Yearly Traffic Safety Analysis

245 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Cass County, total traffic crashes increased by 7.5% from 228 in 2017 to 245 in 2018. The most significant year-over-year change was a sharp rise in traffic fatalities, which increased from one person killed in 2017 to five in 2018. Consequently, the number of fatal crashes also rose from one to five during the same period.

245

▲ 7.5%was 228

Total Crash Events

5

▲ 400.0%was 1

Persons Killed

97

▲ 36.6%was 71

Persons Injured

5

▲ 400.0%was 1

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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 safety trends in Cass County worsened from 2017 to 2018. Total crashes rose by 7.5% from 228 to 245. This was accompanied by a 36.6% increase in injuries, from 71 to 97, and a 400% increase in fatalities, from one to five.

Vulnerable Road User Casualties

1

Cyclists Killed

Prior: 0→%

4

Motorists Killed

Prior: 1↑300.0%

0

Cyclists Injured

Prior: 3↓-100.0%

97

Motorists Injured

Prior: 65↑49.2%

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 timing of crashes shifted between the two periods. In 2018, the peak day for crashes was Friday with 58 incidents, a change from 2017 when Wednesday was the peak day with 41 incidents. The most frequent crash hour also moved from the afternoon commute (3 p.m.) in 2017 to the morning commute (8 a.m.) 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

Crash severity increased notably in 2018 compared to the prior year. The number of fatal crashes increased from one to five, raising the fatal crash rate from 0.4% to 2.0% of all crashes. The count of serious injury crashes also grew from five to eight. While crashes resulting in minor injuries decreased from 30 to 25, the combined total of all injury and fatal crashes rose from 65 in 2017 to 75 in 2018.

Outcome by Severity (Crash Events)

Fatal5fatal crashes2%
↑400.0%prior 1
Serious Injury8serious injury crashes3.3%
↑60.0%prior 5
Minor Injury25minor injury crashes10.2%
↓-16.7%prior 30
Possible Injury37possible injury crashes15.1%
↑27.6%prior 29
No Injury170no injury crashes69.4%
↑4.3%prior 163

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 animals remained the leading contributing factor in both years, though the count decreased from 53 in 2017 to 45 in 2018. The most significant change was the increase in crashes attributed to a driver losing control, which more than doubled from 20 incidents in 2017 to 44 in 2018, making it the second-most common factor. Crashes involving 'driving too fast for conditions' and 'failure to yield from a stop sign' also increased substantially, with each factor rising from 9 crashes in 2017 to 17 in 2018.

Officer-Reported Primary Contributing Cause

Animal45 (18.4%)↓-15.1%prior 53
Lost Control44 (18%)↑120.0%prior 20
Ran off road - straight18 (7.3%)↓-21.7%prior 23
FTYROW: From stop sign17 (6.9%)↑88.9%prior 9
Driving too fast for conditions17 (6.9%)↑88.9%prior 9
Followed too close15 (6.1%)↓-16.7%prior 18
Other (explain in narrative): Other11 (4.5%)↓-35.3%prior 17
Ran off road - left11 (4.5%)↑57.1%prior 7
FTYROW: Making left turn8 (3.3%)↑60.0%prior 5
Driver Distraction: Other interior distraction6 (2.4%)↓-14.3%prior 7

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 on adverse road surfaces increased significantly in 2018. Collisions on wet roads more than doubled from 19 to 39, and crashes on icy or frosty roads more than tripled from 8 to 25. Correspondingly, crashes during rain increased from 6 to 22 incidents, and snow-related crashes rose from 13 to 28. Conversely, crashes on dry road surfaces decreased from 139 in 2017 to 123 in 2018.

Weather

Clear117 (54.2%)
↑1.7%prior 115
Cloudy31 (14.4%)
↓-26.2%prior 42
Snow28 (13.0%)
↑115.4%prior 13
Rain22 (10.2%)
↑266.7%prior 6
Freezing rain/drizzle7 (3.2%)
Blowing Snow7 (3.2%)
↑40.0%prior 5
Fog, smoke, smog3 (1.4%)
Severe Winds1 (0.5%)

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

Lighting

Daylight136 (63.0%)
↑22.5%prior 111
Dark - roadway not lighted51 (23.6%)
↑2.0%prior 50
Dark - roadway lighted13 (6.0%)
↓-27.8%prior 18
Dusk8 (3.7%)
Dawn7 (3.2%)
↑40.0%prior 5
Dark - unknown roadway lighting1 (0.5%)

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

Road Surface

Dry123 (56.9%)
↓-11.5%prior 139
Wet39 (18.1%)
↑105.3%prior 19
Ice/frost25 (11.6%)
↑212.5%prior 8
Snow22 (10.2%)
↑57.1%prior 14
Slush3 (1.4%)
Gravel3 (1.4%)
Other (explain in narrative)1 (0.5%)

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

Vehicles & Demographics

The primary vehicle makes involved in crashes, Ford and Chevrolet, saw their numbers increase from 2017 to 2018, while Dodge-related incidents decreased. Analysis of persons involved in crashes shows a notable demographic shift, with a substantial increase in the number of individuals in the 35-44 age group, which nearly doubled from 42 in 2017 to 80 in 2018. The 26-34 age group also saw a significant increase, from 50 to 72 persons involved.

Top Vehicle Makes (372 vehicles)

1
FORD59 (15.9%)
↑9.3%prior 54
2
CHEV53 (14.2%)
↑8.2%prior 49
3
CHEVROLET27 (7.3%)
↑17.4%prior 23
4
DODG19 (5.1%)
→0.0%prior 19
5
FREIGHTLINER14 (3.8%)
↑16.7%prior 12
6
CHRY11 (3%)
↑10.0%prior 10
7
TOYT10 (2.7%)
8
VOLVO9 (2.4%)
↑80.0%prior 5
9
BUIC9 (2.4%)
↑12.5%prior 8
10
KIA9 (2.4%)

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

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

Sex Distribution (291 persons with recorded sex)

Male178 (61.2%)
↑7.9%prior 165
Female113 (38.8%)
↑29.9%prior 87

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: 245
  • Total persons involved: 464
  • Total vehicles involved: 372

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

ThatCarHitMe.com · An Injuria.ai Company