Yearly Traffic Safety Analysis

1,962 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Dubuque County recorded 1,962 total crashes, a slight decrease of 0.5% from the 1,971 crashes reported in 2016. While the overall crash volume remained stable, the number of fatalities increased significantly, rising from 4 in 2016 to 10 in 2017. This represents a 150% year-over-year increase in traffic-related deaths.

1,962

▼ -0.5%was 1,971

Total Crash Events

10

▲ 150.0%was 4

Persons Killed

561

▼ -7.1%was 604

Persons Injured

10

▲ 150.0%was 4

Fatal Crash Events

Note: "Persons Killed" (10) counts individual fatalities across all crash events. "Fatal" in the severity table below (10) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crash volume in Dubuque County remained relatively stable, with a minor 0.5% decrease from 1,971 incidents in 2016 to 1,962 in 2017. Despite the stable crash numbers, outcomes worsened, as total fatalities increased by 150% from 4 to 10. Conversely, the number of injuries reported saw a 7.1% decline, falling from 604 to 561.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

9

Motorists Killed

Prior: 4↑125.0%

19

Pedestrians Injured

Prior: 19→0.0%

11

Cyclists Injured

Prior: 10↑10.0%

531

Motorists Injured

Prior: 572↓-7.2%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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 2016 and 2017. While Friday remained the peak day for crashes in both years, with counts increasing from 347 to 356, the peak hour for incidents changed. In 2017, the most crashes occurred during the 3 p.m. hour with 202 incidents, shifting from the 5 p.m. peak observed in 2016, which had 179 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes shifted notably in 2017 compared to the prior year. The number of fatal crashes increased from 4 to 10, and their share of all crashes rose from 0.2% to 0.5%. In contrast, all categories of injury-related crashes saw a decrease in both count and proportion; for example, serious injury crashes fell from 27 (1.4% of total) in 2016 to 18 (0.9% of total) in 2017. The proportion of crashes resulting in no injury increased from 75.0% to 76.9%.

Outcome by Severity (Crash Events)

Fatal10fatal crashes0.5%
↑150.0%prior 4
Serious Injury18serious injury crashes0.9%
↓-33.3%prior 27
Minor Injury141minor injury crashes7.2%
↓-8.4%prior 154
Possible Injury285possible injury crashes14.5%
↓-7.2%prior 307
No Injury1,508no injury crashes76.9%
↑2.0%prior 1,479

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes saw some shifts between 2016 and 2017. 'Ran off road - left' remained the top factor in both periods, though its count decreased from 487 crashes in 2016 to 427 in 2017. Crashes attributed to 'Ran Stop Sign' increased from 100 to 112, moving it into the top three factors for 2017. Conversely, 'FTYROW: From stop sign' saw a notable decrease in count, falling from 120 incidents in 2016 to 95 in 2017.

Officer-Reported Primary Contributing Cause

Ran off road - left427 (21.8%)↓-12.3%prior 487
Animal191 (9.7%)↑1.6%prior 188
Ran Stop Sign112 (5.7%)↑12.0%prior 100
Lost Control105 (5.4%)↓-10.3%prior 117
Ran Traffic Signal100 (5.1%)↓-2.0%prior 102
Followed too close97 (4.9%)↑6.6%prior 91
FTYROW: From stop sign95 (4.8%)↓-20.8%prior 120
FTYROW: Making left turn81 (4.1%)↓-3.6%prior 84
Made improper turn73 (3.7%)↑23.7%prior 59
Driver Distraction: Other interior distraction53 (2.7%)↑43.2%prior 37

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions remained largely consistent between 2016 and 2017. The majority of incidents in both years occurred in clear weather and during daylight hours, with proportions changing by less than two percentage points. For instance, crashes in daylight constituted 65.1% of the total in 2017, nearly identical to the 65.0% in 2016. Crashes on dry road surfaces were slightly more common in 2017, accounting for 72.3% of incidents compared to 69.2% in the previous year.

Weather

Clear1,017 (56.2%)
↑2.9%prior 988
Cloudy514 (28.4%)
↓-2.8%prior 529
Rain133 (7.3%)
↑8.1%prior 123
Snow104 (5.7%)
↓-17.5%prior 126
Freezing rain/drizzle23 (1.3%)
↑21.1%prior 19
Fog, smoke, smog10 (0.6%)
↓-9.1%prior 11
Sleet, hail5 (0.3%)
→0.0%prior 5
Other (explain in narrative)2 (0.1%)
Blowing Snow2 (0.1%)
Severe Winds1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight1,277 (70.4%)
↓-0.4%prior 1,282
Dark - roadway lighted314 (17.3%)
↑5.4%prior 298
Dark - roadway not lighted148 (8.2%)
↑2.1%prior 145
Dusk45 (2.5%)
→0.0%prior 45
Dawn27 (1.5%)
↓-35.7%prior 42
Dark - unknown roadway lighting2 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry1,418 (78.2%)
↑3.9%prior 1,365
Wet244 (13.5%)
↓-3.2%prior 252
Snow75 (4.1%)
↓-37.0%prior 119
Ice/frost49 (2.7%)
↑22.5%prior 40
Slush19 (1.0%)
↓-26.9%prior 26
Gravel6 (0.3%)
↓-45.5%prior 11
Sand1 (0.1%)
Water (standing or moving)1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The demographics of vehicles and persons involved in crashes showed minor changes year-over-year. Ford and Chevrolet remained the top two vehicle makes involved in incidents, with the count of Chevrolets decreasing from 796 to 773 while Fords increased from 472 to 499. The age distribution of individuals in crashes was also stable, with the 16-20 and 26-34 age groups representing the largest shares in both years. The representation of these two groups saw a slight increase in their combined share of total persons involved.

Top Vehicle Makes (3,530 vehicles)

1
FORD499 (14.1%)
↑5.7%prior 472
2
CHEV487 (13.8%)
↑11.4%prior 437
3
CHEVROLET286 (8.1%)
↓-20.3%prior 359
4
JEEP154 (4.4%)
↑14.1%prior 135
5
TOYT141 (4%)
↑3.7%prior 136
6
DODG139 (3.9%)
↑16.8%prior 119
7
HOND130 (3.7%)
↑11.1%prior 117
8
TOYOTA109 (3.1%)
↓-6.0%prior 116
9
DODGE108 (3.1%)
↓-8.5%prior 118
10
GMC94 (2.7%)
↑5.6%prior 89

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (2,594 persons with recorded sex)

Male1,400 (54.0%)
↓-7.8%prior 1,519
Female1,194 (46.0%)
↓-0.4%prior 1,199

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 1,962
  • Total persons involved: 3,967
  • Total vehicles involved: 3,530

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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