Yearly Traffic Safety Analysis

156 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Greene County, total crashes increased by 9.1% from 143 in 2017 to 156 in 2018. This period also saw total fatalities double from one to two. One of the most notable year-over-year shifts was the increase in crashes occurring on adverse road surfaces (wet, snow, ice, or slush), which rose from 28 incidents in 2017 to 46 in 2018.

156

▲ 9.1%was 143

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

48

▲ 14.3%was 42

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

Trend Summary

Traffic crashes in Greene County showed an upward trend, increasing by 9.1% from 143 incidents in 2017 to 156 in 2018. This rise was mirrored in crash severity, as total injuries increased 14.3% from 42 to 48, and fatalities doubled from one to two.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 1↑100.0%

0

Other Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 0→%

46

Motorists Injured

Prior: 42↑9.5%

1

Other Injured

Prior: 0→%

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 shifted year-over-year. The peak day for collisions moved from Thursday (25 crashes) in 2017 to Friday (37 crashes) in 2018. A similar shift occurred in the peak time of day, moving from the 7 a.m. hour in 2017 (13 crashes) to the 4 p.m. hour in 2018 (16 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

Overall crash severity increased from 2017 to 2018. The number of fatal crashes doubled from one to two, raising the fatal crash rate from 0.7% to 1.3%. The count of crashes involving any type of injury (serious, minor, or possible) grew from 31 in the prior year to 38 in the current year, while the share of no-injury crashes decreased from 77.6% to 74.4%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.3%
↑100.0%prior 1
Serious Injury7serious injury crashes4.5%
↑16.7%prior 6
Minor Injury14minor injury crashes9%
↑16.7%prior 12
Possible Injury17possible injury crashes10.9%
↑30.8%prior 13
No Injury116no injury crashes74.4%
↑4.5%prior 111

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

Collisions involving animals remained the leading contributing factor, with the count of such incidents increasing 23.8% from 42 in 2017 to 52 in 2018. A significant change was observed in crashes attributed to "Driving too fast for conditions," which quadrupled from 3 incidents to 12. "Driver Distraction: Other interior distraction" also grew as a factor, with crashes nearly doubling from 6 to 11.

Officer-Reported Primary Contributing Cause

Animal52 (33.3%)↑23.8%prior 42
Lost Control12 (7.7%)↓-14.3%prior 14
Driving too fast for conditions12 (7.7%)
Driver Distraction: Other interior distraction11 (7.1%)↑83.3%prior 6
Ran off road - straight9 (5.8%)↑28.6%prior 7
Ran Stop Sign7 (4.5%)
FTYROW: From stop sign7 (4.5%)↓-36.4%prior 11
FTYROW: At uncontrolled intersection6 (3.8%)↓-25.0%prior 8
Followed too close6 (3.8%)
Operating vehicle in an reckless, erratic, careless, negligent manner4 (2.6%)

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

Road & Environmental Conditions

There was a notable increase in crashes occurring under adverse conditions in 2018 compared to 2017. Collisions on roads with wet, snow, ice, or slush surfaces increased from a combined 28 incidents to 46. This aligns with a rise in crashes reported during snow or freezing rain, which collectively jumped from 4 incidents in 2017 to 17 in 2018. Meanwhile, crashes on dry roads decreased from 85 to 82.

Weather

Clear78 (60.5%)
↑6.8%prior 73
Cloudy26 (20.2%)
↓-36.6%prior 41
Snow11 (8.5%)
Freezing rain/drizzle6 (4.7%)
Rain3 (2.3%)
Fog, smoke, smog2 (1.6%)
Other (explain in narrative)1 (0.8%)
Sleet, hail1 (0.8%)
Blowing Snow1 (0.8%)

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

Lighting

Daylight81 (63.3%)
↑12.5%prior 72
Dark - roadway not lighted33 (25.8%)
↑10.0%prior 30
Dusk6 (4.7%)
Dark - roadway lighted5 (3.9%)
↓-37.5%prior 8
Dawn3 (2.3%)
↓-40.0%prior 5

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

Road Surface

Dry82 (63.6%)
↓-3.5%prior 85
Wet17 (13.2%)
↑13.3%prior 15
Snow15 (11.6%)
↑87.5%prior 8
Ice/frost10 (7.8%)
Slush4 (3.1%)
Gravel1 (0.8%)
↓-87.5%prior 8

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

Vehicles & Demographics

Ford and Chevrolet remained the most frequently involved vehicle makes in both periods, with the count for both increasing in 2018. Analysis of persons involved in crashes reveals a significant demographic shift; the number of individuals in the 35-44 age group more than doubled from 20 in 2017 to 51 in 2018. The 26-34 and 65+ age groups also saw notable increases in their involvement in collisions.

Top Vehicle Makes (228 vehicles)

1
FORD50 (21.9%)
↑31.6%prior 38
2
CHEV42 (18.4%)
↑55.6%prior 27
3
GMC13 (5.7%)
↑116.7%prior 6
4
BUIC13 (5.7%)
↑44.4%prior 9
5
DODG11 (4.8%)
↑37.5%prior 8
6
JEEP9 (3.9%)
↑80.0%prior 5
7
PONT8 (3.5%)
↑33.3%prior 6
8
CHEVROLET8 (3.5%)
↓-55.6%prior 18
9
HOND6 (2.6%)
↓-14.3%prior 7
10
NISS5 (2.2%)
→0.0%prior 5

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

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

Sex Distribution (185 persons with recorded sex)

Male95 (51.4%)
↑18.8%prior 80
Female90 (48.6%)
↑34.3%prior 67

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: 156
  • Total persons involved: 280
  • Total vehicles involved: 228

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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