Yearly Traffic Safety Analysis

2,283 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Black Hawk County, total vehicle crashes decreased by 3.0% from 2,354 in 2015 to 2,283 in 2016. Despite the overall reduction in collisions, the number of traffic fatalities increased from 10 to 11 persons year-over-year. The most notable shift in contributing factors was a 15.3% increase in incidents attributed to 'Followed too close,' which rose from 176 to 203 cases.

2,283

▼ -3.0%was 2,354

Total Crash Events

11

▲ 10.0%was 10

Persons Killed

828

▼ -3.2%was 855

Persons Injured

11

▲ 22.2%was 9

Fatal Crash Events

Note: "Persons Killed" (11) counts individual fatalities across all crash events. "Fatal" in the severity table below (11) 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

Overall, traffic crashes in Black Hawk County saw a slight decline in 2016 compared to the previous year, with total incidents falling by 3.0% from 2,354 to 2,283. This downward trend was also reflected in total injuries, which decreased by 3.2% from 855 to 828. However, the number of fatalities rose by one, from 10 in 2015 to 11 in 2016.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

11

Motorists Killed

Prior: 8↑37.5%

0

Other Killed

Prior: 1↓-100.0%

19

Pedestrians Injured

Prior: 25↓-24.0%

24

Cyclists Injured

Prior: 20↑20.0%

784

Motorists Injured

Prior: 810↓-3.2%

1

Other Injured

Prior: 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 remained largely consistent between the two periods. The afternoon rush hour was the most frequent time for crashes in both years, with 3 p.m. being the peak hour in 2016 (207 crashes) and 2015 (214 crashes). The peak day for crashes shifted slightly from Thursday in 2015 (389 crashes) to Friday in 2016 (393 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 slightly increased in 2016 compared to 2015. The fatal crash rate rose from 0.38 to 0.48, with 11 fatal crashes in 2016 versus 9 in the prior year. The proportion of crashes resulting in serious injuries also increased from 1.8% to 2.0% of all incidents. Consequently, the share of crashes with no reported injuries decreased from 70.8% in 2015 to 69.7% in 2016.

Outcome by Severity (Crash Events)

Fatal11fatal crashes0.5%
↑22.2%prior 9
Serious Injury46serious injury crashes2%
↑9.5%prior 42
Minor Injury219minor injury crashes9.6%
↑4.3%prior 210
Possible Injury416possible injury crashes18.2%
↓-2.6%prior 427
No Injury1,591no injury crashes69.7%
↓-4.5%prior 1,666

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 shifted between 2015 and 2016. In 2016, 'Followed too close' became the most cited factor with 203 incidents, an increase of 27 cases (15.3%) from 176 in 2015 when it was ranked second. Crashes involving an animal also increased in count from 168 to 187, moving into the top three factors. Conversely, crashes attributed to 'FTYROW: From stop sign' decreased from 169 to 157 incidents.

Officer-Reported Primary Contributing Cause

Followed too close203 (8.9%)↑15.3%prior 176
Other (explain in narrative): Other196 (8.6%)↓-14.8%prior 230
Animal187 (8.2%)↑11.3%prior 168
FTYROW: From stop sign157 (6.9%)↓-7.1%prior 169
FTYROW: Making left turn146 (6.4%)↑16.8%prior 125
Ran Traffic Signal129 (5.7%)↑8.4%prior 119
Driving too fast for conditions124 (5.4%)↑3.3%prior 120
Ran off road - left105 (4.6%)↓-16.7%prior 126
Ran Stop Sign93 (4.1%)↓-6.1%prior 99
Lost Control88 (3.9%)→0.0%prior 88

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

Road & Environmental Conditions

The distribution of environmental conditions during crashes was largely stable year-over-year. In both 2016 and 2015, approximately two-thirds of crashes occurred in daylight (66.1% and 66.3%, respectively) and on dry road surfaces (67.2% and 64.9%, respectively). There was a minor decrease in the proportion of crashes on roads affected by snow, ice, or slush, which accounted for 14.3% of crashes in 2016, down from 16.9% in 2015.

Weather

Clear1,391 (65.1%)
↓-4.3%prior 1,454
Cloudy425 (19.9%)
↓-0.9%prior 429
Rain130 (6.1%)
↓-14.5%prior 152
Snow112 (5.2%)
↓-29.6%prior 159
Fog, smoke, smog25 (1.2%)
↑316.7%prior 6
Freezing rain/drizzle24 (1.1%)
↑84.6%prior 13
Blowing Snow23 (1.1%)
↑64.3%prior 14
Sleet, hail6 (0.3%)
Severe Winds1 (0.0%)

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

Lighting

Daylight1,510 (70.5%)
↓-3.2%prior 1,560
Dark - roadway lighted379 (17.7%)
↓-8.0%prior 412
Dark - roadway not lighted159 (7.4%)
↓-8.6%prior 174
Dusk41 (1.9%)
↓-12.8%prior 47
Dawn35 (1.6%)
↑16.7%prior 30
Dark - unknown roadway lighting18 (0.8%)
↑28.6%prior 14

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

Road Surface

Dry1,533 (71.5%)
↑0.4%prior 1,527
Wet270 (12.6%)
↓-9.4%prior 298
Snow173 (8.1%)
↓-29.1%prior 244
Ice/frost121 (5.6%)
↑0.8%prior 120
Slush34 (1.6%)
↑3.0%prior 33
Gravel9 (0.4%)
→0.0%prior 9
Other (explain in narrative)2 (0.1%)
Water (standing or moving)1 (0.0%)

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 and the age distribution of drivers showed little change between 2015 and 2016. Ford and Chevrolet vehicles were the most frequently involved makes in both periods. Similarly, the proportional involvement of different driver age groups, such as the 16-20 and 21-25 age brackets, remained consistent year-over-year.

Top Vehicle Makes (4,152 vehicles)

1
FORD613 (14.8%)
↓-6.0%prior 652
2
CHEV484 (11.7%)
↓-23.1%prior 629
3
CHEVROLET408 (9.8%)
↑42.7%prior 286
4
TOYT158 (3.8%)
↓-23.3%prior 206
5
DODG146 (3.5%)
↓-30.8%prior 211
6
DODGE134 (3.2%)
↑59.5%prior 84
7
TOYOTA130 (3.1%)
↑56.6%prior 83
8
GMC117 (2.8%)
↑17.0%prior 100
9
JEEP105 (2.5%)
↓-7.1%prior 113
10
PONT99 (2.4%)
↓-41.1%prior 168

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

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

Sex Distribution (3,292 persons with recorded sex)

Male1,732 (52.6%)
↓-14.5%prior 2,025
Female1,560 (47.4%)
↓-12.0%prior 1,773

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: 2,283
  • Total persons involved: 4,701
  • Total vehicles involved: 4,152

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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