Yearly Traffic Safety Analysis

494 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Marion County recorded 494 total crashes, a 12.6% decrease from the 565 crashes documented in 2016. This overall reduction in crashes was accompanied by a drop in crash severity, with total fatalities decreasing from 3 to 1 and total injuries falling from 163 to 151. The most significant contributing factor in both years was collisions with animals.

494

▼ -12.6%was 565

Total Crash Events

1

▼ -66.7%was 3

Persons Killed

151

▼ -7.4%was 163

Persons Injured

1

▼ -66.7%was 3

Fatal Crash Events

Note: "Persons Killed" (1) 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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Marion County showed a notable decrease year-over-year. Total crashes fell by 12.6% from 565 in 2016 to 494 in 2017. This downward trend was also reflected in crash outcomes, with total injuries declining by 7.4% from 163 to 151, and fatalities dropping from 3 to 1.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 3↓-66.7%

1

Pedestrians Injured

Prior: 4↓-75.0%

150

Motorists Injured

Prior: 157↓-4.5%

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 temporal patterns of crashes showed some shifts between the two periods. While Friday remained the peak day for crashes in both 2017 (84 crashes) and 2016 (97 crashes), the peak hour for incidents shifted from 3 p.m. in 2016 (47 crashes) to 6 p.m. in 2017 (38 crashes). Crash volumes on Wednesday grew to become the second-highest day in 2017 with 81 crashes, a shift from 2016 when Monday was the second-busiest day with 90 crashes.

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

The severity of crashes shifted year-over-year, with the fatal crash rate decreasing from 0.5% of all crashes in 2016 to 0.2% in 2017. However, the proportion of crashes resulting in serious injuries increased, rising from 1.8% of all incidents in 2016 (10 crashes) to 3.4% in 2017 (17 crashes). Crashes resulting in no injury remained the largest category, accounting for approximately 75% of all incidents in both years.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.2%
↓-66.7%prior 3
Serious Injury17serious injury crashes3.4%
↑70.0%prior 10
Minor Injury41minor injury crashes8.3%
↓-22.6%prior 53
Possible Injury65possible injury crashes13.2%
↓-9.7%prior 72
No Injury370no injury crashes74.9%
↓-13.3%prior 427

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

The leading contributing factors for crashes remained consistent, with 'Animal' being the top factor in both 2017 (157 crashes) and 2016 (167 crashes). While the count for most top factors decreased, crashes attributed to 'Failure to Yield Right of Way: From stop sign' increased notably, rising from 19 incidents in 2016 to 34 in 2017. Conversely, crashes involving 'Driving too fast for conditions' saw a significant drop in count from 32 to 14.

Officer-Reported Primary Contributing Cause

Animal157 (31.8%)↓-6.0%prior 167
Lost Control46 (9.3%)↓-4.2%prior 48
FTYROW: From stop sign34 (6.9%)↑78.9%prior 19
Other (explain in narrative): Other28 (5.7%)↓-20.0%prior 35
Ran off road - straight25 (5.1%)↓-16.7%prior 30
Followed too close21 (4.3%)↓-38.2%prior 34
Operating vehicle in an reckless, erratic, careless, negligent manner21 (4.3%)↑90.9%prior 11
Ran off road - left14 (2.8%)↓-26.3%prior 19
Driving too fast for conditions14 (2.8%)↓-56.3%prior 32
Driver Distraction: Other interior distraction11 (2.2%)↓-26.7%prior 15

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

Road & Environmental Conditions

While most crashes in both years occurred in clear weather on dry roads, there was a notable shift in lighting conditions. The proportion of crashes happening in 'Dark - roadway not lighted' conditions increased from 11.7% of total crashes in 2016 to 21.7% in 2017. Concurrently, the share of crashes occurring during daylight hours decreased from 53.5% to 46.0%. The share of incidents on snowy or icy road surfaces also declined, from 8.1% in 2016 to 5.3% in 2017.

Weather

Clear272 (69.4%)
↓-4.9%prior 286
Cloudy83 (21.2%)
↓-7.8%prior 90
Rain19 (4.8%)
↑18.8%prior 16
Snow6 (1.5%)
↓-79.3%prior 29
Fog, smoke, smog5 (1.3%)
Freezing rain/drizzle4 (1.0%)
Blowing Snow2 (0.5%)
Severe Winds1 (0.3%)

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

Lighting

Daylight227 (57.5%)
↓-24.8%prior 302
Dark - roadway not lighted107 (27.1%)
↑62.1%prior 66
Dark - roadway lighted35 (8.9%)
→0.0%prior 35
Dusk17 (4.3%)
↓-10.5%prior 19
Dawn9 (2.3%)
↓-25.0%prior 12

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

Road Surface

Dry310 (78.9%)
↓-4.9%prior 326
Wet37 (9.4%)
→0.0%prior 37
Gravel19 (4.8%)
→0.0%prior 19
Ice/frost13 (3.3%)
↓-18.8%prior 16
Snow12 (3.1%)
↓-52.0%prior 25
Other (explain in narrative)1 (0.3%)
Slush1 (0.3%)
↓-80.0%prior 5

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford (156 vehicles) and Chevrolet (150 vehicles, combining 'CHEV' and 'CHEVROLET') leading in 2017, similar to the prior year. An analysis of persons involved shows a shift in age demographics, with the proportion of individuals in the 26-34 age group increasing from 13.7% of persons in 2016 to 15.6% in 2017. The share of persons in the 65+ age group also grew from 10.5% to 13.3%, while the 45-54 age group's representation decreased from 14.5% to 10.7%.

Top Vehicle Makes (726 vehicles)

1
FORD156 (21.5%)
↑0.6%prior 155
2
CHEV102 (14%)
↑14.6%prior 89
3
CHEVROLET48 (6.6%)
↓-48.4%prior 93
4
DODG38 (5.2%)
↓-19.1%prior 47
5
JEEP26 (3.6%)
↑4.0%prior 25
6
PONT24 (3.3%)
↓-7.7%prior 26
7
GMC23 (3.2%)
↓-20.7%prior 29
8
TOYT23 (3.2%)
↑27.8%prior 18
9
CHRY23 (3.2%)
10
BUIC22 (3%)
↑83.3%prior 12

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

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

Sex Distribution (560 persons with recorded sex)

Male323 (57.7%)
↓-16.3%prior 386
Female237 (42.3%)
↓-12.2%prior 270

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: 494
  • Total persons involved: 852
  • Total vehicles involved: 726

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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