Yearly Traffic Safety Analysis

217 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Washington County, total crashes decreased by 11.4% from 245 in 2018 to 217 in 2019. Despite the overall decline in collisions and a 26.7% drop in injuries from 101 to 74, the number of fatalities increased significantly, rising from one in 2018 to four in 2019.

217

▼ -11.4%was 245

Total Crash Events

4

▲ 300.0%was 1

Persons Killed

74

▼ -26.7%was 101

Persons Injured

3

▲ 200.0%was 1

Fatal Crash Events

Note: "Persons Killed" (4) 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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic collisions in Washington County showed a downward trend from 2018 to 2019, with total crashes falling from 245 to 217. The number of people injured also decreased from 101 to 74. In contrast to these trends, the number of fatalities recorded during this period increased from one to four.

Vulnerable Road User Casualties

4

Motorists Killed

Prior: 1↑300.0%

74

Motorists Injured

Prior: 97↓-23.7%

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 shifted between the two periods. In 2019, the peak day for crashes was Monday with 41 incidents, a change from 2018 when Friday was the peak day with 44 crashes. Similarly, the peak hour for collisions moved from the 5 p.m. hour in 2018 (21 crashes) to the 7 a.m. hour in 2019 (22 crashes), suggesting a change in concentration from the evening to the morning commute.

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

While total crashes declined, the severity of outcomes worsened in some respects. The number of fatal crashes increased from one in 2018 to three in 2019, with the share of fatal crashes rising from 0.4% to 1.4% of all incidents. Conversely, crashes resulting in serious injuries decreased, with the count falling from 10 to 4 and their share of all crashes dropping from 4.1% to 1.8%. The proportion of non-injury crashes increased from 66.1% in 2018 to 68.7% in 2019.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.4%
↑200.0%prior 1
Serious Injury4serious injury crashes1.8%
↓-60.0%prior 10
Minor Injury24minor injury crashes11.1%
↓-11.1%prior 27
Possible Injury37possible injury crashes17.1%
↓-17.8%prior 45
No Injury149no injury crashes68.7%
↓-8.0%prior 162

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

Collisions involving an animal remained the top contributing factor in both years, though the count of such incidents decreased by 38.5% from 39 in 2018 to 24 in 2019. The factor 'Ran off road - left' saw its count double from 10 to 20 incidents, becoming the second-leading cause in 2019. Crashes attributed to 'Lost Control' also increased in count from 13 to 18. Meanwhile, incidents where a driver 'Followed too close' decreased from 21 to 15.

Officer-Reported Primary Contributing Cause

Animal24 (11.1%)↓-38.5%prior 39
Ran off road - left20 (9.2%)↑100.0%prior 10
FTYROW: From stop sign19 (8.8%)→0.0%prior 19
Lost Control18 (8.3%)↑38.5%prior 13
Followed too close15 (6.9%)↓-28.6%prior 21
Other (explain in narrative): Other15 (6.9%)↓-6.3%prior 16
Driving too fast for conditions13 (6%)→0.0%prior 13
FTYROW: Making left turn12 (5.5%)↑71.4%prior 7
Ran off road - straight9 (4.1%)↓-35.7%prior 14
Driver Distraction: Other interior distraction7 (3.2%)↓-22.2%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

The conditions under which crashes occurred saw some year-over-year changes. Crashes on roads with ice or frost more than doubled in count, increasing from 10 incidents in 2018 to 21 in 2019, raising their share of all crashes from 4.1% to 9.7%. In contrast, the number of crashes occurring in darkness on unlit roadways decreased from 52 to 29. The proportion of crashes on dry roads remained relatively stable, accounting for 64.5% of crashes in 2019 compared to 66.5% in 2018.

Weather

Clear131 (63.6%)
↓-18.1%prior 160
Cloudy41 (19.9%)
↑5.1%prior 39
Rain12 (5.8%)
↑20.0%prior 10
Blowing Snow7 (3.4%)
Freezing rain/drizzle7 (3.4%)
→0.0%prior 7
Snow7 (3.4%)
↓-41.7%prior 12
Fog, smoke, smog1 (0.5%)
↓-83.3%prior 6

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

Lighting

Daylight151 (72.6%)
↓-1.9%prior 154
Dark - roadway not lighted29 (13.9%)
↓-44.2%prior 52
Dark - roadway lighted13 (6.3%)
↓-31.6%prior 19
Dawn11 (5.3%)
↑57.1%prior 7
Dark - unknown roadway lighting2 (1.0%)
Dusk2 (1.0%)
↓-60.0%prior 5

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

Road Surface

Dry140 (67.3%)
↓-14.1%prior 163
Wet21 (10.1%)
↓-25.0%prior 28
Ice/frost21 (10.1%)
↑110.0%prior 10
Snow14 (6.7%)
↓-22.2%prior 18
Gravel8 (3.8%)
↓-33.3%prior 12
Slush4 (1.9%)
↓-50.0%prior 8

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

Vehicles & Demographics

An analysis of the persons and vehicles involved in crashes reveals demographic and make-related shifts. The number of persons aged 16-20 involved in crashes decreased from 63 in 2018 to 42 in 2019. In contrast, involvement for the 35-44 age group increased from 56 to 74. Chevrolet and Ford remained the two most frequently involved vehicle makes in both years, though the count of Chevrolets in crashes fell from 98 to 75. Toyota vehicles saw an increase in involvement from 27 to 31.

Top Vehicle Makes (352 vehicles)

1
FORD56 (15.9%)
↑1.8%prior 55
2
CHEV55 (15.6%)
↓-12.7%prior 63
3
CHEVROLET20 (5.7%)
↓-42.9%prior 35
4
CHRY14 (4%)
↑27.3%prior 11
5
DODG13 (3.7%)
↓-31.6%prior 19
6
TOYT12 (3.4%)
↓-14.3%prior 14
7
GMC11 (3.1%)
↓-26.7%prior 15
8
BUIC10 (2.8%)
↑11.1%prior 9
9
DODGE10 (2.8%)
→0.0%prior 10
10
TOYOTA10 (2.8%)
↑42.9%prior 7

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

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

Sex Distribution (326 persons with recorded sex)

Male184 (56.4%)
↑9.5%prior 168
Female142 (43.6%)
↓-7.2%prior 153

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: 217
  • Total persons involved: 463
  • Total vehicles involved: 352

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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