Yearly Traffic Safety Analysis

377 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Boone County recorded 377 total crashes, a 4.6% decrease from the 395 crashes reported in 2017. Despite the overall decline in collisions and a 24.2% drop in total injuries from 161 to 122, the number of fatalities increased from one in the prior year to three in the current year. Crashes attributed to driving under the influence saw a notable decrease, falling from 17 in 2017 to 8 in 2018.

377

▼ -4.6%was 395

Total Crash Events

3

▲ 200.0%was 1

Persons Killed

122

▼ -24.2%was 161

Persons Injured

3

▲ 200.0%was 1

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 trend in traffic crashes in Boone County shows a decrease between 2017 and 2018, with total incidents falling by 4.6% from 395 to 377. This downward trend in crash volume was accompanied by a 24.2% reduction in persons injured. However, the number of fatalities more than doubled, increasing from one person in 2017 to three in 2018.

Vulnerable Road User Casualties

3

Motorists Killed

Prior: 1↑200.0%

122

Motorists Injured

Prior: 156↓-21.8%

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 peak time for crashes remained the 5 p.m. hour in both periods, with a slight increase in incidents from 32 in 2017 to 36 in 2018. A shift occurred in the peak day of the week for collisions. In 2017, Monday was the peak day with 67 crashes, whereas in 2018, the peak shifted to later in the week, with both Thursday and Friday recording 67 crashes each.

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

The severity of crashes worsened in 2018, with the fatal crash rate increasing from 0.25% to 0.8% year-over-year. This corresponds to a rise from one fatal crash in 2017 to three in 2018. Conversely, the proportion of crashes resulting in less severe outcomes improved, as the share of serious injury crashes fell from 3.5% to 2.4% and minor injury crashes declined from 13.2% to 10.1%. Consequently, the percentage of crashes with no injuries rose from 70.4% to 74.0%.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.8%
↑200.0%prior 1
Serious Injury9serious injury crashes2.4%
↓-35.7%prior 14
Minor Injury38minor injury crashes10.1%
↓-26.9%prior 52
Possible Injury48possible injury crashes12.7%
↓-4.0%prior 50
No Injury279no injury crashes74%
↑0.4%prior 278

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 with animals remained the primary contributing factor in both years, with a stable count of 100 in 2017 and 103 in 2018. A significant change occurred with crashes due to 'Lost Control,' which saw a 43% decrease in count from 37 to 21 incidents, dropping from the second to the fourth-ranked cause. In contrast, crashes from 'Ran Stop Sign' increased by 47% in count, from 15 to 22, making it the third most common factor in 2018.

Officer-Reported Primary Contributing Cause

Animal103 (27.3%)↑3.0%prior 100
FTYROW: From stop sign27 (7.2%)↓-20.6%prior 34
Ran Stop Sign22 (5.8%)↑46.7%prior 15
Lost Control21 (5.6%)↓-43.2%prior 37
Followed too close20 (5.3%)↓-13.0%prior 23
Ran off road - left19 (5%)↑58.3%prior 12
Ran off road - straight16 (4.2%)↓-33.3%prior 24
Other (explain in narrative): Other13 (3.4%)→0.0%prior 13
FTYROW: Making left turn13 (3.4%)↑8.3%prior 12
Driver Distraction: Other interior distraction12 (3.2%)↑100.0%prior 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

The distribution of crashes across environmental conditions saw some shifts between 2017 and 2018. The proportion of crashes occurring on dry roads decreased from 57.7% to 49.6%. While the share of crashes in adverse weather like snow or freezing rain declined from 8.4% to 6.4%, the proportion of collisions on slick surfaces such as ice, snow, or slush actually increased from 12.4% to 14.1%. Crashes in daylight conditions made up a slightly smaller share of the total, dropping from 54.2% in 2017 to 50.9% in 2018.

Weather

Clear177 (61.7%)
↑1.1%prior 175
Cloudy67 (23.3%)
↓-21.2%prior 85
Snow14 (4.9%)
↑55.6%prior 9
Rain13 (4.5%)
↑8.3%prior 12
Freezing rain/drizzle8 (2.8%)
↓-46.7%prior 15
Fog, smoke, smog4 (1.4%)
Blowing Snow2 (0.7%)
↓-71.4%prior 7
Severe Winds1 (0.3%)
Other (explain in narrative)1 (0.3%)

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

Lighting

Daylight192 (66.2%)
↓-10.3%prior 214
Dark - roadway not lighted47 (16.2%)
↓-19.0%prior 58
Dark - roadway lighted23 (7.9%)
↑27.8%prior 18
Dawn15 (5.2%)
↑66.7%prior 9
Dusk12 (4.1%)
↑33.3%prior 9
Dark - unknown roadway lighting1 (0.3%)

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

Road Surface

Dry187 (64.7%)
↓-18.0%prior 228
Wet41 (14.2%)
↑57.7%prior 26
Ice/frost27 (9.3%)
↓-20.6%prior 34
Snow24 (8.3%)
↑84.6%prior 13
Gravel7 (2.4%)
→0.0%prior 7
Slush2 (0.7%)
Mud, dirt1 (0.3%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, Ford and Chevrolet, remained consistent, though the number of vehicles from both manufacturers decreased slightly. There was a notable shift in the age demographics of persons involved in crashes. The proportion of individuals in the 16-20 age group fell from 14.4% of all persons in 2017 to 12.5% in 2018. Conversely, the representation of the 26-34 age group increased from 15.9% to 18.0%, and the 65+ age group grew from 10.1% to 11.8%.

Top Vehicle Makes (592 vehicles)

1
FORD102 (17.2%)
↓-3.8%prior 106
2
CHEV86 (14.5%)
↓-5.5%prior 91
3
DODG39 (6.6%)
↑18.2%prior 33
4
CHEVROLET32 (5.4%)
↓-36.0%prior 50
5
TOYT27 (4.6%)
↓-3.6%prior 28
6
JEEP27 (4.6%)
↑17.4%prior 23
7
CHRY18 (3%)
↑125.0%prior 8
8
GMC18 (3%)
↓-10.0%prior 20
9
PONT18 (3%)
↑100.0%prior 9
10
NISS14 (2.4%)

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

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

Sex Distribution (450 persons with recorded sex)

Male264 (58.7%)
↑3.9%prior 254
Female186 (41.3%)
↑1.1%prior 184

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: 377
  • Total persons involved: 718
  • Total vehicles involved: 592

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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