Yearly Traffic Safety Analysis

294 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Tama County, total traffic crashes decreased by 3%, from 303 in 2017 to 294 in 2018. This overall decline was accompanied by a significant reduction in crash severity. The most notable year-over-year change was a 50% decrease in fatalities, which fell from 6 in the prior period to 3 in the current period.

294

▼ -3.0%was 303

Total Crash Events

3

▼ -50.0%was 6

Persons Killed

69

▼ -19.8%was 86

Persons Injured

3

▼ -40.0%was 5

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 Tama County showed improvement year-over-year. The total number of crashes declined by 3% from 303 to 294. More significantly, the number of people killed in crashes was halved, dropping from 6 to 3, and the number of people injured fell by nearly 20%, from 86 to 69.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

3

Motorists Killed

Prior: 5↓-40.0%

1

Pedestrians Injured

Prior: 0→%

68

Motorists Injured

Prior: 84↓-19.0%

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. The peak day for crashes moved from Sunday (54 incidents) in 2017 to Monday (52 incidents) in 2018. While 5 p.m. remained the peak hour in both years, the number of crashes during that hour decreased substantially from 34 in the prior year to 18 in the current year.

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 decreased overall, with the fatal crash rate falling from 1.65 per 100 crashes in 2017 to 1.02 in 2018. The number of fatal crashes dropped from 5 to 3. However, the number of serious injury crashes more than doubled, increasing from 5 in the prior year to 14 in the current year, representing a shift from 1.7% to 4.8% of all crashes.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1%
↓-40.0%prior 5
Serious Injury14serious injury crashes4.8%
↑180.0%prior 5
Minor Injury20minor injury crashes6.8%
↓-37.5%prior 32
Possible Injury24possible injury crashes8.2%
↓-14.3%prior 28
No Injury233no injury crashes79.3%
→0.0%prior 233

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 periods, though the count decreased slightly from 128 incidents in 2017 to 122 in 2018. 'Lost Control' was the second-most cited factor in both years, also seeing a small decline from 29 to 24 crashes. Conversely, crashes attributed to 'Driving too fast for conditions' increased from 17 to 21 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal122 (41.5%)↓-4.7%prior 128
Lost Control24 (8.2%)↓-17.2%prior 29
Driving too fast for conditions21 (7.1%)↑23.5%prior 17
Other (explain in narrative): Other19 (6.5%)↑46.2%prior 13
Ran off road - straight10 (3.4%)↓-28.6%prior 14
Operating vehicle in an reckless, erratic, careless, negligent manner9 (3.1%)↑28.6%prior 7
Ran off road - left9 (3.1%)↓-35.7%prior 14
Driver Distraction: Other interior distraction9 (3.1%)
Made improper turn8 (2.7%)
Other (explain in narrative): No improper action7 (2.4%)

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 on dry roads, there was a notable increase in incidents under adverse conditions. Crashes on roads with snow, ice, or slush more than doubled, rising from 19 incidents in 2017 to 42 in 2018. Similarly, the number of crashes that occurred in dark, unlighted conditions increased from 41 to 52 year-over-year.

Weather

Clear123 (62.8%)
↑12.8%prior 109
Cloudy32 (16.3%)
↓-33.3%prior 48
Snow16 (8.2%)
↑77.8%prior 9
Freezing rain/drizzle8 (4.1%)
Rain6 (3.1%)
↓-40.0%prior 10
Fog, smoke, smog5 (2.6%)
Blowing Snow3 (1.5%)
Sleet, hail2 (1.0%)
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

Daylight117 (59.4%)
→0.0%prior 117
Dark - roadway not lighted52 (26.4%)
↑26.8%prior 41
Dark - roadway lighted18 (9.1%)
↓-14.3%prior 21
Dawn5 (2.5%)
Dusk4 (2.0%)
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

Dry128 (65.3%)
↓-3.8%prior 133
Snow19 (9.7%)
↑58.3%prior 12
Wet15 (7.7%)
↓-16.7%prior 18
Ice/frost14 (7.1%)
↑100.0%prior 7
Gravel10 (5.1%)
→0.0%prior 10
Slush9 (4.6%)
Mud, dirt1 (0.5%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Chevrolet, Ford, and Dodge leading in both periods, although the total number of vehicles from these top makes involved in crashes decreased. Analysis of person demographics reveals a shift in age group involvement. The number of individuals aged 35-44 involved in crashes saw a significant increase, rising from 61 in 2017 to 83 in 2018.

Top Vehicle Makes (383 vehicles)

1
CHEV66 (17.2%)
↓-17.5%prior 80
2
FORD60 (15.7%)
↓-23.1%prior 78
3
DODG24 (6.3%)
↑140.0%prior 10
4
TOYT24 (6.3%)
↑166.7%prior 9
5
CHEVROLET20 (5.2%)
↓-13.0%prior 23
6
CHRY17 (4.4%)
↑54.5%prior 11
7
GMC13 (3.4%)
↑116.7%prior 6
8
JEEP13 (3.4%)
↓-7.1%prior 14
9
DODGE13 (3.4%)
↓-13.3%prior 15
10
PONT10 (2.6%)
→0.0%prior 10

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

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

Sex Distribution (287 persons with recorded sex)

Male171 (59.6%)
↑7.5%prior 159
Female116 (40.4%)
↑4.5%prior 111

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: 294
  • Total persons involved: 480
  • Total vehicles involved: 383

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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