Yearly Traffic Safety Analysis

207 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Madison County recorded 207 total crashes, an increase from 183 crashes in 2016, representing a 13.1% rise year-over-year. While the overall number of crashes grew, the number of fatalities decreased from 5 in 2016 to 3 in 2017. Total injuries saw a slight increase from 69 to 75 over the same period.

207

▲ 13.1%was 183

Total Crash Events

3

▼ -40.0%was 5

Persons Killed

75

▲ 8.7%was 69

Persons Injured

3

▼ -40.0%was 5

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 trends in Madison County showed an overall increase between 2016 and 2017. The total number of crashes rose by 13.1%, from 183 to 207. While total fatalities decreased from 5 to 3, the number of people injured in crashes increased by 8.7% from 69 to 75.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 5↓-40.0%

3

Pedestrians Injured

Prior: 2↑50.0%

1

Cyclists Injured

Prior: 0→%

71

Motorists Injured

Prior: 67↑6.0%

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 shifted between 2016 and 2017. The peak day for collisions moved from Wednesday and Saturday (30 crashes each) in 2016 to Friday (36 crashes) in 2017. Similarly, the peak hour for crashes shifted an hour earlier, from 5 p.m. (18 crashes) in the prior year to 4 p.m. (20 crashes) in the current year. The month with the most crashes also changed from September in 2016 to December in 2017.

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 total crashes increased, the severity of those crashes generally lessened from 2016 to 2017. The number of fatal crashes fell from 5 to 3, and the share of crashes resulting in a fatality dropped from 2.7% to 1.4%. Similarly, serious injury crashes decreased from 10 to 7. Conversely, the number of crashes involving possible injuries increased from 18 to 30.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.4%
↓-40.0%prior 5
Serious Injury7serious injury crashes3.4%
↓-30.0%prior 10
Minor Injury22minor injury crashes10.6%
↑15.8%prior 19
Possible Injury30possible injury crashes14.5%
↑66.7%prior 18
No Injury145no injury crashes70%
↑10.7%prior 131

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 leading contributing factor in both periods, though the count decreased slightly from 63 crashes in 2016 to 59 in 2017. 'Lost Control' remained the second-most cited factor, increasing from 18 to 22 crashes. A notable increase was observed in crashes attributed to 'Driver Distraction: Other interior distraction', which rose from 3 incidents in 2016 to 11 in 2017. Conversely, crashes related to 'Driving too fast for conditions' and 'Followed too close' both saw a decrease in count, falling from 11 to 9 and 10 to 7, respectively.

Officer-Reported Primary Contributing Cause

Animal59 (28.5%)↓-6.3%prior 63
Lost Control22 (10.6%)↑22.2%prior 18
Ran off road - straight19 (9.2%)↑58.3%prior 12
Driver Distraction: Other interior distraction11 (5.3%)
Driving too fast for conditions9 (4.3%)↓-18.2%prior 11
FTYROW: From stop sign8 (3.9%)→0.0%prior 8
Ran off road - left8 (3.9%)↑33.3%prior 6
FTYROW: From yield sign8 (3.9%)↑33.3%prior 6
Followed too close7 (3.4%)↓-30.0%prior 10
Improper Backing5 (2.4%)

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

Road & Environmental Conditions

A larger share of crashes in 2017 occurred in clear conditions compared to 2016. Crashes on dry roads increased from 53.0% of all incidents in 2016 to 60.9% in 2017, while collisions on adverse surfaces like wet, snow, or ice decreased in count from 29 to 24. Similarly, crashes during daylight hours made up a larger percentage of the total, rising from 46.4% in 2016 to 55.1% in 2017. Crashes in dark, unlighted conditions decreased proportionally from 20.2% to 16.9%.

Weather

Clear126 (76.4%)
↑34.0%prior 94
Cloudy23 (13.9%)
↓-25.8%prior 31
Rain8 (4.8%)
Snow4 (2.4%)
↓-50.0%prior 8
Fog, smoke, smog2 (1.2%)
Freezing rain/drizzle2 (1.2%)

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

Lighting

Daylight114 (68.3%)
↑34.1%prior 85
Dark - roadway not lighted35 (21.0%)
↓-5.4%prior 37
Dawn7 (4.2%)
↑16.7%prior 6
Dark - roadway lighted6 (3.6%)
↓-53.8%prior 13
Dusk5 (3.0%)

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

Road Surface

Dry126 (75.9%)
↑29.9%prior 97
Wet13 (7.8%)
↑18.2%prior 11
Gravel12 (7.2%)
↓-36.8%prior 19
Snow6 (3.6%)
↓-33.3%prior 9
Ice/frost5 (3.0%)
↓-37.5%prior 8
Other (explain in narrative)3 (1.8%)
Mud, dirt1 (0.6%)

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 models being the most common in both 2016 and 2017. The number of Ford vehicles in crashes increased from 44 to 66 year-over-year. An analysis of persons involved shows a notable increase in the 45-54 age group, whose count rose from 36 in 2016 to 50 in 2017. The 16-20 age group also saw an increase in the number of individuals involved, from 47 to 55, though their proportional share remained stable.

Top Vehicle Makes (302 vehicles)

1
FORD66 (21.9%)
↑50.0%prior 44
2
CHEV48 (15.9%)
↑17.1%prior 41
3
DODG25 (8.3%)
↑150.0%prior 10
4
CHEVROLET21 (7%)
↓-22.2%prior 27
5
TOYT12 (4%)
6
JEEP12 (4%)
↑20.0%prior 10
7
GMC10 (3.3%)
↑42.9%prior 7
8
BUIC8 (2.6%)
↑60.0%prior 5
9
DODGE7 (2.3%)
↓-58.8%prior 17
10
SUBA7 (2.3%)

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

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

Sex Distribution (213 persons with recorded sex)

Male123 (57.7%)
↑4.2%prior 118
Female90 (42.3%)
↑23.3%prior 73

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: 207
  • Total persons involved: 349
  • Total vehicles involved: 302

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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