Yearly Traffic Safety Analysis

328 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Benton County recorded 328 total crashes, an increase of 6.2% from the 309 crashes reported in 2018. Despite the rise in total incidents, the number of fatalities decreased from 7 in 2018 to 4 in 2019. Similarly, crashes involving a fatality dropped from 6 to 4 over the same period.

328

▲ 6.1%was 309

Total Crash Events

4

▼ -42.9%was 7

Persons Killed

114

▼ -8.1%was 124

Persons Injured

4

▼ -33.3%was 6

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash volume in Benton County saw an increase from 309 incidents in 2018 to 328 in 2019, representing a 6.2% rise. However, the outcomes of these crashes became less severe on average, with total injuries declining from 124 to 114 and total fatalities falling from 7 to 4 year-over-year.

Vulnerable Road User Casualties

4

Motorists Killed

Prior: 7↓-42.9%

114

Motorists Injured

Prior: 122↓-6.6%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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 some consistency and some shifts between 2018 and 2019. Friday remained the peak day for crashes in both years, with 60 incidents in 2018 and 53 in 2019. The peak hour for crashes shifted slightly later in the evening, from the 5 p.m. hour in 2018 (30 crashes) to the 6 p.m. hour in 2019 (28 crashes). Notably, the number of crashes occurring on Saturdays increased from 28 in 2018 to 51 in 2019.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes decreased from 2018 to 2019. The number of fatal crashes fell from 6 to 4, and their proportion of all crashes dropped from 1.9% to 1.2%. Serious injury crashes also decreased in both count, from 16 to 12, and as a share of total crashes, from 5.2% to 3.7%. In contrast, minor injury crashes saw an increase in count from 33 to 44.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.2%
↓-33.3%prior 6
Serious Injury12serious injury crashes3.7%
↓-25.0%prior 16
Minor Injury44minor injury crashes13.4%
↑33.3%prior 33
Possible Injury41possible injury crashes12.5%
→0.0%prior 41
No Injury227no injury crashes69.2%
↑6.6%prior 213

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes remained consistent, though some counts shifted. Collisions involving an 'Animal' continued to be the top factor in both periods, with counts of 81 in 2018 and 78 in 2019. The second-ranked factor, 'Lost Control,' saw its count increase from 38 to 47. Crashes attributed to 'Ran off road - straight' also rose from 23 to 31. Conversely, crashes where 'Failure to Yield Right of Way from a stop sign' was a factor decreased from 23 in 2018 to 17 in 2019.

Officer-Reported Primary Contributing Cause

Animal78 (23.8%)↓-3.7%prior 81
Lost Control47 (14.3%)↑23.7%prior 38
Ran off road - straight31 (9.5%)↑34.8%prior 23
Driving too fast for conditions22 (6.7%)↑4.8%prior 21
FTYROW: From stop sign17 (5.2%)↓-26.1%prior 23
Followed too close17 (5.2%)↑30.8%prior 13
Ran Stop Sign11 (3.4%)↓-21.4%prior 14
Made improper turn11 (3.4%)
Driver Distraction: Other interior distraction8 (2.4%)↑14.3%prior 7
Ran off road - left8 (2.4%)↓-11.1%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While the majority of crashes in both years occurred in clear weather and on dry roads, there was a notable increase in crashes reported under adverse winter conditions in 2019. Crashes on roads with ice or frost more than doubled, increasing from 17 in 2018 to 43 in 2019. Similarly, crashes during snowy weather rose from 16 to 23, and incidents where blowing snow was a factor increased from 1 to 11.

Weather

Clear151 (58.1%)
↑1.3%prior 149
Cloudy50 (19.2%)
↑22.0%prior 41
Snow23 (8.8%)
↑43.8%prior 16
Blowing Snow11 (4.2%)
Freezing rain/drizzle9 (3.5%)
↓-10.0%prior 10
Rain8 (3.1%)
↓-33.3%prior 12
Fog, smoke, smog4 (1.5%)
Severe Winds3 (1.2%)
Other (explain in narrative)1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight172 (64.9%)
↑8.9%prior 158
Dark - roadway not lighted59 (22.3%)
↑22.9%prior 48
Dark - roadway lighted18 (6.8%)
↑20.0%prior 15
Dawn8 (3.0%)
↓-11.1%prior 9
Dusk6 (2.3%)
Dark - unknown roadway lighting2 (0.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry148 (56.3%)
↓-3.9%prior 154
Ice/frost43 (16.3%)
↑152.9%prior 17
Wet27 (10.3%)
↑8.0%prior 25
Snow27 (10.3%)
↑42.1%prior 19
Gravel14 (5.3%)
↓-6.7%prior 15
Mud, dirt2 (0.8%)
Sand1 (0.4%)
Slush1 (0.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Chevrolet (108 vehicles) and Ford (90 vehicles) being the most common in 2019, up from 88 and 79 vehicles respectively in 2018. An analysis of persons involved in crashes shows a shift in age demographics. The number of individuals in the 35-44 age group increased from 69 to 105, and the 45-54 age group grew from 69 to 94. Conversely, the number of individuals aged 65 and older involved in crashes decreased from 75 in 2018 to 69 in 2019.

Top Vehicle Makes (477 vehicles)

1
FORD90 (18.9%)
↑13.9%prior 79
2
CHEV79 (16.6%)
↑14.5%prior 69
3
CHEVROLET29 (6.1%)
↑52.6%prior 19
4
DODG22 (4.6%)
↑37.5%prior 16
5
TOYT20 (4.2%)
↑11.1%prior 18
6
PONT16 (3.4%)
↑33.3%prior 12
7
GMC15 (3.1%)
↑15.4%prior 13
8
DODGE13 (2.7%)
↑62.5%prior 8
9
JEEP12 (2.5%)
↓-25.0%prior 16
10
BUIC12 (2.5%)
↓-33.3%prior 18

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

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

Sex Distribution (444 persons with recorded sex)

Male277 (62.4%)
↑48.9%prior 186
Female167 (37.6%)
↑21.0%prior 138

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 328
  • Total persons involved: 688
  • Total vehicles involved: 477

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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