Yearly Traffic Safety Analysis

1,971 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Dubuque County recorded 1,971 vehicle crashes, a 2.1% increase from the 1,931 crashes reported in 2015. While overall crash volume saw a slight rise, the most notable changes were an increase in crashes resulting in serious injuries, which rose from 16 to 27, and a rise in incidents involving driving under the influence, which increased from 58 to 80.

1,971

▲ 2.1%was 1,931

Total Crash Events

4

▲ 33.3%was 3

Persons Killed

604

▲ 2.0%was 592

Persons Injured

4

▲ 33.3%was 3

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

Trend Summary

The overall trend in traffic crashes in Dubuque County shows a slight increase between 2015 and 2016. The total number of crashes rose by 40 incidents, from 1,931 in 2015 to 1,971 in 2016, representing a 2.1% year-over-year increase. Total injuries also saw a slight increase from 592 to 604.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 2↑100.0%

0

Other Killed

Prior: 0→0.0%

19

Pedestrians Injured

Prior: 15↑26.7%

10

Cyclists Injured

Prior: 10→0.0%

572

Motorists Injured

Prior: 566↑1.1%

3

Other Injured

Prior: 1↑200.0%

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

The temporal patterns of crashes showed a notable shift in the peak hour of activity between the two periods. While Friday remained the busiest day for crashes in both 2015 (372 crashes) and 2016 (347 crashes), the peak hour for incidents moved from midday to the evening commute. In 2016, the 5 p.m. hour saw the most crashes with 179 incidents, a change from 2015 when the 12 p.m. hour was the peak with 157 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

The severity of crashes worsened in 2016 compared to the prior year. The number of fatal crashes increased from 3 to 4, and the total number of fatalities rose from 3 to 4. Crashes resulting in serious injuries saw a substantial increase, jumping from 16 incidents in 2015 to 27 in 2016. The count of minor injury crashes also rose from 132 to 154, while possible injury crashes saw a slight decrease.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.2%
↑33.3%prior 3
Serious Injury27serious injury crashes1.4%
↑68.8%prior 16
Minor Injury154minor injury crashes7.8%
↑16.7%prior 132
Possible Injury307possible injury crashes15.6%
↓-1.6%prior 312
No Injury1,479no injury crashes75%
↑0.7%prior 1,468

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

The leading contributing factors for crashes in Dubuque County remained largely consistent, though some counts shifted significantly. 'Ran off road - left' was the top factor in both years, increasing in count from 440 incidents in 2015 to 487 in 2016. Crashes involving animals, the second-ranked factor, decreased from 204 to 188. Notably, incidents attributed to 'Failure to yield right of way from a stop sign' rose from a count of 91 to 120, becoming the third most common factor in 2016.

Officer-Reported Primary Contributing Cause

Ran off road - left487 (24.7%)↑10.7%prior 440
Animal188 (9.5%)↓-7.8%prior 204
FTYROW: From stop sign120 (6.1%)↑31.9%prior 91
Lost Control117 (5.9%)↓-4.9%prior 123
Ran Traffic Signal102 (5.2%)↓-2.9%prior 105
Ran Stop Sign100 (5.1%)↑13.6%prior 88
Followed too close91 (4.6%)↑33.8%prior 68
FTYROW: Making left turn84 (4.3%)↑6.3%prior 79
Made improper turn59 (3%)↓-4.8%prior 62
Driving too fast for conditions55 (2.8%)↓-9.8%prior 61

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

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with the majority of incidents in both periods occurring in daylight and under clear weather. There was a notable decrease in crashes on adverse road surfaces, with the combined count of incidents on snowy, icy, or slushy roads falling from 231 in 2015 to 185 in 2016. Correspondingly, crashes on dry road surfaces increased from a count of 1,258 to 1,365.

Weather

Clear988 (54.6%)
↓-1.9%prior 1,007
Cloudy529 (29.2%)
↑12.8%prior 469
Snow126 (7.0%)
↑5.9%prior 119
Rain123 (6.8%)
↓-8.2%prior 134
Freezing rain/drizzle19 (1.0%)
↑26.7%prior 15
Fog, smoke, smog11 (0.6%)
↑22.2%prior 9
Sleet, hail5 (0.3%)
Blowing Snow4 (0.2%)
Other (explain in narrative)2 (0.1%)
Severe Winds2 (0.1%)

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

Lighting

Daylight1,282 (70.6%)
↑0.8%prior 1,272
Dark - roadway lighted298 (16.4%)
↑3.5%prior 288
Dark - roadway not lighted145 (8.0%)
↑15.1%prior 126
Dusk45 (2.5%)
↓-13.5%prior 52
Dawn42 (2.3%)
↑90.9%prior 22
Dark - unknown roadway lighting4 (0.2%)

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

Road Surface

Dry1,365 (75.3%)
↑8.5%prior 1,258
Wet252 (13.9%)
↓-3.8%prior 262
Snow119 (6.6%)
↓-20.7%prior 150
Ice/frost40 (2.2%)
↓-7.0%prior 43
Slush26 (1.4%)
↓-31.6%prior 38
Gravel11 (0.6%)
↑37.5%prior 8

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Ford and Chevrolet models being the most common in both 2015 and 2016. An analysis of persons involved reveals a notable shift in age demographics, with the number of individuals in the 26-34 age group decreasing from 637 in 2015 to 526 in 2016. The 16-20 age group remained one of the most represented, with 538 individuals involved in 2016 compared to 548 in the prior year.

Top Vehicle Makes (3,539 vehicles)

1
FORD472 (13.3%)
↑7.8%prior 438
2
CHEV437 (12.3%)
↓-11.0%prior 491
3
CHEVROLET359 (10.1%)
↑15.8%prior 310
4
TOYT136 (3.8%)
↓-14.5%prior 159
5
JEEP135 (3.8%)
↓-10.6%prior 151
6
DODG119 (3.4%)
↓-13.1%prior 137
7
DODGE118 (3.3%)
↑19.2%prior 99
8
HOND117 (3.3%)
↓-5.6%prior 124
9
TOYOTA116 (3.3%)
↑20.8%prior 96
10
HONDA92 (2.6%)
↑29.6%prior 71

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

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

Sex Distribution (2,718 persons with recorded sex)

Male1,519 (55.9%)
↓-7.3%prior 1,638
Female1,199 (44.1%)
↓-11.6%prior 1,357

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: 1,971
  • Total persons involved: 3,920
  • Total vehicles involved: 3,539

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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