 (2.5%)
9
CHRY7 (2.5%)
↓-41.7%prior 12
10
BUIC6 (2.2%)

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

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

Sex Distribution (204 persons with recorded sex)

Male105 (51.5%)
↓-26.1%prior 142
Female99 (48.5%)
↑2.1%prior 97

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: 157
  • Total persons involved: 312
  • Total vehicles involved: 276

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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