Yearly Traffic Safety Analysis

291 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Mahaska County, total traffic crashes decreased from 321 in 2017 to 291 in 2018, a 9.3% reduction. The most significant change was the elimination of fatal crashes, which dropped from one in the prior year to zero in the current year. Overall injuries remained nearly stable, with 108 injuries recorded in 2018 compared to 106 in 2017.

291

▼ -9.3%was 321

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

108

▲ 1.9%was 106

Persons Injured

0

▼ -100.0%was 1

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

Traffic crashes in Mahaska County showed a downward trend year-over-year, falling by 9.3% from 321 incidents in 2017 to 291 in 2018. While the total number of crashes decreased, the number of resulting injuries saw a slight increase of 1.9%, from 106 to 108. Fatalities decreased from one in the prior year to zero in the current period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 1↓-100.0%

1

Pedestrians Injured

Prior: 4↓-75.0%

2

Cyclists Injured

Prior: 0→%

105

Motorists Injured

Prior: 102↑2.9%

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 temporal patterns of crashes in Mahaska County remained largely consistent year-over-year. The peak hour for crashes in both 2018 and 2017 was the 4 p.m. hour, with incidents increasing from 28 to 31. Wednesday was the peak day for crashes in 2018 with 52 incidents, consistent with the prior year where it tied with Friday as the peak day with 53 crashes each.

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 notable improvement, with fatal crashes decreasing from one in 2017 to zero in 2018. The number of serious injury crashes increased slightly from 5 to 7, while minor injury crashes fell from 33 to 19. Crashes resulting in possible injury increased from 47 to 59. The proportion of crashes with no injuries decreased from 73.2% in 2017 to 70.8% in 2018.

Outcome by Severity (Crash Events)

Serious Injury7serious injury crashes2.4%
↑40.0%prior 5
Minor Injury19minor injury crashes6.5%
↓-42.4%prior 33
Possible Injury59possible injury crashes20.3%
↑25.5%prior 47
No Injury206no injury crashes70.8%
↓-12.3%prior 235

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

The leading contributing factors shifted between 2017 and 2018. In 2018, the most common factor was "Failure to Yield Right of Way from a stop sign" with 31 incidents, an increase from 24 in the previous year. Crashes attributed to "Followed too close" also rose, from 17 to 25. Conversely, collisions involving animals saw a significant 50% reduction in count, dropping from 32 incidents in 2017 to 16 in 2018. "Lost Control" as a factor also decreased from 27 to 19 crashes.

Officer-Reported Primary Contributing Cause

FTYROW: From stop sign31 (10.7%)↑29.2%prior 24
Followed too close25 (8.6%)↑47.1%prior 17
FTYROW: Making left turn25 (8.6%)→0.0%prior 25
Other (explain in narrative): Other24 (8.2%)↓-38.5%prior 39
Lost Control19 (6.5%)↓-29.6%prior 27
Animal16 (5.5%)↓-50.0%prior 32
Ran off road - straight14 (4.8%)↑7.7%prior 13
Driving too fast for conditions12 (4.1%)↑50.0%prior 8
Ran off road - left11 (3.8%)↑22.2%prior 9
FTYROW: Other (explain in narrative)10 (3.4%)↑66.7%prior 6

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

Road & Environmental Conditions

Crashes under clear weather and on dry roads constituted the majority in both periods, with their proportions remaining relatively stable year-over-year. A notable shift occurred in lighting conditions; the number of crashes during daylight hours increased from 194 in 2017 to 211 in 2018. Consequently, the share of crashes occurring in daylight grew from 60.4% to 72.5% of all incidents. Crashes in dark conditions, both lighted and unlighted, saw a combined decrease from 83 incidents in 2017 to 52 in 2018.

Weather

Clear186 (66.4%)
↓-5.1%prior 196
Cloudy67 (23.9%)
↑3.1%prior 65
Snow10 (3.6%)
↓-9.1%prior 11
Rain7 (2.5%)
↓-30.0%prior 10
Freezing rain/drizzle4 (1.4%)
Fog, smoke, smog2 (0.7%)
Blowing Snow2 (0.7%)
Other (explain in narrative)1 (0.4%)
Sleet, hail1 (0.4%)

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

Lighting

Daylight211 (75.1%)
↑8.8%prior 194
Dark - roadway not lighted30 (10.7%)
↓-34.8%prior 46
Dark - roadway lighted22 (7.8%)
↓-40.5%prior 37
Dusk9 (3.2%)
↓-18.2%prior 11
Dawn8 (2.8%)
↑14.3%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

Dry218 (77.6%)
↓-6.8%prior 234
Wet29 (10.3%)
↑3.6%prior 28
Snow16 (5.7%)
↓-27.3%prior 22
Ice/frost7 (2.5%)
→0.0%prior 7
Slush5 (1.8%)
Gravel5 (1.8%)
Mud, dirt1 (0.4%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Chevrolet, Ford, and Dodge vehicles comprising the top three in both years. The number of vehicles from these top makes involved in crashes decreased year-over-year: Chevrolet from 132 to 97, Ford from 88 to 83, and Dodge from 52 to 42. Examining the age distribution of all persons involved, there was a decrease in the 16-20 age group (from 88 to 76 persons) and an increase in the 26-34 age group (from 73 to 84 persons).

Top Vehicle Makes (507 vehicles)

1
FORD83 (16.4%)
↓-5.7%prior 88
2
CHEV73 (14.4%)
↓-8.8%prior 80
3
DODG32 (6.3%)
↑14.3%prior 28
4
BUIC27 (5.3%)
↑145.5%prior 11
5
TOYT25 (4.9%)
↑38.9%prior 18
6
CHEVROLET24 (4.7%)
↓-53.8%prior 52
7
PONT17 (3.4%)
↑13.3%prior 15
8
JEEP17 (3.4%)
↓-15.0%prior 20
9
CHRY14 (2.8%)
↑100.0%prior 7
10
GMC14 (2.8%)
→0.0%prior 14

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

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

Sex Distribution (436 persons with recorded sex)

Male256 (58.7%)
↑5.8%prior 242
Female180 (41.3%)
↑1.1%prior 178

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: 291
  • Total persons involved: 595
  • Total vehicles involved: 507

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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