Yearly Traffic Safety Analysis

309 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Benton County, total traffic crashes increased from 274 in 2017 to 309 in 2018, a 12.8% rise. This period also saw increases in total injuries from 103 to 124 and fatalities from 5 to 7. One of the most significant shifts was a doubling in the number of crashes involving a driver under the influence of alcohol or drugs, which grew from 7 to 14 incidents.

309

▲ 12.8%was 274

Total Crash Events

7

▲ 40.0%was 5

Persons Killed

124

▲ 20.4%was 103

Persons Injured

6

▲ 20.0%was 5

Fatal Crash Events

Note: "Persons Killed" (7) counts individual fatalities across all crash events. "Fatal" in the severity table below (6) 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 collisions in Benton County showed an upward trend from 2017 to 2018. The total number of crashes rose by 12.8%, from 274 to 309. Concurrently, the number of people injured increased by 20.4% (from 103 to 124), and the number of fatalities rose from 5 to 7.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

7

Motorists Killed

Prior: 5↑40.0%

2

Pedestrians Injured

Prior: 0→%

122

Motorists Injured

Prior: 101↑20.8%

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 saw some shifts between the two periods. While the peak hour for crashes remained 5 p.m. in both 2017 (26 crashes) and 2018 (30 crashes), the peak day of the week shifted from Thursday (50 crashes) in 2017 to Friday (60 crashes) in 2018. Monthly, November saw a notable spike in 2018 with 48 crashes, a substantial increase from 30 crashes in November of the prior year.

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 from 2017 to 2018. The number of fatal crashes increased from 5 to 6, and the fatal crash rate rose from 1.82 to 1.94 per 100 crashes. The proportion of crashes resulting in serious injuries also grew, accounting for 5.2% of all crashes in 2018 (16 incidents) compared to 4.0% in 2017 (11 incidents). Correspondingly, the share of no-injury crashes decreased from 70.4% to 68.9% year-over-year.

Severity is per crash event (most severe injury). 6 fatal crash events resulted in 7 persons killed.

Outcome by Severity (Crash Events)

Fatal6fatal crashes1.9%
↑20.0%prior 5
Serious Injury16serious injury crashes5.2%
↑45.5%prior 11
Minor Injury33minor injury crashes10.7%
↓-5.7%prior 35
Possible Injury41possible injury crashes13.3%
↑36.7%prior 30
No Injury213no injury crashes68.9%
↑10.4%prior 193

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

While collisions involving an 'Animal' remained the top contributing factor in both 2017 (83 crashes) and 2018 (81 crashes), several other factors saw significant increases in count. Crashes attributed to 'Ran off road - straight' nearly doubled, rising from 12 to 23 incidents. Similarly, crashes where a driver failed to yield from a stop sign ('FTYROW: From stop sign') increased from 16 to 23, and those cited for 'Driving too fast for conditions' grew from 14 to 21.

Officer-Reported Primary Contributing Cause

Animal81 (26.2%)↓-2.4%prior 83
Lost Control38 (12.3%)↓-9.5%prior 42
Ran off road - straight23 (7.4%)↑91.7%prior 12
FTYROW: From stop sign23 (7.4%)↑43.8%prior 16
Driving too fast for conditions21 (6.8%)↑50.0%prior 14
Ran Stop Sign14 (4.5%)↑40.0%prior 10
Followed too close13 (4.2%)↓-13.3%prior 15
Ran off road - left9 (2.9%)↓-10.0%prior 10
Exceeded authorized speed7 (2.3%)
Driver Distraction: Inattentive/lost in thought7 (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

There was a marked increase in crashes occurring during adverse conditions from 2017 to 2018. The number of crashes on wet, snowy, icy, or slush-covered roads more than doubled, rising from 28 incidents in 2017 to 65 in 2018. Similarly, crashes during rain, snow, or freezing rain increased from 17 to 39. In contrast, the number of crashes occurring in dark conditions decreased from 82 in 2017 to 63 in 2018.

Weather

Clear149 (63.4%)
↑13.7%prior 131
Cloudy41 (17.4%)
↓-28.1%prior 57
Snow16 (6.8%)
Rain12 (5.1%)
↑33.3%prior 9
Freezing rain/drizzle10 (4.3%)
Fog, smoke, smog4 (1.7%)
↓-33.3%prior 6
Other (explain in narrative)2 (0.9%)
Blowing Snow1 (0.4%)

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

Lighting

Daylight158 (67.5%)
↑33.9%prior 118
Dark - roadway not lighted48 (20.5%)
↓-31.4%prior 70
Dark - roadway lighted15 (6.4%)
↑25.0%prior 12
Dawn9 (3.8%)
↑28.6%prior 7
Dusk4 (1.7%)
↓-20.0%prior 5

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

Road Surface

Dry154 (65.0%)
↓-10.5%prior 172
Wet25 (10.5%)
↑92.3%prior 13
Snow19 (8.0%)
↑216.7%prior 6
Ice/frost17 (7.2%)
↑88.9%prior 9
Gravel15 (6.3%)
↑36.4%prior 11
Slush4 (1.7%)
Mud, dirt3 (1.3%)

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, Ford and Chevrolet, remained stable in ranking and volume between 2017 and 2018. Analysis of persons involved shows a notable demographic shift by age. The number of individuals in the 26-34 age group involved in crashes increased from 70 to 97, and the 65+ age group saw an increase from 59 to 75 persons. Conversely, the 16-20 age group saw a decrease in involvement, from 91 persons in 2017 to 76 in 2018.

Top Vehicle Makes (428 vehicles)

1
FORD79 (18.5%)
↑2.6%prior 77
2
CHEV69 (16.1%)
↑4.5%prior 66
3
CHEVROLET19 (4.4%)
↓-40.6%prior 32
4
TOYT18 (4.2%)
↑125.0%prior 8
5
BUIC18 (4.2%)
↑80.0%prior 10
6
DODG16 (3.7%)
↑45.5%prior 11
7
JEEP16 (3.7%)
↑100.0%prior 8
8
GMC13 (3%)
↑62.5%prior 8
9
PONT12 (2.8%)
↑140.0%prior 5
10
HYUN12 (2.8%)

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

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

Sex Distribution (324 persons with recorded sex)

Male186 (57.4%)
↑5.7%prior 176
Female138 (42.6%)
↑7.8%prior 128

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: 309
  • Total persons involved: 554
  • Total vehicles involved: 428

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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