Yearly Traffic Safety Analysis

190 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Emmet County, total traffic crashes increased by 4.4% from 182 in 2017 to 190 in 2018. While total injuries rose from 37 to 43, there were no fatalities recorded in either year. The most significant year-over-year change was a 350% increase in the count of crashes attributed to 'Driving too fast for conditions,' which rose from 4 to 18 incidents.

190

▲ 4.4%was 182

Total Crash Events

0

Persons Killed

43

▲ 16.2%was 37

Persons Injured

0

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

Overall, Emmet County experienced an upward trend in traffic incidents year-over-year. Total crashes increased by 4.4%, from 182 in 2017 to 190 in 2018. The number of people injured in these crashes also rose by 16.2%, from 37 to 43, while fatalities remained at zero for both periods.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 0→0.0%

1

Cyclists Injured

Prior: 0→%

42

Motorists Injured

Prior: 37↑13.5%

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 shift in the peak day of the week, moving from Friday in 2017 (33 crashes) to Tuesday in 2018 (38 crashes). The peak hour for collisions remained consistent, clustering in the evening. In 2018, the peak was at 6 p.m. with 15 crashes, which was tied for the peak hour in 2017 with 14 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 saw a slight increase in injury-related incidents, though no fatal crashes occurred in either 2017 or 2018. The count of serious injury crashes rose from 2 to 3, and minor injury crashes increased from 15 to 17. The proportion of crashes resulting in no injuries remained stable, accounting for 81.3% of incidents in 2017 and 81.1% in 2018.

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes1.6%
↑50.0%prior 2
Minor Injury17minor injury crashes8.9%
↑13.3%prior 15
Possible Injury16possible injury crashes8.4%
↓-5.9%prior 17
No Injury154no injury crashes81.1%
↑4.1%prior 148

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 top contributing factor in both years, though the count decreased from 63 in 2017 to 53 in 2018. The most dramatic shift was in crashes due to 'Driving too fast for conditions,' which increased from 4 incidents in 2017 to 18 in 2018, a 350% rise in count. This change elevated the factor from tenth place to the second-most common cause of crashes year-over-year.

Officer-Reported Primary Contributing Cause

Animal53 (27.9%)↓-15.9%prior 63
Driving too fast for conditions18 (9.5%)
Lost Control13 (6.8%)↓-13.3%prior 15
Other (explain in narrative): Other12 (6.3%)↓-20.0%prior 15
Followed too close11 (5.8%)↑10.0%prior 10
Operating vehicle in an reckless, erratic, careless, negligent manner7 (3.7%)↑16.7%prior 6
Ran off road - straight7 (3.7%)↑16.7%prior 6
Ran off road - left6 (3.2%)
FTYROW: From stop sign6 (3.2%)↑20.0%prior 5
FTYROW: From parked position5 (2.6%)→0.0%prior 5

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 significant shift in the environmental conditions under which crashes occurred. Collisions on dry roads decreased from 110 to 76 year-over-year. In contrast, crashes on icy roads increased fivefold from 5 to 25, and incidents on snowy surfaces tripled from 8 to 24. While crashes in daylight increased from 86 to 96, incidents on dark, unlighted roadways decreased from 40 to 29.

Weather

Clear80 (54.4%)
↓-11.1%prior 90
Cloudy34 (23.1%)
→0.0%prior 34
Snow13 (8.8%)
Rain7 (4.8%)
↑16.7%prior 6
Blowing Snow6 (4.1%)
Freezing rain/drizzle3 (2.0%)
Fog, smoke, smog3 (2.0%)
Sleet, hail1 (0.7%)

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

Lighting

Daylight96 (64.9%)
↑11.6%prior 86
Dark - roadway not lighted29 (19.6%)
↓-27.5%prior 40
Dark - roadway lighted19 (12.8%)
↑58.3%prior 12
Dusk3 (2.0%)
Dawn1 (0.7%)

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

Road Surface

Dry76 (51.4%)
↓-30.9%prior 110
Ice/frost25 (16.9%)
↑400.0%prior 5
Snow24 (16.2%)
↑200.0%prior 8
Wet16 (10.8%)
↑23.1%prior 13
Gravel4 (2.7%)
↓-20.0%prior 5
Slush2 (1.4%)
Mud, dirt1 (0.7%)

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

Vehicles & Demographics

Ford became the most common vehicle make involved in crashes in 2018, with its count rising from 33 to 52 vehicles, surpassing Chevrolet. The number of persons aged 16-20 involved in crashes increased substantially from 29 in 2017 to 48 in 2018. Conversely, the involvement of individuals in the 65+ age group decreased from 45 to 32.

Top Vehicle Makes (289 vehicles)

1
FORD52 (18%)
↑57.6%prior 33
2
CHEV42 (14.5%)
↑5.0%prior 40
3
CHEVROLET26 (9%)
↑8.3%prior 24
4
GMC20 (6.9%)
↑17.6%prior 17
5
DODG18 (6.2%)
↑28.6%prior 14
6
BUIC14 (4.8%)
↑55.6%prior 9
7
CHRY10 (3.5%)
↑42.9%prior 7
8
NR10 (3.5%)
↑100.0%prior 5
9
PONT9 (3.1%)
↑28.6%prior 7
10
TOYT8 (2.8%)
↓-27.3%prior 11

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

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

Sex Distribution (198 persons with recorded sex)

Male109 (55.1%)
↑0.9%prior 108
Female89 (44.9%)
↑12.7%prior 79

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: 190
  • Total persons involved: 364
  • Total vehicles involved: 289

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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