Yearly Traffic Safety Analysis

219 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Madison County, total vehicle crashes increased from 207 in 2017 to 219 in 2018, a rise of 5.8%. Despite the increase in overall collisions, the outcomes were significantly less severe. The most notable year-over-year shift was the complete elimination of traffic fatalities, which dropped from 3 in the prior period to zero in the current period.

219

▲ 5.8%was 207

Total Crash Events

0

▼ -100.0%was 3

Persons Killed

51

▼ -32.0%was 75

Persons Injured

0

▼ -100.0%was 3

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic safety trends in Madison County show a mixed but generally positive picture. While the total number of crashes increased by 5.8% from 207 to 219 year-over-year, the severity of these incidents decreased markedly. Total injuries fell by 32%, from 75 to 51, and fatalities were reduced from 3 to 0.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 3↓-100.0%

1

Cyclists Injured

Prior: 1→0.0%

50

Motorists Injured

Prior: 71↓-29.6%

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted slightly between the two periods. In 2018, the peak day for crashes was Thursday with 41 incidents, a change from Friday (36 incidents) in the previous year. The peak hour also moved one hour later, from the 4 p.m. hour in 2017 (20 crashes) to the 5 p.m. hour in 2018 (20 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity saw a significant improvement year-over-year. Fatal crashes were eliminated, falling from 3 in 2017 to 0 in 2018, with the fatal crash rate dropping from 1.45% to 0%. The proportion of crashes resulting in any type of injury (serious, minor, or possible) decreased from 28.5% of all crashes in 2017 to 22.4% in 2018. Consequently, no-injury crashes increased from 70% of the total to 77.6%.

Outcome by Severity (Crash Events)

Serious Injury5serious injury crashes2.3%
↓-28.6%prior 7
Minor Injury18minor injury crashes8.2%
↓-18.2%prior 22
Possible Injury26possible injury crashes11.9%
↓-13.3%prior 30
No Injury170no injury crashes77.6%
↑17.2%prior 145

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both periods, with the count of such crashes increasing by 30.5% from 59 in 2017 to 77 in 2018. The second and third most common factors, "Lost Control" and "Ran off road - straight," maintained their rankings but saw slight decreases in their incident counts. "Lost Control" incidents fell from 22 to 20, while "Ran off road - straight" incidents decreased from 19 to 15.

Officer-Reported Primary Contributing Cause

Animal77 (35.2%)↑30.5%prior 59
Lost Control20 (9.1%)↓-9.1%prior 22
Ran off road - straight15 (6.8%)↓-21.1%prior 19
Ran off road - left11 (5%)↑37.5%prior 8
Driver Distraction: Other interior distraction10 (4.6%)↓-9.1%prior 11
Ran off road - right9 (4.1%)
Followed too close8 (3.7%)↑14.3%prior 7
Driving too fast for conditions7 (3.2%)↓-22.2%prior 9
FTYROW: From stop sign6 (2.7%)↓-25.0%prior 8
Other (explain in narrative): Other5 (2.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

There was a notable shift in the road surface conditions reported during crashes. While dry roads were the most common condition in both years, the number of crashes on dry surfaces fell from 126 to 91. Conversely, crashes on adverse surfaces increased, with incidents on icy or frosty roads rising from 5 to 16 and those on snowy roads increasing from 6 to 15. The distribution of crashes across lighting and weather conditions remained relatively stable year-over-year.

Weather

Clear119 (74.8%)
↓-5.6%prior 126
Cloudy14 (8.8%)
↓-39.1%prior 23
Snow10 (6.3%)
Rain5 (3.1%)
↓-37.5%prior 8
Freezing rain/drizzle5 (3.1%)
Fog, smoke, smog3 (1.9%)
Sleet, hail1 (0.6%)
Other (explain in narrative)1 (0.6%)
Blowing Snow1 (0.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Weather condition at time of crash

Lighting

Daylight110 (69.2%)
↓-3.5%prior 114
Dark - roadway not lighted31 (19.5%)
↓-11.4%prior 35
Dusk8 (5.0%)
↑60.0%prior 5
Dawn6 (3.8%)
↓-14.3%prior 7
Dark - roadway lighted4 (2.5%)
↓-33.3%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Lighting condition field

Road Surface

Dry91 (56.5%)
↓-27.8%prior 126
Gravel17 (10.6%)
↑41.7%prior 12
Ice/frost16 (9.9%)
↑220.0%prior 5
Snow15 (9.3%)
↑150.0%prior 6
Wet14 (8.7%)
↑7.7%prior 13
Slush5 (3.1%)
Sand2 (1.2%)
Other (explain in narrative)1 (0.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Ford (71 vehicles) and Chevrolet (70 vehicles) being the top two most-involved makes in 2018, similar to the prior year. An analysis of the age of persons involved shows a shift in distribution. The number of individuals in the 35-44 and 45-54 age groups involved in crashes increased from 48 to 58 and 50 to 57, respectively, while involvement for the 65+ age group decreased from 47 to 39.

Top Vehicle Makes (300 vehicles)

1
FORD71 (23.7%)
↑7.6%prior 66
2
CHEV55 (18.3%)
↑14.6%prior 48
3
DODG23 (7.7%)
↓-8.0%prior 25
4
JEEP17 (5.7%)
↑41.7%prior 12
5
CHEVROLET15 (5%)
↓-28.6%prior 21
6
BUIC15 (5%)
↑87.5%prior 8
7
NISS8 (2.7%)
↑33.3%prior 6
8
TOYT7 (2.3%)
↓-41.7%prior 12
9
CHRY6 (2%)
→0.0%prior 6
10
TOYO6 (2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records

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

Sex Distribution (213 persons with recorded sex)

Male127 (59.6%)
↑3.3%prior 123
Female86 (40.4%)
↓-4.4%prior 90

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 219
  • Total persons involved: 372
  • Total vehicles involved: 300

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: 2018." Published September 9, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2018-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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