Yearly Traffic Safety Analysis

149 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Union County recorded 149 total crashes, a 5.1% decrease from the 157 crashes reported in 2016. Overall crashes, injuries, and fatalities all declined year-over-year. The most notable shift was in contributing factors, where crashes attributed to 'Lost Control' dropped by 65% from 20 to 7, while those from 'Failure to Yield Right of Way from a stop sign' increased by 80% from 10 to 18.

149

▼ -5.1%was 157

Total Crash Events

1

▼ -50.0%was 2

Persons Killed

66

▼ -9.6%was 73

Persons Injured

1

▼ -50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety metrics in Union County generally improved from 2016 to 2017. Total crashes fell by 5.1% from 157 to 149. The number of persons injured decreased by 9.6% from 73 to 66, and the number of fatalities was reduced from 2 to 1.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 2↓-50.0%

3

Pedestrians Injured

Prior: 1↑200.0%

1

Cyclists Injured

Prior: 1→0.0%

62

Motorists Injured

Prior: 71↓-12.7%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

A shift occurred in the daily peak for crashes, moving from the 2 p.m. hour in 2016 (18 crashes) to the 5 p.m. hour in 2017 (14 crashes). The peak day for collisions also moved from Friday in the prior year (31 crashes) to Thursday in the current year (30 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Overall crash severity decreased from 2016 to 2017. The number of fatal crashes was halved from 2 to 1, causing the fatal crash rate to fall from 1.27% to 0.67%. The proportion of crashes resulting in any injury also declined from 36.3% in 2016 to 32.2% in 2017. Correspondingly, the share of no-injury crashes increased from 62.4% to 67.1% of all incidents.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.7%
↓-50.0%prior 2
Serious Injury3serious injury crashes2%
↓-50.0%prior 6
Minor Injury20minor injury crashes13.4%
↑53.8%prior 13
Possible Injury25possible injury crashes16.8%
↓-34.2%prior 38
No Injury100no injury crashes67.1%
↑2.0%prior 98

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading causes of crashes changed significantly between the two periods. In 2017, 'Failure to Yield Right of Way from a stop sign' became the top factor, with its crash count increasing by 80% from 10 to 18 incidents. Conversely, 'Lost Control,' the leading factor in 2016 with 20 crashes, saw its count drop by 65% to 7 crashes in 2017. 'Followed too close' incidents increased from 13 to 17, becoming the second most common factor in 2017.

Officer-Reported Primary Contributing Cause

FTYROW: From stop sign18 (12.1%)↑80.0%prior 10
Followed too close17 (11.4%)↑30.8%prior 13
Ran off road - straight12 (8.1%)↑50.0%prior 8
FTYROW: Making left turn10 (6.7%)↓-37.5%prior 16
FTYROW: At uncontrolled intersection9 (6%)
Driving too fast for conditions8 (5.4%)
Ran Traffic Signal8 (5.4%)
Lost Control7 (4.7%)↓-65.0%prior 20
Animal7 (4.7%)↓-22.2%prior 9
Ran off road - left7 (4.7%)↑16.7%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in adverse conditions decreased from 2016 to 2017. Collisions on dry road surfaces increased from 72.6% to 77.2% of the total, while the share of crashes on adverse surfaces like snow or ice fell from 20.4% to 15.4%. Similarly, crashes in clear weather made up a larger share of the total in 2017 (68.5%) compared to 2016 (63.1%).

Weather

Clear102 (69.9%)
↑3.0%prior 99
Cloudy30 (20.5%)
↓-28.6%prior 42
Rain5 (3.4%)
↓-16.7%prior 6
Snow4 (2.7%)
↓-33.3%prior 6
Freezing rain/drizzle3 (2.1%)
Fog, smoke, smog2 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight98 (67.1%)
↓-10.9%prior 110
Dark - roadway not lighted28 (19.2%)
↑7.7%prior 26
Dark - roadway lighted9 (6.2%)
↓-18.2%prior 11
Dawn6 (4.1%)
Dark - unknown roadway lighting3 (2.1%)
Dusk2 (1.4%)
↓-60.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry115 (78.2%)
↑0.9%prior 114
Wet9 (6.1%)
↓-35.7%prior 14
Gravel9 (6.1%)
→0.0%prior 9
Ice/frost8 (5.4%)
Snow5 (3.4%)
↓-58.3%prior 12
Mud, dirt1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

There was a notable demographic shift among persons involved in crashes, with the 65+ age group's representation increasing from 10.9% in 2016 to 16.0% in 2017. The ranking of the most common vehicle makes also changed; Chevrolet-involved vehicles (72) surpassed Ford (48) in 2017, reversing the order from 2016 when Ford led with 61 vehicles to Chevrolet's 66.

Top Vehicle Makes (257 vehicles)

1
CHEV56 (21.8%)
↑40.0%prior 40
2
FORD48 (18.7%)
↓-21.3%prior 61
3
DODG17 (6.6%)
↑6.3%prior 16
4
CHEVROLET16 (6.2%)
↓-38.5%prior 26
5
PONT11 (4.3%)
→0.0%prior 11
6
DODGE10 (3.9%)
↓-23.1%prior 13
7
BUIC8 (3.1%)
↑33.3%prior 6
8
JEEP7 (2.7%)
↓-53.3%prior 15
9
KIA6 (2.3%)
10
GMC6 (2.3%)
↑20.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (207 persons with recorded sex)

Male117 (56.5%)
↑11.4%prior 105
Female90 (43.5%)
↓-9.1%prior 99

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 149
  • Total persons involved: 307
  • Total vehicles involved: 257

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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