Yearly Traffic Safety Analysis

228 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Cass County recorded 228 total crashes, a 1.7% decrease from the 232 crashes documented in 2016. The most significant year-over-year change was a substantial drop in traffic fatalities, which fell from 14 in 2016 to 1 in 2017. Total injuries also decreased from 105 to 71 during the same period.

228

▼ -1.7%was 232

Total Crash Events

1

▼ -92.9%was 14

Persons Killed

71

▼ -32.4%was 105

Persons Injured

1

▼ -90.0%was 10

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

Overall, total crashes in Cass County remained relatively stable, decreasing by 1.7% from 232 in 2016 to 228 in 2017. However, the severity of these crashes saw a marked improvement, with total fatalities dropping from 14 to 1 and total injuries decreasing from 105 to 71 over the same period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 14↓-92.9%

0

Other Killed

Prior: 0→0.0%

2

Pedestrians Injured

Prior: 1↑100.0%

3

Cyclists Injured

Prior: 1↑200.0%

65

Motorists Injured

Prior: 103↓-36.9%

1

Other Injured

Prior: 0→%

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 shifted between 2016 and 2017. The peak day for crashes moved from Friday, with 49 incidents in 2016, to Wednesday, with 41 incidents in 2017. Similarly, the peak hour for collisions shifted earlier in the day, from 5 p.m. (22 crashes) in the prior year to 3 p.m. (19 crashes) in the current 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

Crash severity decreased significantly in 2017 compared to 2016. The number of fatal crashes fell from 10 to 1, and their share of all incidents dropped from 4.3% to 0.4%. The count of serious injury crashes also declined from 12 to 5. While the total number of crashes involving any level of injury remained unchanged at 64, their proportion of total crashes was slightly higher in 2017 at 28.1% versus 27.6% in 2016.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
↓-90.0%prior 10
Serious Injury5serious injury crashes2.2%
↓-58.3%prior 12
Minor Injury30minor injury crashes13.2%
↑36.4%prior 22
Possible Injury29possible injury crashes12.7%
↓-3.3%prior 30
No Injury163no injury crashes71.5%
↑3.2%prior 158

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 periods, with the count increasing from 49 in 2016 to 53 in 2017. Crashes attributed to 'Lost Control' decreased from 34 to 20, while incidents of 'Ran off road - straight' increased from 14 to 23. 'Followed too close' as a factor also saw an increase in count from 14 to 18. The ranking of top factors shifted, with 'Lost Control' moving from the second to the third position in 2017.

Officer-Reported Primary Contributing Cause

Animal53 (23.2%)↑8.2%prior 49
Ran off road - straight23 (10.1%)↑64.3%prior 14
Lost Control20 (8.8%)↓-41.2%prior 34
Followed too close18 (7.9%)↑28.6%prior 14
Other (explain in narrative): Other17 (7.5%)↑88.9%prior 9
Operating vehicle in an reckless, erratic, careless, negligent manner9 (3.9%)
Driving too fast for conditions9 (3.9%)↓-43.8%prior 16
FTYROW: From stop sign9 (3.9%)↓-10.0%prior 10
Ran off road - left7 (3.1%)↓-12.5%prior 8
Driver Distraction: Other interior distraction7 (3.1%)↑40.0%prior 5

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 and daylight conditions decreased in 2017 compared to 2016. In 2017, 50.4% of crashes occurred in clear weather, down from 61.2% in the prior year, while daylight crashes accounted for 48.7% of the total, down from 55.6%. Crashes on icy or frosty roads saw a notable reduction, falling from 22 incidents in 2016 to 8 in 2017. Conversely, the share of crashes in cloudy weather and on wet road surfaces increased.

Weather

Clear115 (61.8%)
↓-19.0%prior 142
Cloudy42 (22.6%)
↑44.8%prior 29
Snow13 (7.0%)
↑85.7%prior 7
Rain6 (3.2%)
→0.0%prior 6
Blowing Snow5 (2.7%)
↓-28.6%prior 7
Freezing rain/drizzle3 (1.6%)
↓-40.0%prior 5
Other (explain in narrative)1 (0.5%)
Fog, smoke, smog1 (0.5%)

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

Lighting

Daylight111 (59.4%)
↓-14.0%prior 129
Dark - roadway not lighted50 (26.7%)
↑6.4%prior 47
Dark - roadway lighted18 (9.6%)
↑12.5%prior 16
Dawn5 (2.7%)
↓-16.7%prior 6
Dusk2 (1.1%)
Dark - unknown roadway lighting1 (0.5%)

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

Road Surface

Dry139 (74.3%)
↓-8.6%prior 152
Wet19 (10.2%)
↑26.7%prior 15
Snow14 (7.5%)
↑55.6%prior 9
Ice/frost8 (4.3%)
↓-63.6%prior 22
Gravel4 (2.1%)
Slush2 (1.1%)
Other (explain in narrative)1 (0.5%)

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

Vehicles & Demographics

Ford and Chevrolet remained the most common vehicle makes involved in crashes in both periods; Ford counts decreased slightly from 56 to 54, while combined Chevrolet models increased from 64 to 72. An analysis of persons involved shows a shift in age demographics, as the 16-20 age group saw an increase from 42 to 51 individuals. Conversely, the number of persons in the 55-64 and 65+ age groups involved in crashes decreased from 66 to 50 and 60 to 46, respectively.

Top Vehicle Makes (335 vehicles)

1
FORD54 (16.1%)
↓-3.6%prior 56
2
CHEV49 (14.6%)
↑28.9%prior 38
3
CHEVROLET23 (6.9%)
↓-11.5%prior 26
4
DODG19 (5.7%)
↑72.7%prior 11
5
DODGE15 (4.5%)
↓-21.1%prior 19
6
FREIGHTLINER12 (3.6%)
↑71.4%prior 7
7
CHRY10 (3%)
↑25.0%prior 8
8
JEEP9 (2.7%)
→0.0%prior 9
9
PONT8 (2.4%)
→0.0%prior 8
10
HONDA8 (2.4%)
↓-20.0%prior 10

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

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

Sex Distribution (252 persons with recorded sex)

Male165 (65.5%)
↑1.2%prior 163
Female87 (34.5%)
↓-11.2%prior 98

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: 228
  • Total persons involved: 391
  • Total vehicles involved: 335

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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