Yearly Traffic Safety Analysis

90 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Worth County recorded 90 total vehicle crashes, a 10% decrease from the 100 crashes reported in 2017. While overall crashes and total injuries (27, down from 39) declined, the county experienced 2 fatal crashes resulting in 2 fatalities, compared to zero in the prior year. This introduction of fatal crashes marks the most significant year-over-year change in the data.

90

▼ -10.0%was 100

Total Crash Events

2

Persons Killed

27

▼ -30.8%was 39

Persons Injured

2

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

Overall traffic safety trends in Worth County showed a mix of improvement and concern. The total number of crashes fell by 10%, from 100 in 2017 to 90 in 2018, and the number of people injured in these incidents decreased by 30.8% from 39 to 27. However, this positive trend was offset by the occurrence of two fatal crashes in 2018, whereas none were recorded in 2017.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 0→%

27

Motorists Injured

Prior: 39↓-30.8%

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 a notable shift between the two periods. While Friday remained the peak day for crashes in both 2017 and 2018 with 20 incidents each, the peak hour changed dramatically. In 2018, the most dangerous hour was 9 PM with 14 crashes, a significant shift from 2017's peak at 3 PM, which saw 12 crashes. This indicates a change in crash timing from the afternoon to the late evening.

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 worsened in 2018 with the introduction of 2 fatal incidents, which accounted for 2.2% of all crashes, compared to zero fatal crashes in 2017. Despite this, the overall proportion of crashes resulting in any injury decreased from 29% in 2017 to 22.2% in 2018. The number of serious injury crashes remained stable at 3 in both years, while minor and possible injury crashes saw declines.

Outcome by Severity (Crash Events)

Fatal2fatal crashes2.2%
Serious Injury3serious injury crashes3.3%
→0.0%prior 3
Minor Injury8minor injury crashes8.9%
↓-33.3%prior 12
Possible Injury9possible injury crashes10%
↓-35.7%prior 14
No Injury68no injury crashes75.6%
↓-4.2%prior 71

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

The primary contributing factors cited in crashes remained consistent year-over-year, though their frequency shifted. Collisions involving an 'Animal' remained the top factor but decreased in count from 23 incidents in 2017 to 16 in 2018. 'Lost Control' was the second-most common factor in both years, with a slight decrease from 16 to 15 crashes. Conversely, crashes attributed to 'Driving too fast for conditions' increased from 12 in 2017 to 14 in 2018.

Officer-Reported Primary Contributing Cause

Animal16 (17.8%)↓-30.4%prior 23
Lost Control15 (16.7%)↓-6.3%prior 16
Driving too fast for conditions14 (15.6%)↑16.7%prior 12
Ran off road - straight10 (11.1%)↑11.1%prior 9
Ran off road - left4 (4.4%)↓-20.0%prior 5
Improper or erratic lane changing3 (3.3%)
Followed too close3 (3.3%)
FTYROW: From stop sign2 (2.2%)
FTYROW: Other (explain in narrative)2 (2.2%)
Other (explain in narrative): Other2 (2.2%)

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

Road & Environmental Conditions

An analysis of crash conditions reveals a shift towards more incidents occurring in adverse weather and poor lighting. The proportion of crashes in 'Dark - roadway not lighted' conditions increased from 26% of all incidents in 2017 to 36.7% in 2018. Similarly, the share of crashes on roads with ice or frost increased from 12% in 2017 to 18.9% in 2018. Crashes in clear weather were proportionally similar across both years, accounting for 41% of incidents in 2017 and 43.3% in 2018.

Weather

Clear39 (48.8%)
↓-4.9%prior 41
Cloudy16 (20.0%)
↓-33.3%prior 24
Snow11 (13.8%)
Freezing rain/drizzle5 (6.3%)
Blowing Snow5 (6.3%)
→0.0%prior 5
Rain3 (3.8%)
Fog, smoke, smog1 (1.3%)

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

Lighting

Daylight41 (51.2%)
↓-12.8%prior 47
Dark - roadway not lighted33 (41.3%)
↑26.9%prior 26
Dawn2 (2.5%)
Dusk2 (2.5%)
Dark - unknown roadway lighting1 (1.3%)
Dark - roadway lighted1 (1.3%)

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

Road Surface

Dry39 (48.8%)
↓-18.8%prior 48
Ice/frost17 (21.3%)
↑41.7%prior 12
Snow10 (12.5%)
↑25.0%prior 8
Wet7 (8.8%)
↓-30.0%prior 10
Gravel5 (6.3%)
Slush1 (1.3%)
Other (explain in narrative)1 (1.3%)

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

Vehicles & Demographics

Vehicle and demographic data indicate shifts in both the makes of vehicles and the age of persons involved in crashes. In 2018, Ford and Chevrolet were the most common vehicle makes involved, each with 22 vehicles, a change from 2017 when Chevrolet (30) was more prevalent than Ford (18). The age distribution of individuals in crashes also changed; in 2018, the 26-34 age group was most represented with 31 persons, whereas in 2017, the 45-54 and 65+ age groups were the largest (30 persons each). This suggests a shift towards younger adults being involved in collisions in the more recent period.

Top Vehicle Makes (120 vehicles)

1
FORD22 (18.3%)
↑22.2%prior 18
2
CHEV13 (10.8%)
↓-13.3%prior 15
3
CHEVROLET9 (7.5%)
↓-40.0%prior 15
4
GMC7 (5.8%)
↑40.0%prior 5
5
FREIGHTLINER7 (5.8%)
↑16.7%prior 6
6
VOLVO7 (5.8%)
7
DODGE4 (3.3%)
8
JEEP4 (3.3%)
↓-20.0%prior 5
9
TOYOTA4 (3.3%)
10
NISS3 (2.5%)

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

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

Sex Distribution (102 persons with recorded sex)

Male73 (71.6%)
↑7.4%prior 68
Female29 (28.4%)
↓-21.6%prior 37

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: 90
  • Total persons involved: 150
  • Total vehicles involved: 120

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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