Yearly Traffic Safety Analysis

196 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Franklin County recorded 196 total crashes, a 3.2% increase from the 190 crashes documented in 2016. Despite the rise in total incidents, the number of people injured decreased significantly by 35.2%, from 71 to 46. The most notable shift was a decrease in the overall severity of crashes, with the proportion of incidents resulting in no injuries rising from 72.6% in 2016 to 82.7% in 2017.

196

▲ 3.2%was 190

Total Crash Events

1

Persons Killed

46

▼ -35.2%was 71

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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash volume in Franklin County saw a slight year-over-year increase, rising from 190 incidents in 2016 to 196 in 2017. However, the outcomes of these crashes became less severe, as total injuries fell from 71 to 46, a 35.2% reduction. The number of fatalities remained stable, with one person killed in a crash in both 2016 and 2017.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

1

Pedestrians Injured

Prior: 0→%

45

Motorists Injured

Prior: 70↓-35.7%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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 the two years. While Friday remained the peak day for crashes in both periods, the number of incidents on that day increased from 35 in 2016 to 44 in 2017. The peak hour for collisions shifted from the 3 p.m. hour in 2016, which had 16 crashes, to the 5 p.m. hour in 2017, which had 18 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the number of fatal crashes remained unchanged at one for both 2016 and 2017, the overall severity of crashes decreased. The proportion of crashes resulting in any form of injury (Serious, Minor, or Possible) dropped from a combined 26.9% of all crashes in 2016 to 16.8% in 2017. Correspondingly, the share of no-injury crashes increased from 72.6% (138 incidents) in 2016 to 82.7% (162 incidents) in 2017.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.5%
→0.0%prior 1
Serious Injury4serious injury crashes2%
↑33.3%prior 3
Minor Injury13minor injury crashes6.6%
↓-40.9%prior 22
Possible Injury16possible injury crashes8.2%
↓-38.5%prior 26
No Injury162no injury crashes82.7%
↑17.4%prior 138

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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 26.1% from 46 crashes in 2016 to 58 in 2017. A significant year-over-year change was observed in crashes attributed to 'Driving too fast for conditions,' which more than tripled in count from 6 incidents to 20. Conversely, crashes where a driver failed to yield at an uncontrolled intersection were halved, decreasing from 10 to 5.

Officer-Reported Primary Contributing Cause

Animal58 (29.6%)↑26.1%prior 46
Lost Control20 (10.2%)↑17.6%prior 17
Driving too fast for conditions20 (10.2%)↑233.3%prior 6
Ran off road - straight13 (6.6%)↑8.3%prior 12
Other (explain in narrative): Other11 (5.6%)↓-15.4%prior 13
Ran off road - left8 (4.1%)→0.0%prior 8
FTYROW: From stop sign7 (3.6%)↑16.7%prior 6
Operating vehicle in an reckless, erratic, careless, negligent manner5 (2.6%)→0.0%prior 5
FTYROW: At uncontrolled intersection5 (2.6%)↓-50.0%prior 10
Driver Distraction: Other interior distraction5 (2.6%)↓-28.6%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The data indicates a year-over-year increase in crashes occurring under adverse conditions. Crashes on roads with ice, frost, snow, or wet surfaces rose from 51 incidents in 2016 to 60 in 2017. Similarly, crashes during weather conditions like snow, rain, and freezing rain increased from a combined 23 incidents to 35. Crashes in dark conditions also saw an increase, rising from 33 in 2016 to 42 in 2017.

Weather

Clear70 (50.4%)
↓-9.1%prior 77
Cloudy29 (20.9%)
↓-32.6%prior 43
Freezing rain/drizzle13 (9.4%)
Snow12 (8.6%)
↑50.0%prior 8
Rain6 (4.3%)
↓-33.3%prior 9
Blowing Snow4 (2.9%)
Severe Winds2 (1.4%)
Fog, smoke, smog2 (1.4%)
Other (explain in narrative)1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight87 (62.6%)
↓-20.2%prior 109
Dark - roadway not lighted32 (23.0%)
↑23.1%prior 26
Dark - roadway lighted10 (7.2%)
↑42.9%prior 7
Dawn7 (5.0%)
Dusk3 (2.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry74 (52.9%)
↓-20.4%prior 93
Ice/frost31 (22.1%)
↑138.5%prior 13
Snow14 (10.0%)
↓-22.2%prior 18
Wet12 (8.6%)
↓-33.3%prior 18
Gravel5 (3.6%)
Slush3 (2.1%)
Mud, dirt1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Ford and Chevrolet representing the highest counts in both 2016 and 2017. An analysis of person demographics shows a shift in the age groups of individuals involved in crashes. The number of people in the 45-54 age group increased from 40 in 2016 to 51 in 2017, while the 55-64 age group saw its involvement decrease from 48 to 37 people.

Top Vehicle Makes (264 vehicles)

1
FORD48 (18.2%)
↑2.1%prior 47
2
CHEVROLET37 (14%)
↓-15.9%prior 44
3
CHEV32 (12.1%)
↑14.3%prior 28
4
DODGE10 (3.8%)
↓-9.1%prior 11
5
JEEP9 (3.4%)
6
FREIGHTLINER9 (3.4%)
↑12.5%prior 8
7
TOYOTA9 (3.4%)
↑28.6%prior 7
8
DODG8 (3%)
→0.0%prior 8
9
GMC8 (3%)
→0.0%prior 8
10
CHRYSLER7 (2.7%)
↓-22.2%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (184 persons with recorded sex)

Male116 (63.0%)
↓-10.1%prior 129
Female68 (37.0%)
↓-2.9%prior 70

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 196
  • Total persons involved: 315
  • Total vehicles involved: 264

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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