Yearly Traffic Safety Analysis

157 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Union County, total crashes increased by 5.4% from 149 in 2017 to 157 in 2018. While total fatalities remained steady at one, the number of crashes attributed to 'Lost Control' doubled from 7 to 14 incidents year-over-year.

157

▲ 5.4%was 149

Total Crash Events

1

Persons Killed

64

▼ -3.0%was 66

Persons Injured

1

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 · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Union County showed a slight upward trend, increasing by 5.4% from 149 incidents in 2017 to 157 in 2018. Despite the rise in total crashes, the number of resulting injuries saw a minor decrease from 66 to 64, and fatalities remained unchanged with one death recorded in each period.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

1

Cyclists Injured

Prior: 1→0.0%

63

Motorists Injured

Prior: 62↑1.6%

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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 consistency and some shifts between 2017 and 2018. Thursday remained the peak day for crashes in both years, with 29 incidents in 2018 compared to 30 in the prior year. However, the peak hour for collisions shifted two hours earlier, moving from 5 PM in 2017 (14 crashes) to 3 PM in 2018 (18 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes remained broadly similar year-over-year, with one fatal crash recorded in both 2017 and 2018. The proportion of crashes resulting in no injuries increased from 67.1% of all crashes in 2017 to 69.4% in 2018. Crashes involving serious injuries decreased from 3 to 2, while those with minor injuries rose from 20 to 23.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.6%
→0.0%prior 1
Serious Injury2serious injury crashes1.3%
↓-33.3%prior 3
Minor Injury23minor injury crashes14.6%
↑15.0%prior 20
Possible Injury22possible injury crashes14%
↓-12.0%prior 25
No Injury109no injury crashes69.4%
↑9.0%prior 100

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Most severe injury per crash record

Top Contributing Factors

Failure to yield from a stop sign remained the leading contributing factor in both periods, increasing from 18 crashes in 2017 to 20 in 2018. The most significant change was in 'Lost Control' incidents, which doubled in count from 7 to 14, becoming the second-most common factor in 2018. Conversely, crashes attributed to 'Followed too close' decreased from 17 to 12, and 'Ran off road - straight' incidents fell from 12 to 7.

Officer-Reported Primary Contributing Cause

FTYROW: From stop sign20 (12.7%)↑11.1%prior 18
Lost Control14 (8.9%)↑100.0%prior 7
FTYROW: Making left turn13 (8.3%)↑30.0%prior 10
Followed too close12 (7.6%)↓-29.4%prior 17
Driving too fast for conditions8 (5.1%)→0.0%prior 8
Other (explain in narrative): Other8 (5.1%)
Improper Backing7 (4.5%)
Ran off road - straight7 (4.5%)↓-41.7%prior 12
Animal7 (4.5%)→0.0%prior 7
Operating vehicle in an reckless, erratic, careless, negligent manner6 (3.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes under clear weather and on dry roads remained the most common scenarios in both years. However, there was a notable increase in crashes occurring on adverse road surfaces, with incidents on snow or ice more than doubling from 13 in 2017 to 30 in 2018. Correspondingly, crashes reported during snowy weather increased from 4 to 11. The proportion of crashes occurring in daylight rose from 65.8% of all crashes in 2017 to 71.3% in 2018.

Weather

Clear94 (61.8%)
↓-7.8%prior 102
Cloudy36 (23.7%)
↑20.0%prior 30
Snow11 (7.2%)
Rain5 (3.3%)
→0.0%prior 5
Freezing rain/drizzle4 (2.6%)
Blowing Snow1 (0.7%)
Fog, smoke, smog1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Weather condition at time of crash

Lighting

Daylight112 (74.2%)
↑14.3%prior 98
Dark - roadway not lighted19 (12.6%)
↓-32.1%prior 28
Dark - roadway lighted14 (9.3%)
↑55.6%prior 9
Dawn3 (2.0%)
↓-50.0%prior 6
Dusk3 (2.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Lighting condition field

Road Surface

Dry105 (69.5%)
↓-8.7%prior 115
Snow19 (12.6%)
↑280.0%prior 5
Ice/frost11 (7.3%)
↑37.5%prior 8
Wet9 (6.0%)
→0.0%prior 9
Gravel3 (2.0%)
↓-66.7%prior 9
Mud, dirt2 (1.3%)
Sand1 (0.7%)
Slush1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Road surface condition field

Vehicles & Demographics

Analysis of vehicles involved shows that Ford and Chevrolet were the top two makes in both years, swapping positions for the top rank; Ford-made vehicles increased from 48 to 55, while Chevrolet-made vehicles decreased from 56 to 46. Regarding the demographics of individuals involved in crashes, there was a notable increase in the 55-64 age group, which grew from 24 persons in 2017 to 41 in 2018. Conversely, the number of persons aged 16-20 decreased from 54 to 48, and the 65+ age group saw a reduction from 49 to 36.

Top Vehicle Makes (278 vehicles)

1
FORD55 (19.8%)
↑14.6%prior 48
2
CHEV46 (16.5%)
↓-17.9%prior 56
3
DODG21 (7.6%)
↑23.5%prior 17
4
BUIC16 (5.8%)
↑100.0%prior 8
5
CHEVROLET15 (5.4%)
↓-6.3%prior 16
6
CHRY14 (5%)
↑180.0%prior 5
7
PONT10 (3.6%)
↓-9.1%prior 11
8
GMC10 (3.6%)
↑66.7%prior 6
9
JEEP8 (2.9%)
↑14.3%prior 7
10
DODGE7 (2.5%)
↓-30.0%prior 10

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records

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

Sex Distribution (213 persons with recorded sex)

Male117 (54.9%)
→0.0%prior 117
Female96 (45.1%)
↑6.7%prior 90

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 157
  • Total persons involved: 341
  • Total vehicles involved: 278

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: 2018." Published September 9, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2018-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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