Yearly Traffic Safety Analysis

1,033 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Cerro Gordo County, total vehicle crashes increased by 3.4% from 999 in 2017 to 1,033 in 2018. Despite this rise in overall incidents, the number of fatalities saw a significant year-over-year decrease, dropping from 12 in the prior period to 5 in the current period. The total number of injuries also declined from 351 to 299.

1,033

▲ 3.4%was 999

Total Crash Events

5

▼ -58.3%was 12

Persons Killed

299

▼ -14.8%was 351

Persons Injured

5

▼ -28.6%was 7

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

While the total number of crashes in Cerro Gordo County saw a slight increase of 3.4% from 2017 to 2018, the severity of these incidents decreased. Fatalities fell by 58.3% (from 12 to 5), and total injuries dropped by 14.8% (from 351 to 299). This indicates a shift towards a higher volume of less severe crashes in the most recent period.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 12↓-66.7%

7

Pedestrians Injured

Prior: 8↓-12.5%

7

Cyclists Injured

Prior: 12↓-41.7%

285

Motorists Injured

Prior: 331↓-13.9%

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 largely consistent year-over-year. Friday continued to be the peak day for crashes, with 187 incidents in 2018 compared to 174 in 2017. Similarly, the 5 p.m. hour remained the peak time for collisions in both periods, accounting for 93 crashes in 2018 and 87 in 2017, aligning with the evening commute.

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. Fatal crashes represented 0.5% of all incidents in 2018, down from 0.7% in 2017. Crashes resulting in serious or minor injuries also saw their share decline, with serious injury crashes falling from 2.0% to 1.2% and minor injury crashes dropping from 9.5% to 6.8%. Correspondingly, the proportion of crashes with no injuries rose from 72.5% to 76.1%.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.5%
↓-28.6%prior 7
Serious Injury12serious injury crashes1.2%
↓-40.0%prior 20
Minor Injury70minor injury crashes6.8%
↓-26.3%prior 95
Possible Injury160possible injury crashes15.5%
↑4.6%prior 153
No Injury786no injury crashes76.1%
↑8.6%prior 724

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

The leading contributing factors for crashes were consistent across both periods, with 'Animal' being the most cited cause in both 2018 (163 incidents) and 2017 (157 incidents). 'Followed too close' remained a top factor, increasing in count from 85 to 93 incidents. Notably, crashes attributed to 'Driving too fast for conditions' saw a 27.9% increase in count, rising from 61 incidents in 2017 to 78 in 2018.

Officer-Reported Primary Contributing Cause

Animal163 (15.8%)↑3.8%prior 157
Other (explain in narrative): Other95 (9.2%)↑14.5%prior 83
Followed too close93 (9%)↑9.4%prior 85
Driving too fast for conditions78 (7.6%)↑27.9%prior 61
FTYROW: From stop sign68 (6.6%)↑13.3%prior 60
Ran off road - left65 (6.3%)↓-11.0%prior 73
Lost Control42 (4.1%)↑7.7%prior 39
Ran Traffic Signal39 (3.8%)↑18.2%prior 33
FTYROW: Making left turn32 (3.1%)↑23.1%prior 26
Driver Distraction: Other interior distraction28 (2.7%)↑12.0%prior 25

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

Road & Environmental Conditions

While most crashes in both years occurred in clear weather and on dry roads, there was a notable shift in adverse condition crashes. Incidents on roads with snow, ice, or slush increased from 124 in 2017 to 181 in 2018, a 46% rise in count. Similarly, crashes reported during snowy weather conditions increased from 28 to 49. Crashes in daylight conditions remained the most common scenario in both periods, accounting for 62.9% of incidents in 2018 and 60.1% in 2017.

Weather

Clear562 (63.0%)
↑5.0%prior 535
Cloudy188 (21.1%)
↓-9.6%prior 208
Snow49 (5.5%)
↑75.0%prior 28
Rain46 (5.2%)
↓-6.1%prior 49
Freezing rain/drizzle19 (2.1%)
→0.0%prior 19
Blowing Snow17 (1.9%)
↑54.5%prior 11
Sleet, hail5 (0.6%)
Fog, smoke, smog4 (0.4%)
↓-33.3%prior 6
Severe Winds1 (0.1%)
Other (explain in narrative)1 (0.1%)

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

Lighting

Daylight650 (73.0%)
↑8.3%prior 600
Dark - roadway lighted125 (14.0%)
↑3.3%prior 121
Dark - roadway not lighted79 (8.9%)
↓-14.1%prior 92
Dusk19 (2.1%)
↓-29.6%prior 27
Dawn11 (1.2%)
↓-42.1%prior 19
Dark - unknown roadway lighting7 (0.8%)

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

Road Surface

Dry570 (63.9%)
↓-7.2%prior 614
Wet128 (14.3%)
↑26.7%prior 101
Snow82 (9.2%)
↑54.7%prior 53
Ice/frost80 (9.0%)
↑23.1%prior 65
Slush19 (2.1%)
↑216.7%prior 6
Gravel9 (1.0%)
↓-47.1%prior 17
Other (explain in narrative)3 (0.3%)
Mud, dirt1 (0.1%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, Ford and Chevrolet, remained consistent and saw an increase in their total counts from 2017 to 2018. An analysis of persons involved in crashes shows a demographic shift by age. The number of individuals aged 65 and older involved in crashes increased from 284 to 329, while the 16-20 age group saw a decrease from 262 to 247.

Top Vehicle Makes (1,779 vehicles)

1
FORD330 (18.5%)
↑10.4%prior 299
2
CHEV312 (17.5%)
↑25.3%prior 249
3
CHEVROLET100 (5.6%)
↓-18.7%prior 123
4
TOYT87 (4.9%)
↑16.0%prior 75
5
DODG71 (4%)
↑12.7%prior 63
6
HOND71 (4%)
↑61.4%prior 44
7
GMC58 (3.3%)
↓-10.8%prior 65
8
CHRY56 (3.1%)
↑16.7%prior 48
9
PONT54 (3%)
↑63.6%prior 33
10
JEEP53 (3%)
↑10.4%prior 48

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

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

Sex Distribution (1,413 persons with recorded sex)

Male782 (55.3%)
↑14.5%prior 683
Female631 (44.7%)
↑10.5%prior 571

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: 1,033
  • Total persons involved: 2,110
  • Total vehicles involved: 1,779

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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