Yearly Traffic Safety Analysis

102 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Howard County, total traffic crashes remained unchanged year-over-year, with 102 incidents recorded in both 2018 and 2017. Despite the stable number of crashes, the total number of people injured increased by 15.2%, rising from 33 in 2017 to 38 in 2018. The most significant shift was a 75% increase in crashes involving a driver under the influence, which grew from 4 to 7 incidents.

102

Total Crash Events

0

Persons Killed

38

▲ 15.2%was 33

Persons Injured

0

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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

The overall trend in crash volume for Howard County was stable between 2017 and 2018, with the total number of incidents holding steady at 102. However, the outcomes of these crashes worsened, as the number of total injuries rose by 15.2% from 33 to 38. There were no fatalities recorded in either year.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 0→0.0%

1

Cyclists Injured

Prior: 0→%

37

Motorists Injured

Prior: 33↑12.1%

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

Temporal crash patterns shifted between the two periods. The peak hour for crashes moved from the evening commute at 8 p.m. in 2017 (9 crashes) to the morning at 6 a.m. in 2018 (9 crashes). While Wednesday was a peak day in both years, Friday also emerged as a high-frequency day in 2018, with its crash count rising from 15 to 18.

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

There were no fatal crashes recorded in either 2018 or 2017. However, the severity of injury crashes increased, with the count of serious injury crashes more than doubling from 2 in 2017 to 5 in 2018. Consequently, the share of crashes resulting in a serious injury rose from 2.0% to 4.9% of all incidents year-over-year.

Outcome by Severity (Crash Events)

Serious Injury5serious injury crashes4.9%
↑150.0%prior 2
Minor Injury12minor injury crashes11.8%
↑33.3%prior 9
Possible Injury9possible injury crashes8.8%
↓-25.0%prior 12
No Injury76no injury crashes74.5%
↓-3.8%prior 79

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 were the leading contributing factor in both periods, with the count of such incidents increasing by 12.5% from 40 in 2017 to 45 in 2018. The second-ranked factor changed from "Lost Control" in 2017 (18 crashes) to "Ran off road - straight" in 2018 (11 crashes). Notably, crashes attributed to a driver losing control saw a 72.2% decrease in count year-over-year.

Officer-Reported Primary Contributing Cause

Animal45 (44.1%)↑12.5%prior 40
Ran off road - straight11 (10.8%)↑57.1%prior 7
FTYROW: From stop sign6 (5.9%)
Driving too fast for conditions5 (4.9%)
Lost Control5 (4.9%)↓-72.2%prior 18
Exceeded authorized speed3 (2.9%)
Operating vehicle in an reckless, erratic, careless, negligent manner3 (2.9%)
Ran off road - right3 (2.9%)
Improper Backing2 (2%)
Other (explain in narrative): No improper action2 (2%)

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

Road & Environmental Conditions

The conditions under which crashes occurred shifted between 2017 and 2018. The number of crashes happening in snowy weather tripled from 3 to 9, and collisions on snowy road surfaces increased from 7 to 11. Conversely, crashes in daylight decreased from 44 to 32, while incidents occurring in darkness on unlighted roadways increased from 17 to 22.

Weather

Clear37 (58.7%)
↓-11.9%prior 42
Cloudy11 (17.5%)
↓-35.3%prior 17
Snow9 (14.3%)
Freezing rain/drizzle2 (3.2%)
Blowing Snow1 (1.6%)
Rain1 (1.6%)
Severe Winds1 (1.6%)
Fog, smoke, smog1 (1.6%)

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

Lighting

Daylight32 (50.8%)
↓-27.3%prior 44
Dark - roadway not lighted22 (34.9%)
↑29.4%prior 17
Dawn5 (7.9%)
Dusk2 (3.2%)
Dark - roadway lighted1 (1.6%)
Dark - unknown roadway lighting1 (1.6%)

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

Road Surface

Dry32 (50.8%)
↓-8.6%prior 35
Snow11 (17.5%)
↑57.1%prior 7
Gravel10 (15.9%)
↑11.1%prior 9
Ice/frost7 (11.1%)
↓-36.4%prior 11
Slush2 (3.2%)
Wet1 (1.6%)
↓-80.0%prior 5

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

Vehicles & Demographics

Chevrolet and Ford vehicles were the most frequently involved makes in both years, with counts for both increasing in 2018. The number of people involved in crashes from the 26-34 age group saw a significant increase, rising from 21 individuals in 2017 to 32 in 2018. The 16-20 age group also saw an increase from 17 to 25 individuals. In contrast, involvement from the 55-64 age group decreased from 25 to 19 people.

Top Vehicle Makes (133 vehicles)

1
CHEV30 (22.6%)
↑57.9%prior 19
2
FORD24 (18%)
↑41.2%prior 17
3
CHEVROLET14 (10.5%)
↓-22.2%prior 18
4
DODG7 (5.3%)
↓-22.2%prior 9
5
GMC6 (4.5%)
↑20.0%prior 5
6
HOND5 (3.8%)
7
CHRYSLER4 (3%)
8
NISSAN4 (3%)
9
DODGE4 (3%)
10
JEEP3 (2.3%)

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

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

Sex Distribution (84 persons with recorded sex)

Male58 (69.0%)
↓-9.4%prior 64
Female26 (31.0%)
↓-10.3%prior 29

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 10, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 102
  • Total persons involved: 181
  • Total vehicles involved: 133

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 10, 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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