Yearly Traffic Safety Analysis

88 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Osceola County, total traffic crashes decreased slightly from 90 in 2017 to 88 in 2018, a 2.2% reduction. While overall crashes remained stable, the most significant year-over-year shift was the complete elimination of traffic fatalities, which dropped from 4 in the prior year to 0 in the current year. Conversely, the total number of injuries reported rose from 30 to 38.

88

▼ -2.2%was 90

Total Crash Events

0

▼ -100.0%was 4

Persons Killed

38

▲ 26.7%was 30

Persons Injured

0

▼ -100.0%was 3

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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

The overall crash trend in Osceola County shows a slight decrease in total incidents, falling from 90 to 88 year-over-year. However, this period saw a notable increase in non-fatal injuries, which rose by 26.7% from 30 to 38. The most positive trend was a 100% reduction in fatalities, with zero recorded in 2018 compared to 4 in 2017.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 4↓-100.0%

1

Pedestrians Injured

Prior: 0→%

37

Motorists Injured

Prior: 30↑23.3%

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

Temporal crash patterns shifted between the two periods. In 2018, crashes peaked on Mondays, Tuesdays, and Thursdays, with 15 incidents each, and the peak hour was 5 p.m. with 10 crashes. This contrasts with 2017, when Saturday was the peak day with 21 crashes and 7 p.m. was the peak hour with 8 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

Crash severity improved significantly, with fatal crashes dropping from 3 in 2017 to 0 in 2018. Consequently, the fatality rate per 100 crashes fell from 3.33 to 0. Despite this, the total count of injury-related crashes increased, with minor injury crashes doubling from 7 to 14 and possible injury crashes rising from 9 to 11. The share of all crashes resulting in any injury increased from 33.3% in 2017 to 37.5% in 2018.

Outcome by Severity (Crash Events)

Serious Injury4serious injury crashes4.5%
↓-20.0%prior 5
Minor Injury14minor injury crashes15.9%
↑100.0%prior 7
Possible Injury11possible injury crashes12.5%
↑22.2%prior 9
No Injury59no injury crashes67%
↓-10.6%prior 66

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 in both years, with a slight increase in count from 25 in 2017 to 26 in 2018. The most significant change was in crashes attributed to 'Driving too fast for conditions,' which more than doubled from 5 to 13 incidents. Conversely, crashes involving 'Lost Control' decreased by more than half, from 15 incidents in 2017 to 7 in 2018, dropping from the second to the fourth-ranked factor.

Officer-Reported Primary Contributing Cause

Animal26 (29.5%)↑4.0%prior 25
Driving too fast for conditions13 (14.8%)↑160.0%prior 5
FTYROW: From stop sign7 (8%)
Lost Control7 (8%)↓-53.3%prior 15
FTYROW: At uncontrolled intersection6 (6.8%)→0.0%prior 6
Ran off road - left4 (4.5%)
Ran Stop Sign4 (4.5%)
Followed too close3 (3.4%)
Exceeded authorized speed2 (2.3%)
Made improper turn2 (2.3%)

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

Road & Environmental Conditions

While clear weather and dry roads remained the most common conditions for crashes in both years, there was a notable increase in incidents occurring during adverse conditions. Crashes on roads with snow or ice increased from 11 in 2017 to 18 in 2018. Similarly, crashes during snowy weather conditions (Snow or Blowing Snow) doubled from 5 to 10. Crashes in daylight increased from 40 to 45, while those in unlit dark conditions fell from 24 to 13.

Weather

Clear40 (60.6%)
↓-20.0%prior 50
Cloudy12 (18.2%)
↑20.0%prior 10
Blowing Snow5 (7.6%)
Snow5 (7.6%)
Rain2 (3.0%)
Fog, smoke, smog1 (1.5%)
Freezing rain/drizzle1 (1.5%)

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

Lighting

Daylight45 (68.2%)
↑12.5%prior 40
Dark - roadway not lighted13 (19.7%)
↓-45.8%prior 24
Dawn4 (6.1%)
Dark - roadway lighted3 (4.5%)
↓-40.0%prior 5
Dusk1 (1.5%)

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

Road Surface

Dry36 (55.4%)
↓-28.0%prior 50
Snow10 (15.4%)
Ice/frost8 (12.3%)
↓-11.1%prior 9
Gravel4 (6.2%)
↓-20.0%prior 5
Wet2 (3.1%)
Other (explain in narrative)2 (3.1%)
Slush2 (3.1%)
Water (standing or moving)1 (1.5%)

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

Vehicles & Demographics

Ford and Chevrolet vehicles were the most common makes involved in crashes in both years. The number of Jeeps involved in crashes saw a notable increase, rising from 2 in 2017 to 9 in 2018. Analysis of persons involved shows a significant increase in the 65+ age group, which grew from 16 individuals in 2017 to 28 in 2018. The total number of people involved in crashes increased from 145 to 167 year-over-year.

Top Vehicle Makes (122 vehicles)

1
FORD25 (20.5%)
↑13.6%prior 22
2
CHEVROLET16 (13.1%)
↑77.8%prior 9
3
CHEV11 (9%)
↓-31.3%prior 16
4
JEEP9 (7.4%)
5
GMC6 (4.9%)
6
DODG5 (4.1%)
↓-28.6%prior 7
7
CHRY4 (3.3%)
8
BUIC4 (3.3%)
9
HONDA4 (3.3%)
10
PONT4 (3.3%)

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

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

Sex Distribution (101 persons with recorded sex)

Male52 (51.5%)
↓-7.1%prior 56
Female49 (48.5%)
↑75.0%prior 28

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 10, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 88
  • Total persons involved: 167
  • Total vehicles involved: 122

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 10, 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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