Yearly Traffic Safety Analysis

2,381 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Woodbury County, total vehicle crashes increased slightly from 2,337 in 2017 to 2,381 in 2018, a change of 1.9%. Despite the rise in total incidents, the number of fatalities resulting from these crashes decreased from 8 to 5. The most significant shift in contributing factors was a 40% increase in crashes attributed to a driver running off the left side of the road, which rose from 154 to 216 incidents.

2,381

▲ 1.9%was 2,337

Total Crash Events

5

▼ -37.5%was 8

Persons Killed

785

▼ -0.9%was 792

Persons Injured

5

▼ -16.7%was 6

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

Overall traffic crash trends in Woodbury County were mixed year-over-year. Total crashes saw a minor increase of 1.9%, from 2,337 to 2,381. In contrast, total fatalities fell by 37.5% from 8 to 5, and total injuries saw a slight decrease from 792 to 785.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 7↓-28.6%

0

Other Killed

Prior: 0→0.0%

22

Pedestrians Injured

Prior: 26↓-15.4%

16

Cyclists Injured

Prior: 20↓-20.0%

746

Motorists Injured

Prior: 740↑0.8%

1

Other Injured

Prior: 6↓-83.3%

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 temporal patterns of crashes remained largely consistent between the two periods. Friday was the day with the highest number of crashes in both 2017 (417 crashes) and 2018 (403 crashes). The daily peak for collisions shifted slightly earlier, moving from the 4 p.m. hour in 2017 (210 crashes) to the 3 p.m. hour in 2018 (206 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

The severity of crashes lessened slightly from 2017 to 2018. The number of fatal crashes decreased from 6 to 5, and the share of crashes resulting in a fatality dropped from 0.3% to 0.2%. The proportion of crashes involving any level of injury also decreased, from 31.4% in 2017 to 30.4% in 2018, while no-injury crashes increased their share from 68.4% to 69.3% of all incidents.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.2%
↓-16.7%prior 6
Serious Injury32serious injury crashes1.3%
↓-17.9%prior 39
Minor Injury181minor injury crashes7.6%
↓-13.0%prior 208
Possible Injury513possible injury crashes21.5%
↑5.6%prior 486
No Injury1,650no injury crashes69.3%
↑3.3%prior 1,598

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

While 'Followed too close' remained the top contributing factor in both years, its count decreased by 22% from 312 incidents in 2017 to 243 in 2018. The most significant change was in crashes due to 'Ran off road - left', which increased by 40% from 154 to 216, moving it from the third to the second most common factor. Crashes attributed to 'Driving too fast for conditions' also rose by 23%, from 150 to 184 incidents.

Officer-Reported Primary Contributing Cause

Followed too close243 (10.2%)↓-22.1%prior 312
Ran off road - left216 (9.1%)↑40.3%prior 154
Driving too fast for conditions184 (7.7%)↑22.7%prior 150
Other (explain in narrative): Other167 (7%)↑39.2%prior 120
FTYROW: From stop sign166 (7%)↑1.2%prior 164
Animal143 (6%)↑19.2%prior 120
Lost Control110 (4.6%)↓-6.8%prior 118
Ran Traffic Signal109 (4.6%)↓-9.9%prior 121
FTYROW: Making left turn103 (4.3%)↓-5.5%prior 109
Ran Stop Sign95 (4%)↓-9.5%prior 105

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

Road & Environmental Conditions

There was a notable shift toward more crashes occurring in adverse conditions in 2018 compared to 2017. The share of crashes on dry road surfaces fell from 71.3% to 60.8%, while crashes on snow-covered roads increased from 136 to 214 and on icy roads from 83 to 161. Similarly, the number of crashes during snowy weather nearly doubled, rising from 80 to 152 incidents.

Weather

Clear1,340 (59.8%)
↓-6.3%prior 1,430
Cloudy536 (23.9%)
↓-2.2%prior 548
Snow152 (6.8%)
↑90.0%prior 80
Rain129 (5.8%)
↑12.2%prior 115
Freezing rain/drizzle41 (1.8%)
↑32.3%prior 31
Blowing Snow28 (1.3%)
↑460.0%prior 5
Fog, smoke, smog8 (0.4%)
↑33.3%prior 6
Severe Winds3 (0.1%)
Sleet, hail1 (0.0%)
↓-80.0%prior 5
Other (explain in narrative)1 (0.0%)

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

Lighting

Daylight1,602 (71.1%)
↑5.5%prior 1,519
Dark - roadway lighted432 (19.2%)
↓-0.5%prior 434
Dark - roadway not lighted115 (5.1%)
↓-23.3%prior 150
Dusk53 (2.4%)
↓-31.2%prior 77
Dawn43 (1.9%)
↑16.2%prior 37
Dark - unknown roadway lighting7 (0.3%)
↓-41.7%prior 12

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

Road Surface

Dry1,447 (64.5%)
↓-13.2%prior 1,667
Wet350 (15.6%)
↑29.2%prior 271
Snow214 (9.5%)
↑57.4%prior 136
Ice/frost161 (7.2%)
↑94.0%prior 83
Slush42 (1.9%)
↑44.8%prior 29
Gravel23 (1.0%)
↓-36.1%prior 36
Mud, dirt5 (0.2%)
Water (standing or moving)1 (0.0%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained stable, with Ford and Chevrolet (combining 'CHEV' and 'CHEVROLET' records) being the top two in both years. Ford-involved crashes decreased slightly from 675 to 652, and Chevrolet-involved crashes fell from 895 to 865. Analysis of persons involved shows a decrease in the 16-20 age group (from 653 to 604 persons) and an increase in the 26-34 age group (from 731 to 832 persons).

Top Vehicle Makes (4,459 vehicles)

1
FORD652 (14.6%)
↓-3.4%prior 675
2
CHEV535 (12%)
↑5.5%prior 507
3
CHEVROLET330 (7.4%)
↓-14.9%prior 388
4
JEEP184 (4.1%)
↑23.5%prior 149
5
TOYT176 (3.9%)
↑21.4%prior 145
6
DODG172 (3.9%)
↑30.3%prior 132
7
GMC171 (3.8%)
↑13.2%prior 151
8
NR148 (3.3%)
↑5.7%prior 140
9
KIA145 (3.3%)
↑13.3%prior 128
10
HOND132 (3%)
↓-15.4%prior 156

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

1,105 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (3,296 persons with recorded sex)

Male1,817 (55.1%)
↑2.6%prior 1,771
Female1,479 (44.9%)
↑6.2%prior 1,393

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: 2,381
  • Total persons involved: 5,376
  • Total vehicles involved: 4,459

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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