Yearly Traffic Safety Analysis

183 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Madison County recorded 183 total crashes, a 1.7% increase from the 180 crashes reported in 2015. While the overall crash volume remained relatively stable, the number of people injured rose from 52 to 69, representing a 32.7% increase year-over-year.

183

▲ 1.7%was 180

Total Crash Events

5

▲ 25.0%was 4

Persons Killed

69

▲ 32.7%was 52

Persons Injured

5

▲ 25.0%was 4

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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

Crash trends in Madison County showed a slight increase in volume from 2015 to 2016, with total crashes rising by 1.7% from 180 to 183. The severity of these incidents worsened, as total injuries increased by 32.7% from 52 to 69, and fatalities rose from 4 to 5.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 4↑25.0%

2

Pedestrians Injured

Prior: 0→%

67

Motorists Injured

Prior: 51↑31.4%

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 distribution of crashes shifted between 2015 and 2016. In 2015, Friday was the distinct peak day with 37 crashes, but in 2016, the peak was shared between Wednesday and Saturday, each with 30 crashes. The peak hour for crashes also moved later in the day, from 3 p.m. in 2015 (16 crashes) to 5 p.m. in 2016 (18 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

The severity of crashes increased from 2015 to 2016. The number of fatal crashes rose from 4 to 5, and their corresponding share of all crashes increased from 2.2% to 2.7%. The proportion of crashes resulting in any level of injury (Serious, Minor, or Possible) also grew, from 23.3% of crashes in 2015 to 25.7% in 2016. This was driven by an increase in the share of minor injury crashes, which rose from 7.2% to 10.4% of all incidents.

Outcome by Severity (Crash Events)

Fatal5fatal crashes2.7%
↑25.0%prior 4
Serious Injury10serious injury crashes5.5%
↑11.1%prior 9
Minor Injury19minor injury crashes10.4%
↑46.2%prior 13
Possible Injury18possible injury crashes9.8%
↓-10.0%prior 20
No Injury131no injury crashes71.6%
↓-2.2%prior 134

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 from 50 in 2015 to 63 in 2016. 'Lost Control' was the second-most cited factor in 2016 with 18 incidents, a decrease from 24 incidents in the prior year when it also ranked second. Notably, crashes attributed to 'Followed too close' increased from a count of 1 to 10, while incidents of 'Driving too fast for conditions' decreased from 13 to 11.

Officer-Reported Primary Contributing Cause

Animal63 (34.4%)↑26.0%prior 50
Lost Control18 (9.8%)↓-25.0%prior 24
Ran off road - straight12 (6.6%)↑71.4%prior 7
Driving too fast for conditions11 (6%)↓-15.4%prior 13
Followed too close10 (5.5%)
FTYROW: From stop sign8 (4.4%)↓-33.3%prior 12
Ran off road - left6 (3.3%)↓-14.3%prior 7
FTYROW: From yield sign6 (3.3%)↑20.0%prior 5
Other (explain in narrative): Other5 (2.7%)
Made improper turn5 (2.7%)

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 majority of crashes in both 2015 and 2016 occurred under favorable conditions. In 2016, 51.4% of crashes happened in clear weather, up from 47.2% in 2015. Similarly, crashes on dry road surfaces accounted for 53.0% of the total in 2016, compared to 49.4% in the prior year. The proportion of crashes occurring in dark, unlighted conditions decreased from 26.1% in 2015 to 20.2% in 2016.

Weather

Clear94 (64.8%)
↑10.6%prior 85
Cloudy31 (21.4%)
↓-13.9%prior 36
Snow8 (5.5%)
↓-33.3%prior 12
Rain3 (2.1%)
↓-62.5%prior 8
Freezing rain/drizzle3 (2.1%)
Blowing Snow3 (2.1%)
Fog, smoke, smog2 (1.4%)
Blowing sand, soil, dirt1 (0.7%)

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

Lighting

Daylight85 (58.6%)
↓-2.3%prior 87
Dark - roadway not lighted37 (25.5%)
↓-21.3%prior 47
Dark - roadway lighted13 (9.0%)
↑85.7%prior 7
Dawn6 (4.1%)
Dusk4 (2.8%)

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

Road Surface

Dry97 (66.9%)
↑9.0%prior 89
Gravel19 (13.1%)
↑46.2%prior 13
Wet11 (7.6%)
↓-31.3%prior 16
Snow9 (6.2%)
↓-30.8%prior 13
Ice/frost8 (5.5%)
↓-33.3%prior 12
Slush1 (0.7%)

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 year-over-year, with Ford and Chevrolet (including the abbreviation 'CHEV') being the top makes in both 2015 and 2016 with similar counts. Regarding the age of persons involved, the 16-20 age group represented the largest cohort in both years but saw its count decrease from 55 to 47. Conversely, the number of persons in the 65+ age group involved in crashes increased from 22 in 2015 to 38 in 2016.

Top Vehicle Makes (259 vehicles)

1
FORD44 (17%)
↑2.3%prior 43
2
CHEV41 (15.8%)
↑2.5%prior 40
3
CHEVROLET27 (10.4%)
↓-6.9%prior 29
4
DODGE17 (6.6%)
↑54.5%prior 11
5
JEEP10 (3.9%)
↓-16.7%prior 12
6
DODG10 (3.9%)
↓-56.5%prior 23
7
PONT9 (3.5%)
↑80.0%prior 5
8
GMC7 (2.7%)
↑16.7%prior 6
9
KIA7 (2.7%)
10
FREIGHTLINER6 (2.3%)

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 (191 persons with recorded sex)

Male118 (61.8%)
↓-18.6%prior 145
Female73 (38.2%)
↓-19.8%prior 91

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: 183
  • Total persons involved: 302
  • Total vehicles involved: 259

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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