Yearly Traffic Safety Analysis

208 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Grundy County recorded 208 total crashes, a 4.5% increase from the 199 crashes documented in 2017. Despite the rise in total collisions, the number of reported injuries decreased by 26.7%, from 90 in the prior year to 66 in the current year. The number of fatalities remained stable, with one person killed in a crash in each period.

208

▲ 4.5%was 199

Total Crash Events

1

Persons Killed

66

▼ -26.7%was 90

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

Crash trends in Grundy County showed a slight increase year-over-year, with total collisions rising by 4.5% from 199 in 2017 to 208 in 2018. While the number of fatal crashes remained unchanged at one, there was a notable 26.7% decrease in total injuries, which fell from 90 to 66. The number of crashes involving DUIs was halved, from 4 in 2017 to 2 in 2018.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

1

Pedestrians Injured

Prior: 0→%

65

Motorists Injured

Prior: 90↓-27.8%

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 shifted between the two periods. In 2018, the peak time for crashes was the 3 PM hour with 18 incidents, a change from 2017's peak at the 5 PM hour, which saw 21 incidents. While Friday was the single most frequent crash day in 2017 with 42 crashes, in 2018, Thursday and Friday tied for the most crashes, each with 35.

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 distributions indicated a shift towards less severe outcomes in 2018 compared to 2017. While the number of fatal crashes remained constant at one in both years, the proportion of crashes resulting in no injuries increased from 66.8% to 74.5% of all incidents. Concurrently, the share of minor injury crashes decreased from 16.1% of all crashes in 2017 to 10.6% in 2018.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.5%
→0.0%prior 1
Serious Injury5serious injury crashes2.4%
↓-16.7%prior 6
Minor Injury22minor injury crashes10.6%
↓-31.3%prior 32
Possible Injury25possible injury crashes12%
↓-7.4%prior 27
No Injury155no injury crashes74.5%
↑16.5%prior 133

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 periods, with the count increasing from 60 in 2017 to 74 in 2018. The second-leading factor changed significantly; crashes attributed to 'Lost Control' dropped by 60%, from 30 incidents in 2017 to 12 in 2018. In contrast, crashes due to 'Driving too fast for conditions' increased in count by 55.6%, from 9 in 2017 to 14 in 2018.

Officer-Reported Primary Contributing Cause

Animal74 (35.6%)↑23.3%prior 60
Driving too fast for conditions14 (6.7%)↑55.6%prior 9
Ran off road - straight13 (6.3%)↑30.0%prior 10
Lost Control12 (5.8%)↓-60.0%prior 30
Ran off road - left12 (5.8%)↓-14.3%prior 14
Other (explain in narrative): Other10 (4.8%)↑66.7%prior 6
Driver Distraction: Other interior distraction7 (3.4%)
Operating vehicle in an reckless, erratic, careless, negligent manner6 (2.9%)↑20.0%prior 5
FTYROW: From driveway4 (1.9%)
Driver Distraction: Reaching for object(s)/fallen object(s)4 (1.9%)

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 in both years predominantly occurred in clear weather and daylight on dry roads. However, 2018 saw an increase in crashes under adverse winter conditions compared to 2017. The number of crashes on snowy roads rose from 13 to 18, and incidents on icy or frosty surfaces increased from 6 to 11. Combined, crashes on snow or ice accounted for 13.9% of all crashes in 2018, up from a 9.5% share in 2017.

Weather

Clear93 (64.6%)
↑4.5%prior 89
Cloudy28 (19.4%)
↓-9.7%prior 31
Snow15 (10.4%)
↑66.7%prior 9
Fog, smoke, smog4 (2.8%)
Rain4 (2.8%)
↓-42.9%prior 7

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

Lighting

Daylight88 (60.7%)
↓-8.3%prior 96
Dark - roadway not lighted37 (25.5%)
→0.0%prior 37
Dusk8 (5.5%)
↑33.3%prior 6
Dark - roadway lighted6 (4.1%)
Dawn5 (3.4%)
Dark - unknown roadway lighting1 (0.7%)

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

Road Surface

Dry93 (64.6%)
↓-10.6%prior 104
Snow18 (12.5%)
↑38.5%prior 13
Ice/frost11 (7.6%)
↑83.3%prior 6
Wet9 (6.3%)
↓-35.7%prior 14
Gravel8 (5.6%)
↑14.3%prior 7
Slush3 (2.1%)
Sand1 (0.7%)
Mud, dirt1 (0.7%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Chevrolet and Ford leading in both years; involvement for both makes increased in 2018. A notable demographic shift occurred among persons involved in crashes. The 16-20 age group, which was the largest group in 2017 with 63 individuals, saw its count decrease to 50 in 2018. Conversely, the 45-54 age group became the most represented in 2018, growing from 44 to 56 individuals.

Top Vehicle Makes (280 vehicles)

1
CHEV56 (20%)
↑27.3%prior 44
2
FORD54 (19.3%)
↑38.5%prior 39
3
DODG14 (5%)
↓-6.7%prior 15
4
CHEVROLET12 (4.3%)
↓-29.4%prior 17
5
NISS10 (3.6%)
6
CHRY9 (3.2%)
↓-43.8%prior 16
7
JEEP9 (3.2%)
↑12.5%prior 8
8
KIA9 (3.2%)
9
TOYT8 (2.9%)
↑14.3%prior 7
10
BUIC8 (2.9%)
↑60.0%prior 5

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

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

Sex Distribution (202 persons with recorded sex)

Male124 (61.4%)
↓-5.3%prior 131
Female78 (38.6%)
↑30.0%prior 60

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: 208
  • Total persons involved: 355
  • Total vehicles involved: 280

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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