Yearly Traffic Safety Analysis

2,149 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Dubuque County, total traffic crashes increased by 4.8% from 2,051 in 2018 to 2,149 in 2019. This period saw a rise in both total injuries, from 621 to 633, and total fatalities, from 6 to 8. The most significant year-over-year change was a 64.7% increase in the number of crashes resulting in serious injuries, which grew from 17 to 28.

2,149

▲ 4.8%was 2,051

Total Crash Events

8

▲ 33.3%was 6

Persons Killed

633

▲ 1.9%was 621

Persons Injured

8

▲ 33.3%was 6

Fatal Crash Events

Note: "Persons Killed" (8) counts individual fatalities across all crash events. "Fatal" in the severity table below (8) 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 traffic safety trends in Dubuque County worsened from 2018 to 2019. The total number of crashes rose by 4.8%, an increase of 98 incidents. This upward trend was also reflected in crash outcomes, with total injuries increasing by 1.9% and fatalities rising by 33.3% year-over-year.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 1↑100.0%

0

Cyclists Killed

Prior: 0→0.0%

6

Motorists Killed

Prior: 5↑20.0%

16

Pedestrians Injured

Prior: 20↓-20.0%

12

Cyclists Injured

Prior: 10↑20.0%

605

Motorists Injured

Prior: 591↑2.4%

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 shifts between the two periods. The peak day for crashes moved from Monday in 2018 (329 crashes) to Thursday in 2019 (377 crashes). While the 5 p.m. hour remained the peak time for collisions in both years, the number of crashes during this hour increased from 192 to 213.

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

Crash severity increased from 2018 to 2019. The number of fatal crashes rose from 6 to 8, and the fatal crash rate increased from 0.29 to 0.37 per 100 crashes. More notably, crashes resulting in a serious injury increased by 64.7%, from 17 incidents in 2018 to 28 in 2019, representing a proportional increase from 0.8% to 1.3% of all crashes.

Outcome by Severity (Crash Events)

Fatal8fatal crashes0.4%
↑33.3%prior 6
Serious Injury28serious injury crashes1.3%
↑64.7%prior 17
Minor Injury165minor injury crashes7.7%
→0.0%prior 165
Possible Injury318possible injury crashes14.8%
↑4.3%prior 305
No Injury1,630no injury crashes75.8%
↑4.6%prior 1,558

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 remained largely consistent, though their counts shifted. "Ran off road - left" was the top factor in both years, increasing by 8.4% from 500 crashes in 2018 to 542 in 2019. Collisions involving animals were the second-most common factor in both periods, though their count decreased from 228 to 215. The count for "Ran Stop Sign" incidents increased from 106 to 117, while "Followed too close" rose from 84 to 111.

Officer-Reported Primary Contributing Cause

Ran off road - left542 (25.2%)↑8.4%prior 500
Animal215 (10%)↓-5.7%prior 228
FTYROW: From stop sign118 (5.5%)↑13.5%prior 104
Ran Stop Sign117 (5.4%)↑10.4%prior 106
Followed too close111 (5.2%)↑32.1%prior 84
Ran Traffic Signal103 (4.8%)→0.0%prior 103
Lost Control102 (4.7%)↑10.9%prior 92
Driving too fast for conditions80 (3.7%)↑12.7%prior 71
Made improper turn70 (3.3%)↓-11.4%prior 79
FTYROW: Making left turn68 (3.2%)↓-16.0%prior 81

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 most crashes in both years occurred in clear weather and on dry roads, there was a significant increase in incidents under adverse conditions. Crashes in snowy weather rose from 96 in 2018 to 163 in 2019. Consequently, the proportion of crashes on roads with snow, ice, or slush increased from 9.6% of all crashes in 2018 to 15.4% in 2019.

Weather

Clear1,069 (54.3%)
↑3.2%prior 1,036
Cloudy548 (27.8%)
↑5.0%prior 522
Snow163 (8.3%)
↑69.8%prior 96
Rain108 (5.5%)
↓-21.7%prior 138
Freezing rain/drizzle46 (2.3%)
↑17.9%prior 39
Fog, smoke, smog16 (0.8%)
↑33.3%prior 12
Blowing Snow14 (0.7%)
↑75.0%prior 8
Sleet, hail3 (0.2%)
↓-72.7%prior 11
Severe Winds1 (0.1%)

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

Lighting

Daylight1,434 (72.8%)
↑9.0%prior 1,315
Dark - roadway lighted295 (15.0%)
↑0.3%prior 294
Dark - roadway not lighted138 (7.0%)
↓-18.3%prior 169
Dusk58 (2.9%)
↑9.4%prior 53
Dawn42 (2.1%)
↑82.6%prior 23
Dark - unknown roadway lighting3 (0.2%)
↓-75.0%prior 12

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

Road Surface

Dry1,334 (67.8%)
↓-2.5%prior 1,368
Wet294 (14.9%)
↑1.4%prior 290
Snow159 (8.1%)
↑67.4%prior 95
Ice/frost128 (6.5%)
↑106.5%prior 62
Slush43 (2.2%)
↑7.5%prior 40
Mud, dirt4 (0.2%)
Gravel4 (0.2%)
↓-55.6%prior 9
Other (explain in narrative)3 (0.2%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, Ford and Chevrolet, maintained their top rankings with slight increases in counts year-over-year. The number of Toyotas involved in collisions saw a more notable increase from 246 to 316. In terms of driver demographics, the 26-34 age group's involvement in crashes grew, accounting for 16.1% of all persons in 2019 compared to 14.8% in 2018.

Top Vehicle Makes (3,837 vehicles)

1
FORD558 (14.5%)
↑2.6%prior 544
2
CHEV511 (13.3%)
↑0.2%prior 510
3
CHEVROLET313 (8.2%)
↑4.7%prior 299
4
TOYT194 (5.1%)
↑22.0%prior 159
5
JEEP173 (4.5%)
↑9.5%prior 158
6
DODG154 (4%)
↑20.3%prior 128
7
HOND150 (3.9%)
↑7.1%prior 140
8
GMC137 (3.6%)
↑14.2%prior 120
9
TOYOTA122 (3.2%)
↑40.2%prior 87
10
KIA101 (2.6%)
↓-10.6%prior 113

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

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

Sex Distribution (3,499 persons with recorded sex)

Male1,942 (55.5%)
↑22.4%prior 1,587
Female1,557 (44.5%)
↑17.4%prior 1,326

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: 2,149
  • Total persons involved: 4,921
  • Total vehicles involved: 3,837

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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