Yearly Traffic Safety Analysis

218 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Delaware County recorded 218 total crashes, a 17.4% decrease from the 264 crashes reported in 2016. In the same period, total fatalities fell from 4 to 2, and total injuries decreased from 62 to 56. One of the most significant year-over-year changes was the reduction in crashes involving driving under the influence, which dropped from 13 incidents in 2016 to 4 in 2017.

218

▼ -17.4%was 264

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

56

▼ -9.7%was 62

Persons Injured

2

▼ -50.0%was 4

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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety metrics in Delaware County showed a general improvement from 2016 to 2017. Total crashes declined by 17.4%, from 264 to 218. This downward trend was also observed in crash outcomes, with total fatalities decreasing from 4 to 2 and the number of injuries falling from 62 to 56.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 4↓-50.0%

0

Other Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 1→0.0%

54

Motorists Injured

Prior: 58↓-6.9%

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 timing of crashes shifted between the two years. In 2017, the peak day for crashes was Monday with 41 incidents, a change from 2016 when Saturday had the most crashes at 50. The most common crash time also moved later in the day, from the 3 PM hour in 2016 (24 crashes) to the 5 PM hour in 2017 (23 crashes).

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 overall severity of crashes lessened year-over-year, as fatal crashes were halved from 4 in 2016 to 2 in 2017, and the fatal crash rate dropped from 1.52 to 0.92 per 100 crashes. However, the number of serious injury crashes increased from 3 to 8. The proportion of crashes resulting in any level of injury (possible, minor, or serious) increased slightly from 20.5% in 2016 to 22.5% in 2017, even as the total number of injury-causing crashes fell from 54 to 49.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.9%
↓-50.0%prior 4
Serious Injury8serious injury crashes3.7%
↑166.7%prior 3
Minor Injury15minor injury crashes6.9%
↓-37.5%prior 24
Possible Injury26possible injury crashes11.9%
↓-3.7%prior 27
No Injury167no injury crashes76.6%
↓-18.9%prior 206

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 with an animal remained the leading contributing factor in both periods, with the count of such incidents increasing from 79 in 2016 to 86 in 2017. This represented a rise in its share of total crashes from 29.9% to 39.4%. Other major factors saw decreases in count, including 'Lost Control' (from 29 to 27 crashes), 'Ran off road - straight' (from 19 to 7), and 'Ran Stop Sign' (from 11 to 7).

Officer-Reported Primary Contributing Cause

Animal86 (39.4%)↑8.9%prior 79
Lost Control27 (12.4%)↓-6.9%prior 29
Followed too close13 (6%)↑8.3%prior 12
Other (explain in narrative): Other7 (3.2%)↓-36.4%prior 11
Ran Stop Sign7 (3.2%)↓-36.4%prior 11
Ran off road - straight7 (3.2%)↓-63.2%prior 19
FTYROW: From stop sign6 (2.8%)↓-50.0%prior 12
Driving too fast for conditions6 (2.8%)↓-25.0%prior 8
FTYROW: Making left turn5 (2.3%)↓-16.7%prior 6
Passing: Other passing (explain in narrative)4 (1.8%)

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 clear weather and daylight conditions made up a smaller proportion of the total in 2017 compared to 2016. The share of crashes on dry roads remained relatively stable, at 50.5% in 2017 versus 49.2% in 2016. A notable change was observed in crashes on adverse road surfaces, with incidents on icy or frosty roads more than doubling from 6 in 2016 to 13 in 2017.

Weather

Clear101 (69.7%)
↓-24.1%prior 133
Cloudy27 (18.6%)
↓-15.6%prior 32
Rain6 (4.1%)
↓-33.3%prior 9
Snow6 (4.1%)
↓-33.3%prior 9
Freezing rain/drizzle4 (2.8%)
Fog, smoke, smog1 (0.7%)

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

Lighting

Daylight96 (67.1%)
↓-26.2%prior 130
Dark - roadway not lighted36 (25.2%)
↓-14.3%prior 42
Dark - roadway lighted7 (4.9%)
↓-36.4%prior 11
Dawn2 (1.4%)
↓-66.7%prior 6
Dusk2 (1.4%)
↓-66.7%prior 6

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

Road Surface

Dry110 (75.9%)
↓-15.4%prior 130
Ice/frost13 (9.0%)
↑116.7%prior 6
Wet10 (6.9%)
↓-50.0%prior 20
Gravel8 (5.5%)
↓-46.7%prior 15
Slush2 (1.4%)
↓-71.4%prior 7
Snow2 (1.4%)
↓-87.5%prior 16

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

Vehicles & Demographics

The most common vehicle makes involved in crashes were consistent, with Chevrolet and Ford ranking highest in both years; the count for Chevrolets dropped from 101 to 77, while Fords remained stable at 47. Regarding driver age, there was a significant decrease in the 55-64 age group's involvement, which fell from 64 persons in 2016 to 32 in 2017. Conversely, the number of individuals in the 16-20 age group involved in crashes increased slightly from 57 to 60, despite an overall decrease in the total number of people involved in crashes.

Top Vehicle Makes (294 vehicles)

1
CHEV50 (17%)
↑6.4%prior 47
2
FORD47 (16%)
↑2.2%prior 46
3
CHEVROLET27 (9.2%)
↓-50.0%prior 54
4
BUIC14 (4.8%)
↑27.3%prior 11
5
JEEP12 (4.1%)
↑50.0%prior 8
6
GMC12 (4.1%)
↓-25.0%prior 16
7
DODG11 (3.7%)
↓-38.9%prior 18
8
DODGE7 (2.4%)
↓-53.3%prior 15
9
KIA7 (2.4%)
10
BUICK6 (2%)

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

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

Sex Distribution (208 persons with recorded sex)

Male122 (58.7%)
↓-25.2%prior 163
Female86 (41.3%)
↓-29.5%prior 122

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: 218
  • Total persons involved: 340
  • Total vehicles involved: 294

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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