Yearly Traffic Safety Analysis

549 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Wapello County recorded 549 vehicle crashes, a 3.4% increase from the 531 crashes reported in 2017. While total fatalities remained unchanged at 4, the number of injuries rose by 10.7% to 218. One of the most significant year-over-year changes was a 43.3% decrease in crashes involving driving under the influence (DUI), which fell from 30 incidents in 2017 to 17 in 2018.

549

3.4%was 531

Total Crash Events

4

Persons Killed

218

10.7%was 197

Persons Injured

4

33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 Wapello County saw a slight increase between 2017 and 2018. The total number of crashes rose by 3.4%, from 531 to 549. This increase was accompanied by a 10.7% rise in total injuries, from 197 to 218, while fatalities held steady at 4 for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

4

Motorists Killed

Prior: 40.0%

0

Other Killed

Prior: 00.0%

6

Pedestrians Injured

Prior: 2200.0%

4

Cyclists Injured

Prior: 1300.0%

207

Motorists Injured

Prior: 1937.3%

1

Other Injured

Prior: 10.0%

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 showed some shifts between 2017 and 2018. While Wednesday remained the peak day for crashes in both years with 89 incidents, the peak hour moved two hours later, from 3 p.m. in 2017 (41 crashes) to 5 p.m. in 2018 (46 crashes). A notable change occurred in the daily distribution, with crashes on Mondays and Tuesdays increasing from 67 and 64 respectively to 84 for both days, while Sunday crashes decreased from 76 to 62.

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 slightly worsened from 2017 to 2018. The number of fatal crashes increased from 3 to 4, raising the fatal crash rate from 0.56% to 0.73%. The proportion of crashes resulting in any level of injury (Serious, Minor, or Possible) also increased, accounting for 33.0% of all incidents in 2018 compared to 31.3% in the prior year. Consequently, the share of crashes with no injuries decreased from 68.2% to 66.3%.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.7%
33.3%prior 3
Serious Injury18serious injury crashes3.3%
5.9%prior 17
Minor Injury48minor injury crashes8.7%
-2.0%prior 49
Possible Injury115possible injury crashes20.9%
15.0%prior 100
No Injury364no injury crashes66.3%
0.6%prior 362

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 Wapello County remained consistent year-over-year, with 'Animal' being the most cited cause in both 2018 (98 incidents) and 2017 (96 incidents). 'Lost Control' was the second-most common factor in both periods, though its count decreased from 44 to 40. A significant shift was observed in crashes attributed to 'Driving too fast for conditions,' which increased by 74% from 19 incidents in 2017 to 33 in 2018. Conversely, incidents involving 'Ran Traffic Signal' decreased from 18 to 8.

Officer-Reported Primary Contributing Cause

Animal98 (17.9%)2.1%prior 96
Lost Control40 (7.3%)-9.1%prior 44
Other (explain in narrative): Other40 (7.3%)21.2%prior 33
Followed too close39 (7.1%)2.6%prior 38
FTYROW: From stop sign37 (6.7%)0.0%prior 37
Driving too fast for conditions33 (6%)73.7%prior 19
Ran off road - straight26 (4.7%)36.8%prior 19
Ran Stop Sign25 (4.6%)0.0%prior 25
FTYROW: Making left turn23 (4.2%)-8.0%prior 25
Ran off road - left17 (3.1%)-26.1%prior 23

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 larger proportion of crashes occurred during daylight hours, accounting for 60.3% of all incidents compared to 55.2% in 2017. Conversely, the share of crashes in dark conditions decreased from 26.6% to 21.7%. Regarding road surface conditions, there was a notable increase in crashes on non-dry surfaces. Crashes on wet roads rose from 34 to 53, and incidents on snow-covered roads more than doubled from 14 to 30.

Weather

Clear321 (69.5%)
-8.5%prior 351
Cloudy79 (17.1%)
43.6%prior 55
Rain32 (6.9%)
45.5%prior 22
Snow16 (3.5%)
100.0%prior 8
Freezing rain/drizzle8 (1.7%)
-20.0%prior 10
Other (explain in narrative)2 (0.4%)
Fog, smoke, smog2 (0.4%)
-75.0%prior 8
Severe Winds1 (0.2%)
Sleet, hail1 (0.2%)

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

Lighting

Daylight331 (71.6%)
13.0%prior 293
Dark - roadway lighted65 (14.1%)
4.8%prior 62
Dark - roadway not lighted49 (10.6%)
-27.9%prior 68
Dawn9 (1.9%)
-35.7%prior 14
Dark - unknown roadway lighting5 (1.1%)
-54.5%prior 11
Dusk3 (0.6%)
-57.1%prior 7

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

Road Surface

Dry353 (76.4%)
-8.5%prior 386
Wet53 (11.5%)
55.9%prior 34
Snow30 (6.5%)
114.3%prior 14
Ice/frost19 (4.1%)
46.2%prior 13
Gravel3 (0.6%)
-40.0%prior 5
Sand2 (0.4%)
Slush1 (0.2%)
Mud, dirt1 (0.2%)

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 broadly similar, with Chevrolet and Ford being the most common in both 2017 and 2018. When analyzing the age distribution of all persons involved in crashes, there was a notable increase in the 35-44 age group, which grew from 126 individuals in 2017 to 164 in 2018. The 26-34 age group also saw an increase from 150 to 175 persons. Conversely, the number of persons aged 65 and older involved in crashes decreased from 128 to 110.

Top Vehicle Makes (882 vehicles)

1
FORD146 (16.6%)
0.7%prior 145
2
CHEV142 (16.1%)
15.4%prior 123
3
DODG63 (7.1%)
23.5%prior 51
4
TOYT49 (5.6%)
-14.0%prior 57
5
CHEVROLET45 (5.1%)
-26.2%prior 61
6
GMC33 (3.7%)
37.5%prior 24
7
DODGE31 (3.5%)
-3.1%prior 32
8
JEEP29 (3.3%)
-6.5%prior 31
9
PONT24 (2.7%)
26.3%prior 19
10
TOYOTA23 (2.6%)
-17.9%prior 28

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

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

Sex Distribution (675 persons with recorded sex)

Male415 (61.5%)
15.9%prior 358
Female260 (38.5%)
-7.5%prior 281

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: 549
  • Total persons involved: 1,090
  • Total vehicles involved: 882

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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