Yearly Traffic Safety Analysis

170 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Greene County recorded 170 total crashes, a 7.6% decrease from the 184 crashes reported in 2015. While overall crashes and total injuries (down 36% from 75 to 48) declined, the number of fatalities increased from two to three. A significant year-over-year change was the 62.5% reduction in crashes involving a driver under the influence, which fell from eight in 2015 to three in 2016.

170

▼ -7.6%was 184

Total Crash Events

3

▲ 50.0%was 2

Persons Killed

48

▼ -36.0%was 75

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

Traffic crashes in Greene County showed a downward trend from 2015 to 2016, with total incidents decreasing by 7.6% from 184 to 170. This decline was accompanied by a 36% drop in the number of people injured, from 75 to 48. However, the number of fatalities rose from two in the prior year to three in the current year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 2↑50.0%

2

Pedestrians Injured

Prior: 1↑100.0%

46

Motorists Injured

Prior: 74↓-37.8%

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

In 2016, the peak day for crashes was Friday with 37 incidents, compared to 2015 when Friday and Thursday shared the peak with 30 crashes each. The busiest hours for crashes shifted slightly later in the afternoon; the 3 PM and 5 PM hours were the peak in 2016 with 18 crashes each, whereas the 2 PM and 4 PM hours were the peak in 2015 with 17 crashes each. The general pattern of crashes concentrating in the afternoon remained consistent.

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

While the number of fatal crashes remained constant at two in both 2016 and 2015, the number of individuals killed increased from two to three. Consequently, the fatal crash rate per 100 crashes rose from 1.09 to 1.18. The proportion of crashes resulting in any level of injury decreased from 28.9% in 2015 to 22.3% in 2016, driven by a reduction in the share of both minor and possible injury incidents.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.2%
→0.0%prior 2
Serious Injury10serious injury crashes5.9%
↑66.7%prior 6
Minor Injury13minor injury crashes7.6%
↓-35.0%prior 20
Possible Injury15possible injury crashes8.8%
↓-44.4%prior 27
No Injury130no injury crashes76.5%
↑0.8%prior 129

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 with an animal remained the top contributing factor in both 2016 and 2015, with an identical count of 34 incidents. The count of crashes due to 'Lost Control' increased from 17 to 19, making it the second-most cited factor in 2016. In contrast, crashes attributed to 'Driving too fast for conditions' saw a notable decrease in count from 15 in 2015 to 8 in 2016. Crashes involving 'Driver Distraction: Other interior distraction' tripled, increasing from 3 incidents to 9.

Officer-Reported Primary Contributing Cause

Animal34 (20%)→0.0%prior 34
Lost Control19 (11.2%)↑11.8%prior 17
FTYROW: At uncontrolled intersection12 (7.1%)↓-7.7%prior 13
Ran off road - straight12 (7.1%)↓-7.7%prior 13
Driver Distraction: Other interior distraction9 (5.3%)
Ran Stop Sign8 (4.7%)↑14.3%prior 7
Driving too fast for conditions8 (4.7%)↓-46.7%prior 15
FTYROW: From stop sign8 (4.7%)↓-33.3%prior 12
Made improper turn6 (3.5%)
Ran off road - left5 (2.9%)

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 crashes across weather and road surface conditions was largely consistent year-over-year, with clear weather and dry roads being the most common circumstances in both periods. There was a shift in lighting conditions, as the share of crashes occurring in daylight fell from 65.2% in 2015 to 58.2% in 2016. Correspondingly, the proportion of crashes taking place on dark, unlit roadways increased from 16.8% to 21.2% of all incidents.

Weather

Clear99 (63.9%)
↓-11.6%prior 112
Cloudy36 (23.2%)
↑24.1%prior 29
Snow8 (5.2%)
Rain5 (3.2%)
↓-37.5%prior 8
Freezing rain/drizzle4 (2.6%)
Blowing Snow2 (1.3%)
Other (explain in narrative)1 (0.6%)

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

Lighting

Daylight99 (63.1%)
↓-17.5%prior 120
Dark - roadway not lighted36 (22.9%)
↑16.1%prior 31
Dark - roadway lighted11 (7.0%)
↑57.1%prior 7
Dusk6 (3.8%)
Dawn5 (3.2%)

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

Road Surface

Dry112 (70.4%)
↓-1.8%prior 114
Ice/frost14 (8.8%)
↑27.3%prior 11
Snow11 (6.9%)
↑22.2%prior 9
Wet10 (6.3%)
↓-37.5%prior 16
Gravel8 (5.0%)
↓-33.3%prior 12
Slush3 (1.9%)
Mud, dirt1 (0.6%)

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

Vehicles & Demographics

Ford and Chevrolet (listed as 'CHEVROLET' and 'CHEV') were the most common vehicle makes involved in crashes during both periods. In 2016, Ford vehicles were involved in 45 crashes, down from 57 in 2015. An analysis of persons involved shows the 16-20 age group had the highest representation in 2016 with 44 individuals, which was a decrease from 64 individuals in the same group in 2015.

Top Vehicle Makes (243 vehicles)

1
FORD45 (18.5%)
↓-21.1%prior 57
2
CHEVROLET38 (15.6%)
↑46.2%prior 26
3
CHEV23 (9.5%)
↓-59.6%prior 57
4
DODG11 (4.5%)
↑57.1%prior 7
5
TOYT11 (4.5%)
↑120.0%prior 5
6
GMC11 (4.5%)
→0.0%prior 11
7
PONT8 (3.3%)
↑33.3%prior 6
8
DODGE7 (2.9%)
9
BUIC6 (2.5%)
↑20.0%prior 5
10
HOND6 (2.5%)
↓-14.3%prior 7

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

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

Sex Distribution (189 persons with recorded sex)

Male111 (58.7%)
↓-26.0%prior 150
Female78 (41.3%)
↓-29.7%prior 111

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: 170
  • Total persons involved: 269
  • Total vehicles involved: 243

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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