Yearly Traffic Safety Analysis

2,225 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Black Hawk County, total traffic crashes increased by 3.1%, from 2,159 in 2017 to 2,225 in 2018. This period saw a rise in total fatalities from 8 to 10, a 25% increase. In contrast, the total number of injuries decreased by 4.7% from 787 to 750, highlighting a divergent trend in crash outcomes.

2,225

▲ 3.1%was 2,159

Total Crash Events

10

▲ 25.0%was 8

Persons Killed

750

▼ -4.7%was 787

Persons Injured

7

▼ -12.5%was 8

Fatal Crash Events

Note: "Persons Killed" (10) counts individual fatalities across all crash events. "Fatal" in the severity table below (7) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic collisions in Black Hawk County trended slightly upward, with a 3.1% year-over-year increase from 2,159 to 2,225 crashes. This increase was accompanied by a 25% rise in fatalities, from 8 to 10 deaths. However, the number of individuals injured in these incidents declined by 4.7%, from 787 in 2017 to 750 in 2018.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 2→0.0%

2

Cyclists Killed

Prior: 0→%

6

Motorists Killed

Prior: 6→0.0%

0

Other Killed

Prior: 0→0.0%

15

Pedestrians Injured

Prior: 17↓-11.8%

20

Cyclists Injured

Prior: 20→0.0%

712

Motorists Injured

Prior: 750↓-5.1%

3

Other Injured

Prior: 0→%

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak hour for crashes remained the 3 p.m. hour in both periods, with incident counts in that hour increasing from 184 in 2017 to 207 in 2018. A notable shift occurred in the peak day of the week for crashes, which moved from Friday (397 crashes) in 2017 to Monday (368 crashes) in 2018.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes shifted between the two years, with the proportion of no-injury crashes increasing from 70.0% to 72.6% of all incidents. Crashes resulting in minor or possible injuries saw a decrease in both their total count and their share of all crashes. While the number of fatal crash events decreased from 8 to 7, the total number of people killed in crashes increased from 8 to 10.

Severity is per crash event (most severe injury). 7 fatal crash events resulted in 10 persons killed.

Outcome by Severity (Crash Events)

Fatal7fatal crashes0.3%
↓-12.5%prior 8
Serious Injury32serious injury crashes1.4%
↑10.3%prior 29
Minor Injury181minor injury crashes8.1%
↓-10.0%prior 201
Possible Injury390possible injury crashes17.5%
↓-4.6%prior 409
No Injury1,615no injury crashes72.6%
↑6.8%prior 1,512

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factors were largely consistent, but their counts shifted year-over-year. Crashes attributed to "Driving too fast for conditions" saw a significant 54.9% increase in count, rising from 82 in 2017 to 127 in 2018. In contrast, crashes due to "Followed too close" decreased in count from 204 to 189, and those from "Ran Traffic Signal" dropped from 129 to 113.

Officer-Reported Primary Contributing Cause

Other (explain in narrative): Other206 (9.3%)↓-1.0%prior 208
Followed too close189 (8.5%)↓-7.4%prior 204
Animal182 (8.2%)↑4.0%prior 175
FTYROW: From stop sign156 (7%)↑9.9%prior 142
Ran off road - left132 (5.9%)↑18.9%prior 111
Driving too fast for conditions127 (5.7%)↑54.9%prior 82
Ran Traffic Signal113 (5.1%)↓-12.4%prior 129
FTYROW: Making left turn98 (4.4%)↓-16.2%prior 117
Lost Control92 (4.1%)↓-2.1%prior 94
Ran Stop Sign91 (4.1%)↑2.2%prior 89

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

There was a marked increase in crashes occurring under adverse road conditions. The number of crashes on roads with snow, ice, or slush grew from 442 in 2017 to 613 in 2018, raising their share of total crashes from 20.5% to 27.5%. In contrast, the proportion of crashes occurring in daylight versus dark conditions remained relatively stable year-over-year.

Weather

Clear1,345 (64.6%)
↓-0.7%prior 1,354
Cloudy400 (19.2%)
↓-1.0%prior 404
Rain123 (5.9%)
↓-4.7%prior 129
Snow122 (5.9%)
↑35.6%prior 90
Freezing rain/drizzle61 (2.9%)
↑103.3%prior 30
Blowing Snow11 (0.5%)
↓-38.9%prior 18
Fog, smoke, smog10 (0.5%)
↓-33.3%prior 15
Sleet, hail7 (0.3%)
Severe Winds1 (0.0%)
Other (explain in narrative)1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Weather condition at time of crash

Lighting

Daylight1,432 (68.6%)
↑1.5%prior 1,411
Dark - roadway lighted368 (17.6%)
↓-4.2%prior 384
Dark - roadway not lighted191 (9.2%)
↑8.5%prior 176
Dusk40 (1.9%)
↑5.3%prior 38
Dawn35 (1.7%)
↑9.4%prior 32
Dark - unknown roadway lighting20 (1.0%)
↑81.8%prior 11

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Lighting condition field

Road Surface

Dry1,453 (69.9%)
↓-8.2%prior 1,583
Wet279 (13.4%)
↑6.1%prior 263
Snow175 (8.4%)
↑54.9%prior 113
Ice/frost122 (5.9%)
↑114.0%prior 57
Slush37 (1.8%)
↑311.1%prior 9
Gravel13 (0.6%)
↑8.3%prior 12
Water (standing or moving)1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes, Ford and Chevrolet, maintained their top rankings in both years. A significant demographic shift was observed in the age of persons involved in crashes. The number of individuals in the 55-64 age group increased from 421 to 495, while the 65+ age group grew from 386 to 496. Conversely, involvement for the 45-54 age group decreased from 523 to 468 persons.

Top Vehicle Makes (4,002 vehicles)

1
FORD659 (16.5%)
↑10.8%prior 595
2
CHEV654 (16.3%)
↑14.1%prior 573
3
CHEVROLET208 (5.2%)
↓-16.5%prior 249
4
TOYT206 (5.1%)
↓-4.2%prior 215
5
DODG189 (4.7%)
↑13.2%prior 167
6
PONT114 (2.8%)
↓-5.8%prior 121
7
CHRY111 (2.8%)
↑1.8%prior 109
8
NISS109 (2.7%)
↑12.4%prior 97
9
JEEP106 (2.6%)
↓-13.8%prior 123
10
GMC105 (2.6%)
→0.0%prior 105

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records

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

Sex Distribution (3,294 persons with recorded sex)

Male1,789 (54.3%)
↑6.9%prior 1,674
Female1,505 (45.7%)
↑5.6%prior 1,425

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 2,225
  • Total persons involved: 4,760
  • Total vehicles involved: 4,002

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: 2018." Published September 9, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2018-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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