Yearly Traffic Safety Analysis

119 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Shelby County, total crashes remained relatively stable, with 119 incidents in 2017 compared to 117 in 2016. While overall volume changed little, the number of fatalities rose from 2 to 3. The most significant shift in contributing factors was a sharp increase in crashes attributed to drivers losing control, which grew from 9 incidents in 2016 to 23 in 2017.

119

▲ 1.7%was 117

Total Crash Events

3

▲ 50.0%was 2

Persons Killed

54

▲ 8.0%was 50

Persons Injured

3

▲ 50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (3) 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

Crash trends in Shelby County showed a slight increase year-over-year. Total crashes rose by 1.7% from 117 in 2016 to 119 in 2017. Similarly, total injuries increased from 50 to 54, and fatalities rose from 2 to 3 in the same period.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

2

Motorists Killed

Prior: 2→0.0%

0

Pedestrians Injured

Prior: 0→0.0%

54

Motorists Injured

Prior: 49↑10.2%

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 periods. In 2017, Tuesday was the most frequent day for crashes with 24 incidents, a change from 2016 when Friday was the peak day with 23 crashes. The peak time for collisions remained in the late afternoon; the hours of 4 p.m., 5 p.m., and 6 p.m. each saw 9 crashes in 2017, compared to a single peak hour at 5 p.m. in 2016 with 11 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 severity of crashes worsened slightly in 2017 compared to 2016. The number of fatal crashes increased from 2 to 3, and the fatal crash rate rose from 1.7% to 2.5% of all incidents. While the count of serious injury crashes remained stable at 6, crashes resulting in possible injuries increased from 13 to 18. Correspondingly, the share of crashes with no injuries decreased from 70.9% in 2016 to 65.5% in 2017.

Outcome by Severity (Crash Events)

Fatal3fatal crashes2.5%
↑50.0%prior 2
Serious Injury6serious injury crashes5%
→0.0%prior 6
Minor Injury14minor injury crashes11.8%
↑7.7%prior 13
Possible Injury18possible injury crashes15.1%
↑38.5%prior 13
No Injury78no injury crashes65.5%
↓-6.0%prior 83

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

The leading causes of crashes changed significantly year-over-year. In 2017, 'Lost Control' became the top contributing factor, accounting for 23 crashes, a notable increase from 9 incidents in 2016. Conversely, crashes involving an 'Animal', the top factor in 2016 with 22 incidents, decreased to 17 crashes in 2017. Incidents where a driver 'Ran off road - straight' also increased, rising from 5 in 2016 to 15 in 2017.

Officer-Reported Primary Contributing Cause

Lost Control23 (19.3%)↑155.6%prior 9
Animal17 (14.3%)↓-22.7%prior 22
Ran off road - straight15 (12.6%)↑200.0%prior 5
Other (explain in narrative): Other11 (9.2%)↑37.5%prior 8
FTYROW: From stop sign8 (6.7%)
FTYROW: Making left turn5 (4.2%)↓-28.6%prior 7
Driving too fast for conditions5 (4.2%)↓-16.7%prior 6
Followed too close5 (4.2%)
Ran Stop Sign3 (2.5%)
Ran off road - left3 (2.5%)

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

Road & Environmental Conditions

While most crashes in both years occurred in daylight and on dry roads, there was an increase in incidents under adverse weather. In 2017, 7 crashes occurred during freezing rain and 5 in snow, compared to 5 and 1, respectively, in 2016. The number of crashes on snowy road surfaces also increased from 4 in 2016 to 8 in 2017. The proportion of crashes in clear weather and on dry roads remained largely consistent across both periods.

Weather

Clear69 (67.6%)
↑6.2%prior 65
Cloudy16 (15.7%)
→0.0%prior 16
Freezing rain/drizzle7 (6.9%)
↑40.0%prior 5
Snow5 (4.9%)
Fog, smoke, smog2 (2.0%)
Blowing Snow2 (2.0%)
Rain1 (1.0%)

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

Lighting

Daylight66 (62.9%)
↑1.5%prior 65
Dark - roadway not lighted21 (20.0%)
↓-8.7%prior 23
Dark - roadway lighted11 (10.5%)
↑37.5%prior 8
Dawn3 (2.9%)
Dusk3 (2.9%)
Dark - unknown roadway lighting1 (1.0%)

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

Road Surface

Dry72 (69.9%)
→0.0%prior 72
Wet10 (9.7%)
↑25.0%prior 8
Snow8 (7.8%)
Ice/frost7 (6.8%)
↓-22.2%prior 9
Gravel6 (5.8%)
→0.0%prior 6

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, though their counts shifted. In 2017, combining 'CHEV' and 'CHEVROLET' records, Chevrolet vehicles were involved in 41 crashes, making it the most common make, compared to 41 involvements in 2016. Ford vehicle involvements decreased from 41 in 2016 to 32 in 2017. Regarding person demographics, the 45-54 age group saw a decrease in involvement from 40 persons in 2016 to 31 in 2017, while the 0-15 age group saw an increase from 7 to 13 persons.

Top Vehicle Makes (175 vehicles)

1
FORD32 (18.3%)
↓-22.0%prior 41
2
CHEV28 (16%)
↑33.3%prior 21
3
CHEVROLET13 (7.4%)
↓-35.0%prior 20
4
DODGE8 (4.6%)
↑60.0%prior 5
5
DODG7 (4%)
↑16.7%prior 6
6
CHRY6 (3.4%)
↑20.0%prior 5
7
GMC5 (2.9%)
8
MERC5 (2.9%)
9
TOYT5 (2.9%)
10
PONTIAC4 (2.3%)
↓-33.3%prior 6

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

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

Sex Distribution (111 persons with recorded sex)

Male69 (62.2%)
↓-6.8%prior 74
Female42 (37.8%)
↓-22.2%prior 54

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: 119
  • Total persons involved: 208
  • Total vehicles involved: 175

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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