Yearly Traffic Safety Analysis

2,601 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Johnson County recorded 2,601 total crashes, an increase of 9.3% from the 2,380 crashes reported in 2015. The most significant year-over-year change was a substantial rise in traffic fatalities, which increased from 5 in 2015 to 16 in 2016. Total injuries also saw a slight increase from 730 to 772.

2,601

▲ 9.3%was 2,380

Total Crash Events

16

▲ 220.0%was 5

Persons Killed

772

▲ 5.8%was 730

Persons Injured

14

▲ 180.0%was 5

Fatal Crash Events

Note: "Persons Killed" (16) counts individual fatalities across all crash events. "Fatal" in the severity table below (14) 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 Johnson County showed an upward trend from 2015 to 2016. The total number of crashes increased by 9.3%, from 2,380 to 2,601. This increase was accompanied by a 5.8% rise in total injuries and a 220% increase in fatalities.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 1↑100.0%

1

Cyclists Killed

Prior: 0→%

13

Motorists Killed

Prior: 4↑225.0%

0

Other Killed

Prior: 0→0.0%

36

Pedestrians Injured

Prior: 32↑12.5%

47

Cyclists Injured

Prior: 48↓-2.1%

687

Motorists Injured

Prior: 647↑6.2%

2

Other Injured

Prior: 3↓-33.3%

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 2015 and 2016. Friday continued to be the peak day for crashes, with the total count on this day increasing from 412 to 458. The 5 p.m. hour was the peak hour in both years, although the number of crashes during this hour decreased slightly from 265 in 2015 to 253 in 2016.

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

Crash severity worsened significantly in 2016 compared to the previous year. The number of fatal crashes nearly tripled, increasing from 5 to 14, and the corresponding fatal crash rate rose from 0.21% to 0.54% of all crashes. While the absolute count of serious injury crashes remained unchanged at 45, the proportions of all injury-related crashes remained relatively stable as a percentage of the total.

Severity is per crash event (most severe injury). 14 fatal crash events resulted in 16 persons killed.

Outcome by Severity (Crash Events)

Fatal14fatal crashes0.5%
↑180.0%prior 5
Serious Injury45serious injury crashes1.7%
→0.0%prior 45
Minor Injury220minor injury crashes8.5%
↑8.4%prior 203
Possible Injury394possible injury crashes15.1%
↑11.3%prior 354
No Injury1,928no injury crashes74.1%
↑8.7%prior 1,773

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 were consistent year-over-year, with 'Followed too close' remaining the top cause in both 2015 and 2016. Crashes attributed to this factor increased by 18.6% in count, from 494 to 586. While 'Driving too fast for conditions' remained a top-three factor, its count decreased by 9.2% from 196 to 178. Notably, crashes involving running a traffic signal increased by 32.3%, from 62 incidents in 2015 to 82 in 2016.

Officer-Reported Primary Contributing Cause

Followed too close586 (22.5%)↑18.6%prior 494
Driving too fast for conditions178 (6.8%)↓-9.2%prior 196
Animal147 (5.7%)↑1.4%prior 145
Other (explain in narrative): Other133 (5.1%)↓-2.9%prior 137
FTYROW: From stop sign115 (4.4%)↓-8.0%prior 125
Lost Control113 (4.3%)↑17.7%prior 96
Improper or erratic lane changing95 (3.7%)↑26.7%prior 75
Ran off road - left94 (3.6%)↓-2.1%prior 96
FTYROW: Making left turn90 (3.5%)↑9.8%prior 82
Made improper turn86 (3.3%)↑4.9%prior 82

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

Road & Environmental Conditions

Crashes in 2016 occurred under more favorable conditions compared to 2015. The proportion of crashes happening in daylight increased from 69.7% to 72.0% of the total. There was a notable decrease in crashes occurring during adverse weather, with rain-related incidents falling from 225 to 108. Similarly, crashes on non-dry road surfaces like wet or snow-covered roads decreased as a share of the total, with wet-road crashes falling from 372 to 270.

Weather

Clear1,535 (60.9%)
↑14.5%prior 1,341
Cloudy715 (28.4%)
↑25.9%prior 568
Snow116 (4.6%)
→0.0%prior 116
Rain108 (4.3%)
↓-52.0%prior 225
Freezing rain/drizzle20 (0.8%)
↑25.0%prior 16
Fog, smoke, smog11 (0.4%)
↓-15.4%prior 13
Blowing Snow8 (0.3%)
↓-46.7%prior 15
Other (explain in narrative)4 (0.2%)
Severe Winds2 (0.1%)
Sleet, hail1 (0.0%)

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

Lighting

Daylight1,874 (74.0%)
↑13.0%prior 1,659
Dark - roadway lighted302 (11.9%)
↓-0.3%prior 303
Dark - roadway not lighted238 (9.4%)
↑5.3%prior 226
Dusk59 (2.3%)
↓-21.3%prior 75
Dawn48 (1.9%)
↑23.1%prior 39
Dark - unknown roadway lighting10 (0.4%)
↓-23.1%prior 13

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

Road Surface

Dry2,001 (79.5%)
↑24.0%prior 1,614
Wet270 (10.7%)
↓-27.4%prior 372
Snow120 (4.8%)
↓-36.2%prior 188
Ice/frost71 (2.8%)
↓-28.3%prior 99
Slush32 (1.3%)
↑146.2%prior 13
Gravel17 (0.7%)
↑41.7%prior 12
Other (explain in narrative)2 (0.1%)
↓-75.0%prior 8
Mud, dirt2 (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 most common vehicle makes involved in crashes remained consistent, with Chevrolet, Ford, and Toyota being the top three in both 2015 and 2016 after consolidating abbreviated and full names. When analyzing the age of persons involved, the 26-34 age group was the most represented in both years, though their count decreased from 930 to 895. Conversely, the number of individuals in the 16-20 and 21-25 age groups involved in crashes increased from 710 to 737 and 818 to 860, respectively.

Top Vehicle Makes (4,837 vehicles)

1
FORD677 (14%)
↑14.2%prior 593
2
CHEV408 (8.4%)
↓-8.9%prior 448
3
TOYT375 (7.8%)
↓-2.6%prior 385
4
CHEVROLET302 (6.2%)
↑48.8%prior 203
5
TOYOTA238 (4.9%)
↑60.8%prior 148
6
HOND204 (4.2%)
↓-10.5%prior 228
7
HONDA156 (3.2%)
↑85.7%prior 84
8
JEEP149 (3.1%)
→0.0%prior 149
9
NISS123 (2.5%)
↓-9.6%prior 136
10
DODG122 (2.5%)
↓-17.6%prior 148

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

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

Sex Distribution (3,944 persons with recorded sex)

Male2,171 (55.0%)
↑2.1%prior 2,127
Female1,773 (45.0%)
↓-0.5%prior 1,782

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: 2,601
  • Total persons involved: 5,318
  • Total vehicles involved: 4,837

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: 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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