Yearly Traffic Safety Analysis

746 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Marshall County, total crashes increased from 686 in 2017 to 746 in 2018, an 8.8% rise. Despite the increase in total collisions, the number of fatalities saw a significant decrease, falling from 12 in the prior year to 4 in the current year. This shift towards less severe outcomes occurred even as the overall crash volume grew.

746

▲ 8.7%was 686

Total Crash Events

4

▼ -66.7%was 12

Persons Killed

198

▼ -9.6%was 219

Persons Injured

4

▼ -63.6%was 11

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

Overall, the total number of crashes in Marshall County increased by 8.8% from 2017 to 2018, rising from 686 to 746 incidents. However, the outcomes of these crashes became less severe, with total injuries declining by 9.6% from 219 to 198, and fatalities dropping by 66.7% from 12 to 4.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 11↓-63.6%

8

Pedestrians Injured

Prior: 5↑60.0%

4

Cyclists Injured

Prior: 3↑33.3%

186

Motorists Injured

Prior: 211↓-11.8%

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 remained consistent year-over-year, with Friday being the peak day and the 3 p.m. hour being the peak hour in both periods. The crash count during these peak times increased, with Friday crashes rising from 119 to 142 and 3 p.m. hour crashes increasing from 63 to 85. A notable increase was also observed on Tuesdays, where crash counts rose from 79 in 2017 to 118 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

The severity of crashes decreased from 2017 to 2018, with the fatal crash rate falling from 1.6% to 0.5%. Crashes resulting in a fatality dropped from 11 to 4, and serious injury crashes decreased from 17 to 12. Consequently, the proportion of crashes with no reported injuries increased, rising from 72.7% of all incidents in 2017 to 77.6% in 2018.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.5%
↓-63.6%prior 11
Serious Injury12serious injury crashes1.6%
↓-29.4%prior 17
Minor Injury68minor injury crashes9.1%
↓-10.5%prior 76
Possible Injury83possible injury crashes11.1%
→0.0%prior 83
No Injury579no injury crashes77.6%
↑16.0%prior 499

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 an animal remained the leading contributing factor in both periods, with the count increasing from 133 crashes in 2017 to 156 in 2018. 'Failure to yield from a stop sign' was the second-leading factor in 2018 with 51 crashes, a slight increase from 48 crashes in the prior year. Conversely, crashes attributed to 'Lost Control' decreased in count from 57 to 41, and 'Ran Stop Sign' incidents fell from 45 to 32.

Officer-Reported Primary Contributing Cause

Animal156 (20.9%)↑17.3%prior 133
FTYROW: From stop sign51 (6.8%)↑6.3%prior 48
Other (explain in narrative): Other42 (5.6%)↑44.8%prior 29
Lost Control41 (5.5%)↓-28.1%prior 57
Followed too close37 (5%)↑8.8%prior 34
FTYROW: Making left turn37 (5%)↓-7.5%prior 40
Driving too fast for conditions35 (4.7%)↑84.2%prior 19
Ran Stop Sign32 (4.3%)↓-28.9%prior 45
Ran off road - left30 (4%)↑15.4%prior 26
Ran Traffic Signal27 (3.6%)↑3.8%prior 26

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

Road & Environmental Conditions

Crash conditions remained broadly similar between 2017 and 2018, with the majority of incidents in both years occurring in daylight, during clear weather, and on dry roads. The number of crashes on dry roads was nearly unchanged at 442 in 2018 compared to 444 in 2017, though this represented a smaller share of the higher total crash volume (59.2% vs. 64.7%). Crashes in daylight and clear weather increased in count, consistent with the overall rise in total incidents.

Weather

Clear417 (68.4%)
↑4.5%prior 399
Cloudy130 (21.3%)
↑18.2%prior 110
Snow22 (3.6%)
↓-8.3%prior 24
Rain18 (3.0%)
↓-10.0%prior 20
Freezing rain/drizzle11 (1.8%)
↑37.5%prior 8
Fog, smoke, smog4 (0.7%)
↓-20.0%prior 5
Severe Winds3 (0.5%)
Blowing Snow2 (0.3%)
Sleet, hail2 (0.3%)
Other (explain in narrative)1 (0.2%)

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

Lighting

Daylight418 (68.4%)
↑16.1%prior 360
Dark - roadway lighted94 (15.4%)
↑22.1%prior 77
Dark - roadway not lighted72 (11.8%)
↓-25.0%prior 96
Dawn14 (2.3%)
↓-26.3%prior 19
Dusk10 (1.6%)
↓-37.5%prior 16
Dark - unknown roadway lighting3 (0.5%)
↓-50.0%prior 6

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

Road Surface

Dry442 (72.6%)
↓-0.5%prior 444
Wet58 (9.5%)
↑9.4%prior 53
Snow47 (7.7%)
↑20.5%prior 39
Gravel25 (4.1%)
↑47.1%prior 17
Ice/frost18 (3.0%)
↑12.5%prior 16
Slush15 (2.5%)
Mud, dirt3 (0.5%)
Other (explain in narrative)1 (0.2%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes, led by Chevrolet and Ford, remained consistent between 2017 and 2018. An analysis of persons involved in crashes reveals a notable increase in younger age groups; the number of individuals aged 16-20 rose from 130 to 189, and those aged 21-25 increased from 108 to 161. The involvement of most other age groups saw more modest changes.

Top Vehicle Makes (1,205 vehicles)

1
CHEV214 (17.8%)
↑25.1%prior 171
2
FORD207 (17.2%)
↑21.1%prior 171
3
DODG81 (6.7%)
↑22.7%prior 66
4
CHEVROLET58 (4.8%)
↓-9.4%prior 64
5
HOND54 (4.5%)
↑3.8%prior 52
6
TOYT52 (4.3%)
↑2.0%prior 51
7
CHRY47 (3.9%)
↑42.4%prior 33
8
JEEP46 (3.8%)
↑35.3%prior 34
9
GMC40 (3.3%)
↓-7.0%prior 43
10
NISS33 (2.7%)
↑120.0%prior 15

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

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

Sex Distribution (974 persons with recorded sex)

Male560 (57.5%)
↑16.4%prior 481
Female414 (42.5%)
↑18.6%prior 349

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: 746
  • Total persons involved: 1,479
  • Total vehicles involved: 1,205

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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