Yearly Traffic Safety Analysis

135 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Monroe County, total traffic crashes increased by 17.4%, rising from 115 in 2017 to 135 in 2018. The most significant year-over-year change was the recording of two fatalities in 2018, whereas none were recorded in the prior year. Additionally, total injuries rose from 31 to 39 during the same period.

135

▲ 17.4%was 115

Total Crash Events

2

Persons Killed

39

▲ 25.8%was 31

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (2) 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 · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Monroe County showed an increase between 2017 and 2018. The total number of crashes rose by 17.4% from 115 to 135. Similarly, the number of people injured increased by 25.8% from 31 to 39, and fatalities increased from zero to two.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 0→%

1

Pedestrians Injured

Prior: 3↓-66.7%

1

Cyclists Injured

Prior: 0→%

37

Motorists Injured

Prior: 28↑32.1%

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 time patterns of crashes shifted year-over-year. The peak day for crashes moved from Thursday (23 crashes) in 2017 to Wednesday (25 crashes) in 2018. The morning peak hour also shifted earlier, from 7 a.m. in 2017 (10 crashes) to 6 a.m. in 2018 (17 crashes), with a 70% increase in crash volume during that hour.

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 worsened in 2018 compared to 2017. The county recorded one fatal crash resulting in two deaths in 2018, after having zero fatal crashes the previous year. The proportion of crashes resulting in any level of injury (Fatal, Serious, Minor, or Possible) increased from 20.9% of all crashes in 2017 to 25.9% in 2018. Consequently, the share of crashes with no reported injuries decreased from 79.1% to 74.1%.

Severity is per crash event (most severe injury). 1 fatal crash events resulted in 2 persons killed.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.7%
Serious Injury1serious injury crashes0.7%
↓-50.0%prior 2
Minor Injury11minor injury crashes8.1%
↑37.5%prior 8
Possible Injury22possible injury crashes16.3%
↑57.1%prior 14
No Injury100no injury crashes74.1%
↑9.9%prior 91

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 with animals were the leading contributing factor in both periods, with the count increasing by 27.5% from 51 incidents in 2017 to 65 in 2018. 'Lost Control' was the second-most cited factor in both years, with a stable count of 12 crashes. The number of crashes attributed to 'Driving too fast for conditions' tripled from 2 to 6, and 'Ran off road - straight' also increased from 2 to 6 incidents.

Officer-Reported Primary Contributing Cause

Animal65 (48.1%)↑27.5%prior 51
Lost Control12 (8.9%)→0.0%prior 12
Other (explain in narrative): Other7 (5.2%)↓-12.5%prior 8
FTYROW: From stop sign6 (4.4%)↑20.0%prior 5
Ran off road - straight6 (4.4%)
Driving too fast for conditions6 (4.4%)
Followed too close5 (3.7%)
Ran off road - left4 (3%)
Operating vehicle in an reckless, erratic, careless, negligent manner4 (3%)
Swerving/Evasive Action3 (2.2%)

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

Road & Environmental Conditions

The most notable shift in crash conditions was related to road surface. The proportion of crashes occurring on wet roads increased from 6.8% in 2017 to 12.9% in 2018 among crashes with documented surface conditions. Correspondingly, the share of incidents on dry surfaces decreased from 80.8% to 66.7%. Proportions of crashes by weather and lighting conditions remained largely consistent between the two years.

Weather

Clear67 (72.0%)
↑31.4%prior 51
Cloudy12 (12.9%)
↓-14.3%prior 14
Snow6 (6.5%)
Rain4 (4.3%)
Freezing rain/drizzle2 (2.2%)
Fog, smoke, smog2 (2.2%)

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

Lighting

Daylight50 (53.8%)
↑8.7%prior 46
Dark - roadway not lighted20 (21.5%)
↑25.0%prior 16
Dawn13 (14.0%)
↑116.7%prior 6
Dark - roadway lighted7 (7.5%)
Dusk3 (3.2%)

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

Road Surface

Dry62 (66.7%)
↑5.1%prior 59
Wet12 (12.9%)
↑140.0%prior 5
Gravel7 (7.5%)
Snow7 (7.5%)
Ice/frost4 (4.3%)
Slush1 (1.1%)

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

Vehicles & Demographics

The top three vehicle makes involved in crashes were consistent, but their ranking changed. In 2018, Chevrolet became the most frequent make with 41 vehicles, surpassing Ford with 34; in 2017, Ford led with 41 vehicles to Chevrolet's 34. The age distribution of persons involved in crashes also shifted, with the 26-34 age group increasing from 19 individuals in 2017 to 39 in 2018. Conversely, the number of persons in the 65+ age group decreased from 27 to 19.

Top Vehicle Makes (173 vehicles)

1
FORD34 (19.7%)
↓-17.1%prior 41
2
CHEV28 (16.2%)
↑27.3%prior 22
3
DODG14 (8.1%)
↑75.0%prior 8
4
CHEVROLET13 (7.5%)
↑8.3%prior 12
5
GMC9 (5.2%)
6
JEEP7 (4%)
↑40.0%prior 5
7
NISS6 (3.5%)
8
BUIC6 (3.5%)
9
KIA4 (2.3%)
10
TOYOTA4 (2.3%)

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

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

Sex Distribution (118 persons with recorded sex)

Male64 (54.2%)
↓-7.2%prior 69
Female54 (45.8%)
↑12.5%prior 48

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: 135
  • Total persons involved: 218
  • Total vehicles involved: 173

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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