Yearly Traffic Safety Analysis

894 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Des Moines County recorded 894 total crashes, a 3.6% increase from the 863 crashes reported in 2017. While total crashes rose slightly, the number of fatalities decreased from 3 to 2. The most significant year-over-year change was a 45% reduction in serious injury crashes, which fell from 20 in 2017 to 11 in 2018.

894

▲ 3.6%was 863

Total Crash Events

2

▼ -33.3%was 3

Persons Killed

191

▼ -8.2%was 208

Persons Injured

2

▼ -33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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, total crashes in Des Moines County saw a slight increase of 3.6% in 2018 compared to the previous year, rising from 863 to 894 incidents. Despite this increase in crash volume, the outcomes were less severe, with total injuries declining by 8.2% from 208 to 191 and fatalities falling from 3 to 2.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 3↓-66.7%

5

Pedestrians Injured

Prior: 5→0.0%

4

Cyclists Injured

Prior: 3↑33.3%

182

Motorists Injured

Prior: 198↓-8.1%

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

The temporal patterns of crashes remained largely consistent year-over-year, with Friday being the peak day and 3 p.m. the peak hour in both 2017 and 2018. However, there was a notable shift in daily distribution, with crashes on Sunday decreasing from 122 to 86, while Monday crashes increased from 109 to 131. The morning commute also saw a change, with the 8 a.m. hour experiencing a rise in crashes from 24 to 49.

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 in 2018 compared to the prior year. The number of fatal crashes fell from 3 to 2, and the fatal crash rate per 100 crashes dropped from 0.35 to 0.22. Most notably, serious injury crashes were nearly halved, falling from 20 incidents (2.3% of total) in 2017 to 11 (1.2% of total) in 2018. Consequently, the proportion of non-injury crashes increased from 78.0% to 80.5% of all incidents.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.2%
↓-33.3%prior 3
Serious Injury11serious injury crashes1.2%
↓-45.0%prior 20
Minor Injury57minor injury crashes6.4%
↑21.3%prior 47
Possible Injury104possible injury crashes11.6%
↓-13.3%prior 120
No Injury720no injury crashes80.5%
↑7.0%prior 673

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

The leading contributing factors for crashes in 2018 were consistent with the prior year, though their frequencies shifted. Collisions involving an animal remained the top factor but decreased in count from 146 in 2017 to 117 in 2018. In contrast, crashes attributed to 'Followed too close' increased by 36%, from 61 incidents to 83, making it the second most common factor. Other notable increases include crashes from 'Driving too fast for conditions,' which rose from 39 to 52, and 'Ran off road - left,' which increased from 48 to 62.

Officer-Reported Primary Contributing Cause

Animal117 (13.1%)↓-19.9%prior 146
Followed too close83 (9.3%)↑36.1%prior 61
Other (explain in narrative): Other65 (7.3%)↑18.2%prior 55
Ran off road - left62 (6.9%)↑29.2%prior 48
Driving too fast for conditions52 (5.8%)↑33.3%prior 39
FTYROW: From stop sign51 (5.7%)↑18.6%prior 43
FTYROW: Making left turn46 (5.1%)↑9.5%prior 42
Lost Control39 (4.4%)↓-11.4%prior 44
Improper Backing32 (3.6%)↑77.8%prior 18
Ran Stop Sign26 (2.9%)→0.0%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

In 2018, a greater share of crashes occurred during daylight hours, accounting for 64.0% of incidents compared to 58.1% in 2017. However, there was a notable shift toward more crashes on adverse road surfaces. Collisions on wet roads increased from 80 to 104, on snowy roads from 42 to 63, and on icy or frosty roads from 11 to 31. This corresponds with an increase in crashes during cloudy and snowy weather conditions compared to the prior year.

Weather

Clear480 (60.9%)
↓-5.1%prior 506
Cloudy207 (26.3%)
↑24.0%prior 167
Rain44 (5.6%)
→0.0%prior 44
Snow30 (3.8%)
↑25.0%prior 24
Freezing rain/drizzle13 (1.6%)
Fog, smoke, smog10 (1.3%)
↑25.0%prior 8
Severe Winds2 (0.3%)
Other (explain in narrative)2 (0.3%)

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

Lighting

Daylight572 (72.3%)
↑14.2%prior 501
Dark - roadway lighted121 (15.3%)
↓-11.0%prior 136
Dark - roadway not lighted64 (8.1%)
↓-14.7%prior 75
Dusk24 (3.0%)
→0.0%prior 24
Dawn7 (0.9%)
↓-58.8%prior 17
Dark - unknown roadway lighting3 (0.4%)
↓-50.0%prior 6

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

Road Surface

Dry574 (72.7%)
↓-4.0%prior 598
Wet104 (13.2%)
↑30.0%prior 80
Snow63 (8.0%)
↑50.0%prior 42
Ice/frost31 (3.9%)
↑181.8%prior 11
Slush9 (1.1%)
Gravel6 (0.8%)
↓-66.7%prior 18
Mud, dirt2 (0.3%)
Water (standing or moving)1 (0.1%)

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 Ford (277 vehicles), Chevrolet (266), and Dodge (132) being the most common in 2018, similar to 2017. Demographically, there were shifts in the age of persons involved in crashes. The number of individuals aged 65 and older involved in collisions increased notably from 199 to 242. Conversely, the 55-64 age group saw a decrease in involvement, from 204 individuals in 2017 to 160 in 2018.

Top Vehicle Makes (1,542 vehicles)

1
FORD277 (18%)
↑14.9%prior 241
2
CHEV181 (11.7%)
↓-8.1%prior 197
3
DODG94 (6.1%)
↓-1.1%prior 95
4
CHEVROLET85 (5.5%)
↓-20.6%prior 107
5
NR71 (4.6%)
↑82.1%prior 39
6
GMC69 (4.5%)
↑60.5%prior 43
7
KIA66 (4.3%)
↑32.0%prior 50
8
TOYO48 (3.1%)
↑182.4%prior 17
9
CHRY44 (2.9%)
↓-2.2%prior 45
10
JEEP42 (2.7%)
↓-16.0%prior 50

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

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

Sex Distribution (1,140 persons with recorded sex)

Male603 (52.9%)
↑7.1%prior 563
Female537 (47.1%)
↑5.1%prior 511

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: 894
  • Total persons involved: 1,819
  • Total vehicles involved: 1,542

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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