Yearly Traffic Safety Analysis

742 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Warren County recorded 742 total crashes, a 3.1% increase from the 720 crashes reported in 2017. Despite the rise in total incidents, the number of people injured decreased by 9.7% from 236 to 213. A notable factor in the overall crash increase was a rise in incidents involving animals, which grew in count from 143 in 2017 to 168 in 2018.

742

▲ 3.1%was 720

Total Crash Events

5

Persons Killed

213

▼ -9.7%was 236

Persons Injured

5

▲ 25.0%was 4

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 crashes in Warren County saw a slight increase, rising from 720 in 2017 to 742 in 2018, a change of 3.1%. While total crashes went up, the number of resulting injuries declined from 236 to 213. The number of fatalities remained unchanged at 5 for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 5→0.0%

2

Pedestrians Injured

Prior: 1↑100.0%

1

Cyclists Injured

Prior: 5↓-80.0%

210

Motorists Injured

Prior: 230↓-8.7%

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 remarkably consistent year-over-year. Friday was the peak day for crashes in both 2018 and 2017, with an identical count of 130 incidents. Similarly, the 7 a.m. hour was the peak time for crashes in both periods, recording 64 crashes in 2018 and 68 in 2017, with no significant shifts in the overall daily or hourly distributions.

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 showed a mixed change year-over-year. The number of fatal crashes increased from 4 to 5, raising the fatal crash rate from 0.56% to 0.67%. However, the overall proportion of crashes resulting in any level of injury (serious, minor, or possible) decreased from 25.1% in 2017 to 23.7% in 2018. Consequently, the share of no-injury crashes rose from 74.3% to 75.6%.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.7%
↑25.0%prior 4
Serious Injury15serious injury crashes2%
↓-16.7%prior 18
Minor Injury64minor injury crashes8.6%
↑1.6%prior 63
Possible Injury97possible injury crashes13.1%
↓-3.0%prior 100
No Injury561no injury crashes75.6%
↑4.9%prior 535

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 the leading contributing factor in both periods, with the count increasing from 143 in 2017 to 168 in 2018. While 'Lost Control' remained a top cause, its count decreased from 70 to 58. Conversely, crashes attributed to 'Followed too close' saw a notable increase in count from 40 to 53, becoming the third most common factor in 2018. 'Driving too fast for conditions' saw a slight decrease in count from 43 to 40 incidents.

Officer-Reported Primary Contributing Cause

Animal168 (22.6%)↑17.5%prior 143
Lost Control58 (7.8%)↓-17.1%prior 70
Followed too close53 (7.1%)↑32.5%prior 40
Other (explain in narrative): Other47 (6.3%)↑17.5%prior 40
Driving too fast for conditions40 (5.4%)↓-7.0%prior 43
FTYROW: From stop sign35 (4.7%)↓-10.3%prior 39
Ran off road - left35 (4.7%)↓-20.5%prior 44
FTYROW: Making left turn33 (4.4%)↑22.2%prior 27
Ran off road - straight32 (4.3%)↑10.3%prior 29
Driver Distraction: Other interior distraction24 (3.2%)↓-7.7%prior 26

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 proportion of crashes occurring in clear weather and on dry roads decreased from 2017 to 2018. In 2018, 50.9% of crashes happened in clear weather, down from 56.1% the prior year, while crashes on dry surfaces fell from 65.8% to 58.0% of the total. Correspondingly, the share of crashes on adverse road surfaces like wet, snow, or ice increased from 15.7% in 2017 to 22.6% in 2018. Crashes during daylight hours accounted for 54.2% of the total in 2018, a slight decrease from 57.4% in 2017.

Weather

Clear378 (61.4%)
↓-6.4%prior 404
Cloudy133 (21.6%)
↓-2.9%prior 137
Rain44 (7.1%)
↑12.8%prior 39
Snow34 (5.5%)
↑126.7%prior 15
Freezing rain/drizzle18 (2.9%)
↑63.6%prior 11
Fog, smoke, smog4 (0.6%)
↓-20.0%prior 5
Sleet, hail2 (0.3%)
Other (explain in narrative)1 (0.2%)
Blowing Snow1 (0.2%)
Severe Winds1 (0.2%)

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

Lighting

Daylight402 (65.0%)
↓-2.7%prior 413
Dark - roadway not lighted113 (18.3%)
↑7.6%prior 105
Dark - roadway lighted61 (9.9%)
↑24.5%prior 49
Dusk24 (3.9%)
↓-17.2%prior 29
Dawn16 (2.6%)
↓-23.8%prior 21
Dark - unknown roadway lighting2 (0.3%)

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

Road Surface

Dry430 (69.8%)
↓-9.3%prior 474
Wet93 (15.1%)
↑43.1%prior 65
Snow31 (5.0%)
↑19.2%prior 26
Ice/frost31 (5.0%)
↑72.2%prior 18
Gravel17 (2.8%)
↓-41.4%prior 29
Slush7 (1.1%)
Sand3 (0.5%)
Mud, dirt3 (0.5%)
Water (standing or moving)1 (0.2%)

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 showed some shifts, though Ford remained the most frequent, with its involvement increasing from 201 vehicles in 2017 to 243 in 2018. Chevrolet vehicles, listed under both 'CHEV' and 'CHEVROLET', collectively decreased from 251 to 215. Regarding persons involved, there was a noticeable shift in age demographics, with the proportion of individuals aged 45-54 increasing from 11.2% of persons in 2017 to 15.6% in 2018. Conversely, the 16-20 age group's representation decreased from 16.8% to 14.7%.

Top Vehicle Makes (1,171 vehicles)

1
FORD243 (20.8%)
↑20.9%prior 201
2
CHEV162 (13.8%)
↓-4.1%prior 169
3
DODG77 (6.6%)
↑16.7%prior 66
4
TOYT75 (6.4%)
↑50.0%prior 50
5
CHEVROLET53 (4.5%)
↓-35.4%prior 82
6
JEEP48 (4.1%)
↑41.2%prior 34
7
NISS30 (2.6%)
↑3.4%prior 29
8
PONT28 (2.4%)
↑12.0%prior 25
9
HOND28 (2.4%)
↑40.0%prior 20
10
GMC28 (2.4%)
↓-3.4%prior 29

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

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

Sex Distribution (955 persons with recorded sex)

Male552 (57.8%)
↑12.7%prior 490
Female403 (42.2%)
↑6.1%prior 380

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: 742
  • Total persons involved: 1,415
  • Total vehicles involved: 1,171

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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