Yearly Traffic Safety Analysis

230 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Crawford County recorded 230 total crashes, a 16.1% decrease from the 274 crashes reported in 2017. While overall crashes declined, the number of fatalities increased from zero in 2017 to three in 2018. The total number of injuries remained relatively stable, with 99 in 2018 compared to 94 in the prior year.

230

▼ -16.1%was 274

Total Crash Events

3

Persons Killed

99

▲ 5.3%was 94

Persons Injured

3

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

Overall crash volume in Crawford County decreased by 16.1% from 2017 to 2018, with 44 fewer incidents reported. Despite the drop in total crashes, the number of people injured saw a slight increase from 94 to 99. Most notably, fatalities rose from zero in 2017 to three in 2018.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 0→%

1

Pedestrians Injured

Prior: 1→0.0%

1

Cyclists Injured

Prior: 0→%

97

Motorists Injured

Prior: 93↑4.3%

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 Monday with 40 incidents, a change from Friday, which saw 48 crashes in the prior year. Similarly, the peak hour for collisions moved from 3 p.m. in 2017 (23 crashes) to 5 p.m. in 2018 (20 crashes).

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 worsened despite a lower total crash count. In 2018, Crawford County experienced 3 fatal crashes, an increase from zero in the previous year. The proportion of crashes resulting in any injury (fatal, serious, minor, or possible) increased from 29.2% of all crashes in 2017 to 37.4% in 2018. Correspondingly, the share of non-injury crashes decreased from 70.8% to 62.6% year-over-year.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.3%
Serious Injury4serious injury crashes1.7%
↓-55.6%prior 9
Minor Injury28minor injury crashes12.2%
↑7.7%prior 26
Possible Injury51possible injury crashes22.2%
↑13.3%prior 45
No Injury144no injury crashes62.6%
↓-25.8%prior 194

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 involving animals remained a leading contributing factor in both periods, although the count decreased from 40 in 2017 to 28 in 2018. The number of crashes attributed to 'Lost Control' also fell from 27 to 21. Conversely, crashes where a driver failed to yield the right-of-way while making a left turn increased from 7 incidents in 2017 to 10 in 2018.

Officer-Reported Primary Contributing Cause

Animal28 (12.2%)↓-30.0%prior 40
Other (explain in narrative): Other24 (10.4%)↑100.0%prior 12
Lost Control21 (9.1%)↓-22.2%prior 27
Ran off road - left13 (5.7%)↓-7.1%prior 14
Followed too close13 (5.7%)↓-31.6%prior 19
Driving too fast for conditions12 (5.2%)↑33.3%prior 9
Ran off road - straight12 (5.2%)↓-25.0%prior 16
FTYROW: From stop sign10 (4.3%)↓-23.1%prior 13
FTYROW: Making left turn10 (4.3%)↑42.9%prior 7
Driver Distraction: Other interior distraction7 (3%)↓-12.5%prior 8

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 the majority of crashes in both years occurred in clear weather on dry roads, there was a notable shift in adverse condition incidents. Crashes occurring during freezing rain or drizzle tripled, rising from 5 incidents in 2017 to 15 in 2018. Crashes on wet surfaces also increased from 21 to 28, while incidents on dark, unlighted roadways decreased from 57 to 37.

Weather

Clear135 (65.9%)
↓-18.7%prior 166
Cloudy34 (16.6%)
↓-32.0%prior 50
Freezing rain/drizzle15 (7.3%)
↑200.0%prior 5
Rain9 (4.4%)
↓-10.0%prior 10
Snow5 (2.4%)
↓-44.4%prior 9
Blowing Snow4 (2.0%)
Fog, smoke, smog2 (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

Daylight137 (65.9%)
↓-10.5%prior 153
Dark - roadway not lighted37 (17.8%)
↓-35.1%prior 57
Dark - roadway lighted26 (12.5%)
↑13.0%prior 23
Dusk4 (1.9%)
↓-42.9%prior 7
Dawn2 (1.0%)
↓-71.4%prior 7
Dark - unknown roadway lighting2 (1.0%)

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

Road Surface

Dry132 (63.5%)
↓-26.7%prior 180
Wet28 (13.5%)
↑33.3%prior 21
Ice/frost21 (10.1%)
↑31.3%prior 16
Snow13 (6.3%)
↓-18.8%prior 16
Gravel7 (3.4%)
↓-41.7%prior 12
Slush6 (2.9%)
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 vehicles being the most frequent in both periods. In 2018, Chevrolet vehicles were involved in 74 crashes and Fords in 59, reversing the order from 2017 when Fords were more prevalent (86 vs. 82). Analysis of persons involved in crashes shows a significant decrease in the 65+ age group, which fell from 66 individuals in 2017 to 44 in 2018.

Top Vehicle Makes (396 vehicles)

1
FORD59 (14.9%)
↓-31.4%prior 86
2
CHEV50 (12.6%)
↑4.2%prior 48
3
DODG29 (7.3%)
↑26.1%prior 23
4
CHEVROLET24 (6.1%)
↓-29.4%prior 34
5
GMC23 (5.8%)
↑9.5%prior 21
6
CHRY14 (3.5%)
→0.0%prior 14
7
DODGE13 (3.3%)
↑30.0%prior 10
8
NR13 (3.3%)
↑116.7%prior 6
9
JEEP13 (3.3%)
↓-18.8%prior 16
10
NISS12 (3%)
→0.0%prior 12

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

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

Sex Distribution (290 persons with recorded sex)

Male164 (56.6%)
↓-12.8%prior 188
Female126 (43.4%)
↑6.8%prior 118

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: 230
  • Total persons involved: 501
  • Total vehicles involved: 396

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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