Yearly Traffic Safety Analysis

531 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Wapello County recorded 531 total vehicle crashes, a 1.5% increase from the 523 crashes reported in 2016. While total crashes, fatalities (4, down from 6), and injuries (197, down from 229) remained relatively stable or decreased, incidents involving driving under the influence (DUI) saw a significant year-over-year increase, rising from 12 in 2016 to 30 in 2017.

531

▲ 1.5%was 523

Total Crash Events

4

▼ -33.3%was 6

Persons Killed

197

▼ -14.0%was 229

Persons Injured

3

▼ -25.0%was 4

Fatal Crash Events

Note: "Persons Killed" (4) 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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, the total number of crashes in Wapello County saw a slight increase of 1.5% from 2016 to 2017, rising from 523 to 531. Despite the rise in total incidents, the human toll was lower, with total fatalities decreasing from 6 to 4 and total injuries falling from 229 to 197.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 6↓-33.3%

0

Other Killed

Prior: 0→0.0%

2

Pedestrians Injured

Prior: 4↓-50.0%

1

Cyclists Injured

Prior: 3↓-66.7%

193

Motorists Injured

Prior: 222↓-13.1%

1

Other Injured

Prior: 0→%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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 2016 and 2017. The peak day for crashes moved from Friday (93 incidents) in 2016 to Wednesday (89 incidents) in 2017. However, the peak hour for collisions remained consistent year-over-year, with the 3 p.m. hour seeing the highest frequency in both periods with 41 crashes each year.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes shifted between the two periods. The fatal crash rate decreased from 0.76% of all crashes in 2016 to 0.56% in 2017, with the count of fatal crashes dropping from 4 to 3. The proportion of crashes resulting in serious injuries increased from 2.3% to 3.2%, while the share of minor and possible injury crashes declined. Crashes resulting in no injury comprised a larger share of the total, rising from 65.8% in 2016 to 68.2% in 2017.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.6%
↓-25.0%prior 4
Serious Injury17serious injury crashes3.2%
↑41.7%prior 12
Minor Injury49minor injury crashes9.2%
↓-14.0%prior 57
Possible Injury100possible injury crashes18.8%
↓-5.7%prior 106
No Injury362no injury crashes68.2%
↑5.2%prior 344

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both years, with the count of such incidents increasing from 73 in 2016 to 96 in 2017. 'Lost Control' was the second most common factor in both periods, though its count decreased from 52 to 44. 'Failure to yield right-of-way from a stop sign' dropped from the third-ranked factor in 2016 (45 crashes) to fourth in 2017 (37 crashes), while 'Followed too close' moved into the third position with 38 crashes in 2017, down from 40 the prior year.

Officer-Reported Primary Contributing Cause

Animal96 (18.1%)↑31.5%prior 73
Lost Control44 (8.3%)↓-15.4%prior 52
Followed too close38 (7.2%)↓-5.0%prior 40
FTYROW: From stop sign37 (7%)↓-17.8%prior 45
Other (explain in narrative): Other33 (6.2%)↑37.5%prior 24
FTYROW: Making left turn25 (4.7%)↓-3.8%prior 26
Ran Stop Sign25 (4.7%)↑13.6%prior 22
Ran off road - left23 (4.3%)↓-23.3%prior 30
Operating vehicle in an reckless, erratic, careless, negligent manner20 (3.8%)↑66.7%prior 12
Driving too fast for conditions19 (3.6%)↓-40.6%prior 32

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in both periods predominantly occurred in clear weather and on dry roads. In 2017, the proportion of crashes on dry surfaces increased, accounting for 72.7% of all incidents compared to 67.3% in 2016. Conversely, crashes during daylight hours, while still the majority, decreased as a share of the total, falling from 62.1% in 2016 to 55.2% in 2017. Crashes in dark conditions (both lighted and unlighted roadways) increased from 120 to 130.

Weather

Clear351 (77.0%)
↑10.4%prior 318
Cloudy55 (12.1%)
↓-38.2%prior 89
Rain22 (4.8%)
↑37.5%prior 16
Freezing rain/drizzle10 (2.2%)
→0.0%prior 10
Snow8 (1.8%)
↓-57.9%prior 19
Fog, smoke, smog8 (1.8%)
Severe Winds1 (0.2%)
Blowing Snow1 (0.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight293 (64.4%)
↓-9.8%prior 325
Dark - roadway not lighted68 (14.9%)
↑13.3%prior 60
Dark - roadway lighted62 (13.6%)
↑3.3%prior 60
Dawn14 (3.1%)
Dark - unknown roadway lighting11 (2.4%)
↑120.0%prior 5
Dusk7 (1.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry386 (84.6%)
↑9.7%prior 352
Wet34 (7.5%)
↓-12.8%prior 39
Snow14 (3.1%)
↓-51.7%prior 29
Ice/frost13 (2.9%)
↓-31.6%prior 19
Gravel5 (1.1%)
→0.0%prior 5
Sand2 (0.4%)
Slush2 (0.4%)
↓-77.8%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

Ford, Chevrolet, and Dodge remained the top three vehicle makes involved in crashes in both 2016 and 2017, with little change in their overall counts. The demographic profile of persons involved in crashes saw some shifts, as the number of individuals in the 21-25 age group increased from 100 to 119 and the 65+ age group grew from 112 to 128. Conversely, involvement for the 16-20 age group saw a slight decrease from 144 to 137 persons.

Top Vehicle Makes (864 vehicles)

1
FORD145 (16.8%)
↓-7.1%prior 156
2
CHEV123 (14.2%)
↑26.8%prior 97
3
CHEVROLET61 (7.1%)
↓-28.2%prior 85
4
TOYT57 (6.6%)
↑83.9%prior 31
5
DODG51 (5.9%)
↑21.4%prior 42
6
DODGE32 (3.7%)
↓-27.3%prior 44
7
JEEP31 (3.6%)
↑6.9%prior 29
8
TOYOTA28 (3.2%)
↓-6.7%prior 30
9
CHRY25 (2.9%)
↑38.9%prior 18
10
GMC24 (2.8%)
↓-17.2%prior 29

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (639 persons with recorded sex)

Male358 (56.0%)
↓-1.6%prior 364
Female281 (44.0%)
↑1.1%prior 278

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 531
  • Total persons involved: 1,016
  • Total vehicles involved: 864

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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