Yearly Traffic Safety Analysis

227 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Washington County, total traffic crashes decreased from 273 in 2015 to 227 in 2016, a reduction of approximately 16.8%. Despite the overall drop in collisions, the total number of injuries reported rose significantly from 90 to 133, an increase of 47.8%. The number of fatalities remained constant at 3 for both years.

227

▼ -16.8%was 273

Total Crash Events

3

Persons Killed

133

▲ 47.8%was 90

Persons Injured

3

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 · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend for traffic incidents in Washington County was mixed. The total number of crashes declined by 16.8%, from 273 in 2015 to 227 in 2016. In contrast, the number of persons injured in these crashes increased substantially by 47.8%, rising from 90 to 133, while fatalities held steady at 3.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 2↑50.0%

5

Pedestrians Injured

Prior: 1↑400.0%

2

Cyclists Injured

Prior: 1↑100.0%

126

Motorists Injured

Prior: 87↑44.8%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal crash patterns shifted year-over-year. In 2016, the peak day for crashes was Tuesday with 44 incidents, a change from 2015 when Thursday was the peak day with 56 crashes. The busiest hour for collisions also moved earlier, shifting from 5 p.m. in 2015 (24 crashes) to 3 p.m. in 2016 (20 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the number of fatal crashes was unchanged at 3, the fatal crash rate increased from 1.1% in 2015 to 1.3% in 2016 due to the lower overall crash volume. The proportion of crashes resulting in an injury grew significantly, with crashes involving any level of injury accounting for 42.3% of incidents in 2016, up from 27.1% in 2015. This was driven by increases in both serious injury crashes (from 9 to 15) and possible injury crashes (from 36 to 51).

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.3%
→0.0%prior 3
Serious Injury15serious injury crashes6.6%
↑66.7%prior 9
Minor Injury27minor injury crashes11.9%
↑3.8%prior 26
Possible Injury51possible injury crashes22.5%
↑41.7%prior 36
No Injury131no injury crashes57.7%
↓-34.2%prior 199

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an 'Animal' remained the top contributing factor in both periods, but the count of such incidents fell by 43.6%, from 55 in 2015 to 31 in 2016. 'Lost Control' and 'FTYROW: From stop sign' were the second and third most common factors in both years; the former's count decreased slightly from 26 to 24, while the latter held steady at 21 incidents. Notably, the count of crashes attributed to 'Followed too close' doubled, increasing from 9 in 2015 to 18 in 2016.

Officer-Reported Primary Contributing Cause

Animal31 (13.7%)↓-43.6%prior 55
Lost Control24 (10.6%)↓-7.7%prior 26
FTYROW: From stop sign21 (9.3%)→0.0%prior 21
Followed too close18 (7.9%)↑100.0%prior 9
Ran off road - left17 (7.5%)↑41.7%prior 12
Ran off road - straight13 (5.7%)↓-31.6%prior 19
Driver Distraction: Other interior distraction10 (4.4%)↑66.7%prior 6
Ran Stop Sign10 (4.4%)→0.0%prior 10
Other (explain in narrative): Other8 (3.5%)↑33.3%prior 6
Driving too fast for conditions7 (3.1%)↓-65.0%prior 20

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in 2016 were more likely to occur on dry roads compared to the prior year, with the proportion rising from 66.3% to 74.4%. Correspondingly, the number of crashes on adverse surfaces like snow, ice, or wet pavement decreased from 63 incidents in 2015 to 32 in 2016. A similar trend was observed for weather, as crashes during adverse conditions like rain or snow dropped from 41 to 19. The distribution of crashes by lighting conditions was stable, with daylight accounting for just over 60% of incidents in both years.

Weather

Clear166 (76.1%)
↓-0.6%prior 167
Cloudy33 (15.1%)
↓-34.0%prior 50
Snow7 (3.2%)
↓-65.0%prior 20
Freezing rain/drizzle6 (2.8%)
Fog, smoke, smog2 (0.9%)
↓-60.0%prior 5
Rain2 (0.9%)
↓-81.8%prior 11
Severe Winds2 (0.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight139 (63.5%)
↓-14.7%prior 163
Dark - roadway not lighted44 (20.1%)
↓-25.4%prior 59
Dark - roadway lighted19 (8.7%)
↑11.8%prior 17
Dawn10 (4.6%)
↓-9.1%prior 11
Dusk6 (2.7%)
↓-40.0%prior 10
Dark - unknown roadway lighting1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry169 (77.5%)
↓-6.6%prior 181
Gravel15 (6.9%)
↑7.1%prior 14
Wet12 (5.5%)
↓-42.9%prior 21
Ice/frost11 (5.0%)
↓-21.4%prior 14
Snow8 (3.7%)
↓-69.2%prior 26
Other (explain in narrative)2 (0.9%)
Slush1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes were consistent, with Ford, Chevrolet, and Dodge appearing most frequently in both years. Ford's involvement was nearly flat with 65 crashes in 2016 compared to 64 in 2015. Demographics of persons involved in crashes showed a notable shift; the sex distribution moved from a male majority in 2015 (225 males to 152 females) to an almost even split in 2016 (131 males to 134 females). Additionally, the number of involved persons in the 16-20 age group increased from 79 to 86.

Top Vehicle Makes (368 vehicles)

1
FORD65 (17.7%)
↑1.6%prior 64
2
CHEV40 (10.9%)
↓-28.6%prior 56
3
CHEVROLET38 (10.3%)
↓-2.6%prior 39
4
PONTIAC16 (4.3%)
↑128.6%prior 7
5
TOYT15 (4.1%)
↓-6.3%prior 16
6
GMC15 (4.1%)
↓-6.3%prior 16
7
HOND14 (3.8%)
↑100.0%prior 7
8
TOYOTA12 (3.3%)
↓-20.0%prior 15
9
DODGE12 (3.3%)
↓-29.4%prior 17
10
DODG10 (2.7%)
↓-47.4%prior 19

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (265 persons with recorded sex)

Female134 (50.6%)
↓-11.8%prior 152
Male131 (49.4%)
↓-41.8%prior 225

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 227
  • Total persons involved: 441
  • Total vehicles involved: 368

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: 2016." Published September 9, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-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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