Yearly Traffic Safety Analysis

190 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Franklin County recorded 190 total crashes, a 20.3% increase from the 158 crashes reported in 2015. While total fatalities decreased from 3 to 1, total injuries rose by 24.6% from 57 to 71. The number of single-vehicle crashes, a significant portion of the total, grew from 63 incidents in 2015 to 93 in 2016.

190

▲ 20.3%was 158

Total Crash Events

1

▼ -66.7%was 3

Persons Killed

71

▲ 24.6%was 57

Persons Injured

1

▼ -50.0%was 2

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

Overall, traffic crashes in Franklin County increased from 2015 to 2016. The total number of crashes rose by 20.3%, from 158 to 190. Similarly, the number of people injured increased by 24.6% year-over-year, from 57 to 71, while fatalities decreased from 3 to 1.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 3↓-66.7%

1

Cyclists Injured

Prior: 1→0.0%

70

Motorists Injured

Prior: 55↑27.3%

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 showed some shifts between 2015 and 2016. Friday remained the peak day for crashes in both years, with the count increasing from 29 to 35. However, the peak hour for crashes moved from the morning commute at 7 a.m. in 2015, which saw 17 crashes, to the afternoon at 3 p.m. in 2016, with 16 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

While the total number of crashes increased, the fatal crash rate per 100 crashes decreased from 1.27 in 2015 to 0.53 in 2016. The proportion of crashes involving any level of injury remained stable, accounting for 27.4% of all crashes in 2016 compared to 27.8% in 2015. Crashes resulting in serious injuries increased in count from 1 to 3, and their share of total crashes grew from 0.6% to 1.6%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.5%
↓-50.0%prior 2
Serious Injury3serious injury crashes1.6%
↑200.0%prior 1
Minor Injury22minor injury crashes11.6%
↑29.4%prior 17
Possible Injury26possible injury crashes13.7%
↑8.3%prior 24
No Injury138no injury crashes72.6%
↑21.1%prior 114

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, with the count increasing by 21.1% from 38 crashes in 2015 to 46 in 2016. 'Lost Control' was the second-ranked factor in both years with an identical count of 17 incidents. The number of crashes attributed to 'Failure to Yield Right of Way from a stop sign' decreased from 10 in 2015 to 6 in 2016.

Officer-Reported Primary Contributing Cause

Animal46 (24.2%)↑21.1%prior 38
Lost Control17 (8.9%)→0.0%prior 17
Other (explain in narrative): Other13 (6.8%)↑44.4%prior 9
Ran off road - straight12 (6.3%)→0.0%prior 12
FTYROW: At uncontrolled intersection10 (5.3%)↑11.1%prior 9
FTYROW: Making left turn9 (4.7%)↑28.6%prior 7
Ran off road - left8 (4.2%)→0.0%prior 8
Driver Distraction: Other interior distraction7 (3.7%)
FTYROW: From stop sign6 (3.2%)↓-40.0%prior 10
Driving too fast for conditions6 (3.2%)→0.0%prior 6

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

Road & Environmental Conditions

In both 2016 and 2015, the majority of crashes occurred in clear weather, during daylight hours, and on dry roads. The number of crashes during daylight increased from 93 to 109, and on dry roads from 86 to 93. Notably, crashes in cloudy conditions increased significantly from 24 in 2015 to 43 in 2016. Incidents on icy road surfaces also rose from 8 to 13 year-over-year.

Weather

Clear77 (51.7%)
↑2.7%prior 75
Cloudy43 (28.9%)
↑79.2%prior 24
Rain9 (6.0%)
↑12.5%prior 8
Snow8 (5.4%)
↓-11.1%prior 9
Freezing rain/drizzle4 (2.7%)
↓-20.0%prior 5
Severe Winds4 (2.7%)
Blowing Snow2 (1.3%)
Other (explain in narrative)1 (0.7%)
Fog, smoke, smog1 (0.7%)
↓-85.7%prior 7

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

Lighting

Daylight109 (73.2%)
↑17.2%prior 93
Dark - roadway not lighted26 (17.4%)
↑18.2%prior 22
Dark - roadway lighted7 (4.7%)
→0.0%prior 7
Dusk4 (2.7%)
Dawn3 (2.0%)
↓-40.0%prior 5

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

Road Surface

Dry93 (62.4%)
↑8.1%prior 86
Wet18 (12.1%)
↑12.5%prior 16
Snow18 (12.1%)
↑28.6%prior 14
Ice/frost13 (8.7%)
↑62.5%prior 8
Other (explain in narrative)2 (1.3%)
Mud, dirt2 (1.3%)
Slush2 (1.3%)
Gravel1 (0.7%)

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

Vehicles & Demographics

An analysis of vehicle makes involved in crashes shows Ford was a top make in both years, with its count remaining stable at 47 vehicles in 2016 compared to 48 in 2015. The number of Chevrolet vehicles (including 'Chev' abbreviations) involved in crashes increased from 50 to 72. Regarding the demographics of people involved, the count of individuals in the 21-25 age group rose from 20 to 33, while those in the 26-34 age group decreased from 56 to 46.

Top Vehicle Makes (292 vehicles)

1
FORD47 (16.1%)
↓-2.1%prior 48
2
CHEVROLET44 (15.1%)
↑100.0%prior 22
3
CHEV28 (9.6%)
→0.0%prior 28
4
DODGE11 (3.8%)
↑10.0%prior 10
5
PONTIAC11 (3.8%)
6
CHRYSLER9 (3.1%)
↑80.0%prior 5
7
CHRY8 (2.7%)
↑60.0%prior 5
8
DODG8 (2.7%)
↓-33.3%prior 12
9
FREIGHTLINER8 (2.7%)
↓-11.1%prior 9
10
GMC8 (2.7%)
→0.0%prior 8

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

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

Sex Distribution (199 persons with recorded sex)

Male129 (64.8%)
↑13.2%prior 114
Female70 (35.2%)
↓-7.9%prior 76

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: 190
  • Total persons involved: 338
  • Total vehicles involved: 292

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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