Yearly Traffic Safety Analysis

239 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Jefferson County, total vehicle crashes decreased by 6.6% from 256 in 2015 to 239 in 2016. While the number of fatalities remained constant at one, the most notable year-over-year shift was a 17.7% reduction in total injuries, from 79 to 65, and a 50% drop in the number of serious injury crashes.

239

▼ -6.6%was 256

Total Crash Events

1

Persons Killed

65

▼ -17.7%was 79

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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

The overall trend in Jefferson County from 2015 to 2016 shows a decrease in traffic incidents. Total crashes fell by 6.6%, from 256 to 239. This was accompanied by a 17.7% decline in injuries, from 79 to 65, while fatalities held steady at one for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 0→%

0

Other Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 2↓-50.0%

62

Motorists Injured

Prior: 77↓-19.5%

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 peak hour for crashes remained consistent at 5 p.m. in both 2015 and 2016, with 23 and 24 crashes respectively. However, the peak day of the week shifted from Monday (55 crashes) in 2015 to Friday (45 crashes) in 2016. November was the month with the most crashes in both periods, recording 37 incidents in 2015 and 33 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

The number of fatal crashes was unchanged at one incident in both 2015 and 2016. There was a positive shift in injury outcomes, with serious injury crashes falling by 50% from 8 incidents in 2015 to 4 in 2016. Correspondingly, the total number of persons injured decreased from 79 to 65 year-over-year.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
→0.0%prior 1
Serious Injury4serious injury crashes1.7%
↓-50.0%prior 8
Minor Injury21minor injury crashes8.8%
↓-19.2%prior 26
Possible Injury29possible injury crashes12.1%
↑11.5%prior 26
No Injury184no injury crashes77%
↓-5.6%prior 195

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

Collisions involving an animal remained the top contributing factor in both periods, though the count of such incidents decreased from 81 in 2015 to 71 in 2016. The top three primary factors were consistent year-over-year, including 'Lost Control' (27 incidents in 2015 vs. 26 in 2016) and 'FTYROW: From stop sign' (15 vs. 16). Notably, crashes attributed to 'Driving too fast for conditions' increased from 10 to 16.

Officer-Reported Primary Contributing Cause

Animal71 (29.7%)↓-12.3%prior 81
Lost Control26 (10.9%)↓-3.7%prior 27
FTYROW: From stop sign16 (6.7%)↑6.7%prior 15
Driving too fast for conditions16 (6.7%)↑60.0%prior 10
Ran off road - straight14 (5.9%)↑55.6%prior 9
Ran Stop Sign13 (5.4%)↑8.3%prior 12
Ran off road - left10 (4.2%)↓-9.1%prior 11
Followed too close10 (4.2%)↓-28.6%prior 14
Other (explain in narrative): Other9 (3.8%)↓-10.0%prior 10
Improper Backing4 (1.7%)

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 proportion of crashes occurring in clear weather and daylight conditions remained stable between 2015 and 2016. A notable shift occurred in road surface conditions; crashes on icy or frosty roads increased fivefold, from 3 incidents in 2015 to 15 in 2016. Conversely, crashes on snowy surfaces were halved, decreasing from 18 to 9.

Weather

Clear113 (65.7%)
↓-7.4%prior 122
Cloudy28 (16.3%)
↓-12.5%prior 32
Snow9 (5.2%)
↓-30.8%prior 13
Rain8 (4.7%)
↓-27.3%prior 11
Freezing rain/drizzle7 (4.1%)
Blowing Snow3 (1.7%)
Fog, smoke, smog2 (1.2%)
↓-60.0%prior 5
Other (explain in narrative)1 (0.6%)
Sleet, hail1 (0.6%)

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

Lighting

Daylight126 (72.8%)
↓-10.0%prior 140
Dark - roadway not lighted30 (17.3%)
↑11.1%prior 27
Dark - roadway lighted11 (6.4%)
→0.0%prior 11
Dawn5 (2.9%)
→0.0%prior 5
Dusk1 (0.6%)
↓-83.3%prior 6

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

Road Surface

Dry119 (68.8%)
↓-11.9%prior 135
Wet16 (9.2%)
↓-20.0%prior 20
Ice/frost15 (8.7%)
Snow9 (5.2%)
↓-50.0%prior 18
Gravel9 (5.2%)
↑28.6%prior 7
Slush3 (1.7%)
Water (standing or moving)2 (1.2%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes were consistent, with Ford and Chevrolet leading in both years; Chevrolet-involved crashes decreased from 69 to 59, while Ford-involved crashes were stable at 58 and 60. Analysis of persons involved shows a significant drop in the 65+ age group, from 75 individuals in 2015 to 51 in 2016. The 16-20 age group also saw a decrease in involvement from 57 persons to 37.

Top Vehicle Makes (341 vehicles)

1
FORD60 (17.6%)
↑3.4%prior 58
2
CHEVROLET39 (11.4%)
↑14.7%prior 34
3
TOYT22 (6.5%)
↓-12.0%prior 25
4
CHEV20 (5.9%)
↓-42.9%prior 35
5
GMC16 (4.7%)
→0.0%prior 16
6
TOYOTA15 (4.4%)
↑7.1%prior 14
7
DODGE15 (4.4%)
↓-21.1%prior 19
8
DODG12 (3.5%)
↓-53.8%prior 26
9
JEEP10 (2.9%)
10
HOND8 (2.3%)
→0.0%prior 8

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

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

Sex Distribution (276 persons with recorded sex)

Male143 (51.8%)
↓-26.3%prior 194
Female133 (48.2%)
↓-8.3%prior 145

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: 239
  • Total persons involved: 382
  • Total vehicles involved: 341

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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