Yearly Traffic Safety Analysis

686 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Marshall County recorded 686 total traffic crashes, representing a 5.0% decrease from the 722 crashes reported in 2016. While overall collisions and injuries (219, down from 226) declined, the most significant year-over-year change was a sharp increase in fatalities, which rose from 4 in 2016 to 12 in 2017.

686

▼ -5.0%was 722

Total Crash Events

12

▲ 200.0%was 4

Persons Killed

219

▼ -3.1%was 226

Persons Injured

11

▲ 175.0%was 4

Fatal Crash Events

Note: "Persons Killed" (12) counts individual fatalities across all crash events. "Fatal" in the severity table below (11) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in Marshall County shows a decrease in total crashes, which fell by 5.0% from 722 in 2016 to 686 in 2017. Total injuries also saw a modest decline of 3.1%, from 226 to 219. However, this downward trend in crash volume was contrasted by a 200% increase in fatalities, which grew from 4 to 12 year-over-year.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

11

Motorists Killed

Prior: 4↑175.0%

5

Pedestrians Injured

Prior: 9↓-44.4%

3

Cyclists Injured

Prior: 5↓-40.0%

211

Motorists Injured

Prior: 212↓-0.5%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes in Marshall County showed consistency between the two periods. Friday remained the peak day for crashes, accounting for 119 incidents in 2017 and 116 in 2016. Similarly, the 3 p.m. hour was the most frequent time for collisions in both years, with 63 crashes in 2017 and 61 in the prior year.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Although total crashes decreased, their severity increased in 2017 compared to 2016. The number of fatal crashes more than doubled from 4 to 11, raising the fatal crash rate from 0.6% to 1.6% of all collisions. The proportion of crashes involving serious injuries also increased from 1.9% (14 crashes) to 2.5% (17 crashes), while the share of no-injury crashes declined from 75.9% to 72.7%.

Severity is per crash event (most severe injury). 11 fatal crash events resulted in 12 persons killed.

Outcome by Severity (Crash Events)

Fatal11fatal crashes1.6%
↑175.0%prior 4
Serious Injury17serious injury crashes2.5%
↑21.4%prior 14
Minor Injury76minor injury crashes11.1%
↑8.6%prior 70
Possible Injury83possible injury crashes12.1%
↓-3.5%prior 86
No Injury499no injury crashes72.7%
↓-8.9%prior 548

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both years, though the count decreased from 141 in 2016 to 133 in 2017. The top three factors were identical across both periods, including "Lost Control" and "FTYROW: From stop sign." A notable shift was the 32.4% increase in the count of crashes attributed to "Ran Stop Sign," which rose from 34 to 45 incidents. Conversely, crashes due to "Driving too fast for conditions" fell by 47.2% in count, from 36 to 19.

Officer-Reported Primary Contributing Cause

Animal133 (19.4%)↓-5.7%prior 141
Lost Control57 (8.3%)↓-3.4%prior 59
FTYROW: From stop sign48 (7%)↓-12.7%prior 55
Ran Stop Sign45 (6.6%)↑32.4%prior 34
FTYROW: Making left turn40 (5.8%)↑5.3%prior 38
Followed too close34 (5%)↓-17.1%prior 41
Ran off road - straight31 (4.5%)↑24.0%prior 25
Other (explain in narrative): Other29 (4.2%)↓-21.6%prior 37
Ran off road - left26 (3.8%)↑8.3%prior 24
Ran Traffic Signal26 (3.8%)→0.0%prior 26

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in clear weather increased from 52.2% in 2016 to 58.2% in 2017. There was a corresponding increase in the share of crashes happening in dark conditions, which rose from 22.7% (164 incidents) to 26.1% (179 incidents). Crashes on adverse road surfaces such as wet, snow, or ice were less frequent, decreasing from 135 incidents in 2016 to 112 in 2017.

Weather

Clear399 (69.6%)
↑5.8%prior 377
Cloudy110 (19.2%)
↓-29.5%prior 156
Snow24 (4.2%)
↑4.3%prior 23
Rain20 (3.5%)
↓-4.8%prior 21
Freezing rain/drizzle8 (1.4%)
↑14.3%prior 7
Fog, smoke, smog5 (0.9%)
Blowing Snow4 (0.7%)
↓-20.0%prior 5
Severe Winds2 (0.3%)
Other (explain in narrative)1 (0.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight360 (62.7%)
↓-9.8%prior 399
Dark - roadway not lighted96 (16.7%)
↑24.7%prior 77
Dark - roadway lighted77 (13.4%)
↓-9.4%prior 85
Dawn19 (3.3%)
↑35.7%prior 14
Dusk16 (2.8%)
↓-20.0%prior 20
Dark - unknown roadway lighting6 (1.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry444 (77.4%)
↓-0.7%prior 447
Wet53 (9.2%)
↓-8.6%prior 58
Snow39 (6.8%)
↑11.4%prior 35
Gravel17 (3.0%)
↑21.4%prior 14
Ice/frost16 (2.8%)
↓-50.0%prior 32
Slush4 (0.7%)
↓-60.0%prior 10
Mud, dirt1 (0.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

In 2017, Chevrolet and Ford vehicles were each involved in 171 crashes, a change from 2016 when Fords (198) were more prevalent than Chevrolets (147). Analysis of persons involved shows a notable decrease in the 16-20 age group (from 173 to 130) and the 21-25 age group (from 138 to 108). In contrast, involvement increased for individuals aged 26-34 (from 184 to 203) and 55-64 (from 150 to 171).

Top Vehicle Makes (1,109 vehicles)

1
CHEV171 (15.4%)
↑16.3%prior 147
2
FORD171 (15.4%)
↓-13.6%prior 198
3
DODG66 (6%)
↑34.7%prior 49
4
CHEVROLET64 (5.8%)
↓-37.3%prior 102
5
HOND52 (4.7%)
↑8.3%prior 48
6
TOYT51 (4.6%)
↑96.2%prior 26
7
GMC43 (3.9%)
↑22.9%prior 35
8
JEEP34 (3.1%)
↓-35.8%prior 53
9
BUIC34 (3.1%)
↑17.2%prior 29
10
DODGE33 (3%)
↓-35.3%prior 51

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (830 persons with recorded sex)

Male481 (58.0%)
↓-5.1%prior 507
Female349 (42.0%)
↓-5.4%prior 369

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 686
  • Total persons involved: 1,318
  • Total vehicles involved: 1,109

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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