Yearly Traffic Safety Analysis

200 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Madison County recorded 200 total crashes, an 8.7% decrease from the 219 crashes documented in 2018. While total injuries remained relatively stable, increasing slightly from 51 to 53, the most significant change was the occurrence of one fatal crash resulting in one death in 2019, whereas no fatalities were reported in the prior year.

200

▼ -8.7%was 219

Total Crash Events

1

Persons Killed

53

▲ 3.9%was 51

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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, the total number of crashes in Madison County decreased from 219 in 2018 to 200 in 2019, representing an 8.7% reduction. Despite this drop in total incidents, the number of people injured saw a slight increase from 51 to 53. The county also recorded one fatality in 2019, compared to none in the previous year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 0→%

1

Pedestrians Injured

Prior: 0→%

2

Cyclists Injured

Prior: 1↑100.0%

50

Motorists Injured

Prior: 50→0.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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 periods. The peak day for crashes moved from Thursday in 2018, with 41 incidents, to Monday in 2019, with 34 incidents. However, the peak hour for crashes remained consistent, occurring at 5 p.m. in both years, with 19 crashes in 2019 and 20 in 2018.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity saw a mixed trend year-over-year. In 2019, there was one fatal crash, accounting for 0.5% of all incidents, whereas 2018 had none. The share of serious injury crashes increased from 2.3% (5 crashes) in 2018 to 4.0% (8 crashes) in 2019. Conversely, crashes resulting in possible injuries saw a significant decrease, dropping from 26 incidents (11.9% of total) in 2018 to 13 (6.5% of total) in 2019.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.5%
Serious Injury8serious injury crashes4%
↑60.0%prior 5
Minor Injury17minor injury crashes8.5%
↓-5.6%prior 18
Possible Injury13possible injury crashes6.5%
↓-50.0%prior 26
No Injury161no injury crashes80.5%
↓-5.3%prior 170

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the top contributing factor in both years but decreased in volume, falling from 77 crashes in 2018 to 59 in 2019, a 23.4% drop in count. The most significant year-over-year change was in crashes attributed to 'Driving too fast for conditions,' which surged from 7 incidents in 2018 to 19 in 2019. Conversely, crashes due to 'Lost Control' declined from 20 to 15 over the same period.

Officer-Reported Primary Contributing Cause

Animal59 (29.5%)↓-23.4%prior 77
Driving too fast for conditions19 (9.5%)↑171.4%prior 7
Lost Control15 (7.5%)↓-25.0%prior 20
Driver Distraction: Other interior distraction12 (6%)↑20.0%prior 10
Ran off road - straight10 (5%)↓-33.3%prior 15
Other (explain in narrative): Other9 (4.5%)↑80.0%prior 5
FTYROW: From stop sign8 (4%)↑33.3%prior 6
Followed too close8 (4%)→0.0%prior 8
Ran off road - left6 (3%)↓-45.5%prior 11
Improper Backing5 (2.5%)→0.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in various conditions remained broadly similar year-over-year. Crashes during daylight hours constituted a slightly larger share of the total in 2019 (56.5%) compared to 2018 (50.2%). Incidents on snowy road surfaces increased from 15 in 2018 to 21 in 2019. Crashes in clear weather conditions decreased from 119 to 99, while those in cloudy conditions increased from 14 to 19.

Weather

Clear99 (66.4%)
↓-16.8%prior 119
Cloudy19 (12.8%)
↑35.7%prior 14
Snow10 (6.7%)
→0.0%prior 10
Blowing Snow8 (5.4%)
Rain7 (4.7%)
↑40.0%prior 5
Freezing rain/drizzle4 (2.7%)
↓-20.0%prior 5
Other (explain in narrative)1 (0.7%)
Fog, smoke, smog1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight113 (75.3%)
↑2.7%prior 110
Dark - roadway not lighted23 (15.3%)
↓-25.8%prior 31
Dark - roadway lighted9 (6.0%)
Dusk3 (2.0%)
↓-62.5%prior 8
Dawn1 (0.7%)
↓-83.3%prior 6
Dark - unknown roadway lighting1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry78 (52.3%)
↓-14.3%prior 91
Snow21 (14.1%)
↑40.0%prior 15
Wet20 (13.4%)
↑42.9%prior 14
Ice/frost14 (9.4%)
↓-12.5%prior 16
Gravel13 (8.7%)
↓-23.5%prior 17
Other (explain in narrative)1 (0.7%)
Sand1 (0.7%)
Slush1 (0.7%)
↓-80.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes shifted between the two years. In 2018, Ford was the most common make with 71 vehicles involved, but this number dropped to 48 in 2019. The age distribution of persons involved in crashes showed a notable increase in the 16-20 age group, which grew from 53 individuals in 2018 to 68 in 2019. The number of people aged 65 and older involved in crashes also rose from 39 to 54.

Top Vehicle Makes (286 vehicles)

1
CHEV54 (18.9%)
↓-1.8%prior 55
2
FORD48 (16.8%)
↓-32.4%prior 71
3
CHEVROLET19 (6.6%)
↑26.7%prior 15
4
JEEP17 (5.9%)
→0.0%prior 17
5
DODG13 (4.5%)
↓-43.5%prior 23
6
BUIC11 (3.8%)
↓-26.7%prior 15
7
TOYT11 (3.8%)
↑57.1%prior 7
8
HOND7 (2.4%)
9
NISSAN7 (2.4%)
10
TOYO6 (2.1%)
→0.0%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

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

Sex Distribution (269 persons with recorded sex)

Male153 (56.9%)
↑20.5%prior 127
Female116 (43.1%)
↑34.9%prior 86

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 200
  • Total persons involved: 403
  • Total vehicles involved: 286

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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