Yearly Traffic Safety Analysis

318 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Henry County, total vehicle crashes decreased from 345 in 2017 to 318 in 2018, a 7.8% reduction. Despite the overall drop in collisions, the number of fatalities doubled from two to four during the same period. This increase in fatal outcomes represents the most significant year-over-year shift in the county's crash data.

318

▼ -7.8%was 345

Total Crash Events

4

▲ 100.0%was 2

Persons Killed

94

▼ -4.1%was 98

Persons Injured

4

▲ 100.0%was 2

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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

The overall trend in Henry County shows a decrease in traffic crashes year-over-year. Collisions fell by 7.8%, from 345 incidents in 2017 to 318 in 2018. However, this positive trend in crash volume was countered by a negative trend in severity, as total fatalities rose from two to four, and the number of fatal crashes doubled from two to four.

Vulnerable Road User Casualties

4

Motorists Killed

Prior: 2↑100.0%

94

Motorists Injured

Prior: 96↓-2.1%

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

Temporal crash patterns shifted between the two periods. In 2018, the peak day for crashes was Friday with 60 incidents, moving from Saturday which saw the most crashes (59) in 2017. The peak hour also shifted later into the evening, from the 5 p.m. hour (28 crashes) in 2017 to the 9 p.m. hour (26 crashes) in 2018.

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

While total crashes declined, the severity of crashes worsened from 2017 to 2018. The number of fatal crashes doubled from two to four, and the fatal crash rate increased from 0.58 to 1.26 per 100 crashes. The share of crashes resulting in minor injuries decreased from 11.6% (40 crashes) to 7.9% (25 crashes), while the proportion of crashes with possible injuries increased slightly from 9.3% to 10.7%.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.3%
↑100.0%prior 2
Serious Injury8serious injury crashes2.5%
↑14.3%prior 7
Minor Injury25minor injury crashes7.9%
↓-37.5%prior 40
Possible Injury34possible injury crashes10.7%
↑6.3%prior 32
No Injury247no injury crashes77.7%
↓-6.4%prior 264

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 years, with nearly identical counts of 123 in 2018 and 124 in 2017. However, the counts for other leading factors decreased significantly; crashes attributed to 'Lost Control' dropped from 37 to 17, and incidents involving 'Failure to Yield Right of Way from a stop sign' fell from 22 to 12. 'Driving too fast for conditions' rose in the rankings to become the third most common factor in 2018 with 15 incidents, up from 11 the prior year.

Officer-Reported Primary Contributing Cause

Animal123 (38.7%)↓-0.8%prior 124
Lost Control17 (5.3%)↓-54.1%prior 37
Driving too fast for conditions15 (4.7%)↑36.4%prior 11
Followed too close15 (4.7%)↑36.4%prior 11
Ran off road - straight14 (4.4%)↓-6.7%prior 15
FTYROW: From stop sign12 (3.8%)↓-45.5%prior 22
Ran Stop Sign12 (3.8%)↑33.3%prior 9
Other (explain in narrative): Other10 (3.1%)↑100.0%prior 5
Ran off road - left10 (3.1%)→0.0%prior 10
Driver Distraction: Other interior distraction9 (2.8%)↓-25.0%prior 12

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

Road & Environmental Conditions

Year-over-year, there was a notable decrease in crashes occurring in 'Dark - roadway not lighted' conditions, which fell from 85 incidents in 2017 to 55 in 2018. Conversely, crashes on roads with snow or ice increased. Collisions on snowy surfaces rose from 8 to 14, and crashes on icy or frosty roads increased from 6 to 10, suggesting a higher number of incidents occurred during adverse winter conditions in 2018 compared to the previous year.

Weather

Clear160 (69.0%)
↓-19.2%prior 198
Cloudy37 (15.9%)
↓-7.5%prior 40
Rain12 (5.2%)
↓-14.3%prior 14
Snow11 (4.7%)
↑120.0%prior 5
Freezing rain/drizzle8 (3.4%)
Blowing Snow2 (0.9%)
Fog, smoke, smog2 (0.9%)

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

Lighting

Daylight150 (64.7%)
↓-1.3%prior 152
Dark - roadway not lighted55 (23.7%)
↓-35.3%prior 85
Dark - roadway lighted15 (6.5%)
→0.0%prior 15
Dawn8 (3.4%)
↑33.3%prior 6
Dusk3 (1.3%)
↓-57.1%prior 7
Dark - unknown roadway lighting1 (0.4%)

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

Road Surface

Dry176 (75.9%)
↓-16.2%prior 210
Wet22 (9.5%)
↓-12.0%prior 25
Snow14 (6.0%)
↑75.0%prior 8
Ice/frost10 (4.3%)
↑66.7%prior 6
Gravel7 (3.0%)
↓-58.8%prior 17
Slush2 (0.9%)
Other (explain in narrative)1 (0.4%)

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

Vehicles & Demographics

The top two vehicle makes involved in crashes swapped positions between periods; Chevrolet was most frequent in 2018 with 96 vehicles, while Ford was most frequent in 2017 with 81 vehicles (compared to 107 Chevrolets). There was a significant shift in the age distribution of persons involved in crashes. The 16-20 age group saw its involvement decrease from 86 individuals in 2017 to 53 in 2018, while the 35-44 age group's involvement increased from 66 to 94 persons.

Top Vehicle Makes (444 vehicles)

1
CHEV67 (15.1%)
↓-1.5%prior 68
2
FORD64 (14.4%)
↓-21.0%prior 81
3
CHEVROLET29 (6.5%)
↓-25.6%prior 39
4
DODG27 (6.1%)
↑28.6%prior 21
5
TOYT20 (4.5%)
↑33.3%prior 15
6
DODGE16 (3.6%)
↓-20.0%prior 20
7
JEEP14 (3.2%)
8
KIA13 (2.9%)
↑85.7%prior 7
9
GMC13 (2.9%)
↓-40.9%prior 22
10
CHRY13 (2.9%)
↑8.3%prior 12

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

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

Sex Distribution (338 persons with recorded sex)

Male217 (64.2%)
↓-6.1%prior 231
Female121 (35.8%)
↓-12.9%prior 139

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: 318
  • Total persons involved: 566
  • Total vehicles involved: 444

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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