Yearly Traffic Safety Analysis

274 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Benton County recorded 274 total crashes, a 2.6% increase from the 267 crashes in 2016. Despite this slight rise in crash volume, outcomes improved, with total fatalities dropping 44% from 9 to 5 and total injuries decreasing 19% from 127 to 103. The most pronounced shift in crash causation was a 77% year-over-year increase in the count of crashes attributed to animals, which rose from 47 to 83 incidents.

274

▲ 2.6%was 267

Total Crash Events

5

▼ -44.4%was 9

Persons Killed

103

▼ -18.9%was 127

Persons Injured

5

▼ -16.7%was 6

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends in Benton County show a slight increase in volume but a marked decrease in severity year-over-year. Total crashes rose from 267 in 2016 to 274 in 2017, an increase of 2.6%. In contrast, fatalities fell from 9 to 5, and injuries dropped from 127 to 103 during the same period, indicating that while more incidents occurred, they were less severe on average.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 9↓-44.4%

2

Cyclists Injured

Prior: 0→%

101

Motorists Injured

Prior: 127↓-20.5%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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 in the peak hour of day, while the peak day of the week remained consistent. Thursday was the most common day for crashes in both 2017 (50 crashes) and 2016 (45 crashes). However, the peak hour for crashes shifted from the morning commute at 7 a.m. in 2016, which saw 21 crashes, to the evening commute at 5 p.m. in 2017, which saw 26 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased in 2017 compared to the prior year. The number of fatal crashes fell from 6 to 5, and the fatal crash rate as a share of all crashes dipped from 2.2% to 1.8%. Similarly, crashes resulting in a serious injury decreased from 18 to 11. Consequently, the proportion of crashes with no reported injuries increased from 64.4% of all incidents in 2016 to 70.4% in 2017.

Outcome by Severity (Crash Events)

Fatal5fatal crashes1.8%
↓-16.7%prior 6
Serious Injury11serious injury crashes4%
↓-38.9%prior 18
Minor Injury35minor injury crashes12.8%
↑25.0%prior 28
Possible Injury30possible injury crashes10.9%
↓-30.2%prior 43
No Injury193no injury crashes70.4%
↑12.2%prior 172

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

While the top two contributing factors remained the same year-over-year, their counts shifted significantly. "Animal" was the leading factor in both periods, but its count surged by 77%, from 47 crashes in 2016 to 83 in 2017. "Lost Control" remained the second-ranked cause with a stable count (39 in 2016 vs. 42 in 2017). Notably, crashes attributed to "Driving too fast for conditions" decreased by 42% in count, from 24 incidents in 2016 to 14 in 2017.

Officer-Reported Primary Contributing Cause

Animal83 (30.3%)↑76.6%prior 47
Lost Control42 (15.3%)↑7.7%prior 39
FTYROW: From stop sign16 (5.8%)↑45.5%prior 11
Followed too close15 (5.5%)↑7.1%prior 14
Driving too fast for conditions14 (5.1%)↓-41.7%prior 24
Ran off road - straight12 (4.4%)↓-40.0%prior 20
Ran off road - left10 (3.6%)↑100.0%prior 5
Ran Stop Sign10 (3.6%)↑11.1%prior 9
Driver Distraction: Other interior distraction7 (2.6%)↓-36.4%prior 11
Operating vehicle in an reckless, erratic, careless, negligent manner7 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crash conditions saw a shift in lighting, with a lower proportion of crashes occurring in daylight (55.4% in 2016 vs. 43.1% in 2017) and a higher proportion in unlit dark conditions (18.7% vs. 25.5%). There was a significant improvement regarding road surface conditions, as the number of crashes on adverse surfaces like ice, snow, and slush dropped from 49 in 2016 to 28 in 2017. The proportion of crashes on dry roads remained stable across both years.

Weather

Clear131 (62.1%)
↓-9.0%prior 144
Cloudy57 (27.0%)
↑7.5%prior 53
Rain9 (4.3%)
↑12.5%prior 8
Fog, smoke, smog6 (2.8%)
→0.0%prior 6
Snow4 (1.9%)
↓-42.9%prior 7
Freezing rain/drizzle3 (1.4%)
Blowing Snow1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight118 (55.7%)
↓-20.3%prior 148
Dark - roadway not lighted70 (33.0%)
↑40.0%prior 50
Dark - roadway lighted12 (5.7%)
→0.0%prior 12
Dawn7 (3.3%)
↓-53.3%prior 15
Dusk5 (2.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry172 (81.1%)
↑6.2%prior 162
Wet13 (6.1%)
↓-7.1%prior 14
Gravel11 (5.2%)
↓-26.7%prior 15
Ice/frost9 (4.2%)
↓-50.0%prior 18
Snow6 (2.8%)
↓-53.8%prior 13
Other (explain in narrative)1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

While the top vehicle makes involved in crashes, led by Chevrolet and Ford, remained consistent between 2016 and 2017, there was a significant demographic shift among persons involved. The number of individuals in the 16-20 age group involved in crashes increased by 65.5%, from 55 people in 2016 to 91 in 2017. This made the 16-20 age bracket the most represented group in 2017, a change from 2016 when the 26-34 age group had the highest involvement.

Top Vehicle Makes (386 vehicles)

1
FORD77 (19.9%)
↑5.5%prior 73
2
CHEV66 (17.1%)
↑37.5%prior 48
3
CHEVROLET32 (8.3%)
↓-43.9%prior 57
4
TOYOTA16 (4.1%)
→0.0%prior 16
5
DODGE13 (3.4%)
↓-13.3%prior 15
6
KIA12 (3.1%)
↑140.0%prior 5
7
DODG11 (2.8%)
↓-26.7%prior 15
8
HOND11 (2.8%)
↑57.1%prior 7
9
BUIC10 (2.6%)
10
NISSAN9 (2.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (304 persons with recorded sex)

Male176 (57.9%)
↑8.0%prior 163
Female128 (42.1%)
↑1.6%prior 126

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 274
  • Total persons involved: 473
  • Total vehicles involved: 386

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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

ThatCarHitMe.com · An Injuria.ai Company