Yearly Traffic Safety Analysis

2,163 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Pottawattamie County recorded 2,163 total crashes, a 5.6% increase from the 2,048 crashes documented in 2016. While the total number of fatalities remained unchanged at 12, there was a notable drop in pedestrian fatalities from three in the prior year to zero in 2017. Crashes involving a driver under the influence also increased by 16.3%, from 80 incidents in 2016 to 93 in 2017.

2,163

▲ 5.6%was 2,048

Total Crash Events

12

Persons Killed

729

▼ -1.0%was 736

Persons Injured

10

▼ -16.7%was 12

Fatal Crash Events

Note: "Persons Killed" (12) counts individual fatalities across all crash events. "Fatal" in the severity table below (10) 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, traffic crashes in Pottawattamie County increased by 5.6% from 2016 to 2017, rising from 2,048 to 2,163 incidents. Despite the rise in total crashes, the number of injuries saw a slight decline of approximately 1%, from 736 to 729. The number of fatalities held steady at 12 for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 3↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

12

Motorists Killed

Prior: 9↑33.3%

16

Pedestrians Injured

Prior: 18↓-11.1%

16

Cyclists Injured

Prior: 16→0.0%

697

Motorists Injured

Prior: 700↓-0.4%

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 Monday (336 crashes) in the prior year to Friday (366 crashes) in the current year. However, the peak hour for collisions remained consistent, with the 3 p.m. hour seeing the highest volume in both periods, recording 170 crashes in both 2016 and 2017.

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 slightly between the two periods. The number of fatal crashes decreased from 12 in 2016 to 10 in 2017, and their share of all crashes fell from 0.6% to 0.5%. The overall proportion of crashes resulting in an injury remained stable near 30%. However, the count of serious injury crashes fell from 58 to 52, while minor injury crashes increased from 175 to 203.

Severity is per crash event (most severe injury). 10 fatal crash events resulted in 12 persons killed.

Outcome by Severity (Crash Events)

Fatal10fatal crashes0.5%
↓-16.7%prior 12
Serious Injury52serious injury crashes2.4%
↓-10.3%prior 58
Minor Injury203minor injury crashes9.4%
↑16.0%prior 175
Possible Injury401possible injury crashes18.5%
↑2.6%prior 391
No Injury1,497no injury crashes69.2%
↑6.0%prior 1,412

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 contributing factors to crashes remained largely consistent, though some counts shifted. 'Followed too close' was the top factor in both periods, with its crash count increasing from 275 in 2016 to 305 in 2017. Collisions involving an animal also rose from 188 to 202 incidents, holding the second rank. A notable change was seen in crashes attributed to 'Ran Traffic Signal', which decreased from 92 in 2016 to 71 in 2017, dropping it from the top five contributing factors.

Officer-Reported Primary Contributing Cause

Followed too close305 (14.1%)↑10.9%prior 275
Animal202 (9.3%)↑7.4%prior 188
Lost Control145 (6.7%)→0.0%prior 145
Ran off road - left136 (6.3%)↑4.6%prior 130
Other (explain in narrative): Other110 (5.1%)↑18.3%prior 93
Ran off road - straight99 (4.6%)↑30.3%prior 76
Ran Stop Sign83 (3.8%)↑25.8%prior 66
Driving too fast for conditions78 (3.6%)↓-6.0%prior 83
FTYROW: Making left turn75 (3.5%)↑29.3%prior 58
FTYROW: From stop sign74 (3.4%)↓-3.9%prior 77

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 daylight on dry roads. In 2017, 60.1% of crashes happened in daylight, a slight decrease from 62.4% in 2016, with a corresponding increase in the share of crashes occurring in dark conditions. The percentage of crashes taking place in clear weather decreased from 65.2% to 62.5% year-over-year. The share of crashes on non-dry road surfaces, such as wet or icy roads, remained relatively stable, changing from 27.8% in 2016 to 26.5% in 2017.

Weather

Clear1,352 (68.6%)
↑1.2%prior 1,336
Cloudy374 (19.0%)
↑20.6%prior 310
Rain127 (6.4%)
↑35.1%prior 94
Snow49 (2.5%)
↓-18.3%prior 60
Freezing rain/drizzle39 (2.0%)
↑21.9%prior 32
Fog, smoke, smog17 (0.9%)
↑54.5%prior 11
Severe Winds6 (0.3%)
↓-33.3%prior 9
Blowing Snow6 (0.3%)
↓-68.4%prior 19
Sleet, hail2 (0.1%)

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

Lighting

Daylight1,300 (65.0%)
↑1.6%prior 1,279
Dark - roadway lighted365 (18.2%)
↑5.8%prior 345
Dark - roadway not lighted222 (11.1%)
↑29.1%prior 172
Dusk56 (2.8%)
↑51.4%prior 37
Dawn52 (2.6%)
↑10.6%prior 47
Dark - unknown roadway lighting6 (0.3%)
↑20.0%prior 5

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

Road Surface

Dry1,589 (80.3%)
↑7.4%prior 1,479
Wet235 (11.9%)
↑8.3%prior 217
Ice/frost69 (3.5%)
↓-21.6%prior 88
Snow54 (2.7%)
↓-6.9%prior 58
Gravel21 (1.1%)
↓-16.0%prior 25
Slush5 (0.3%)
↓-50.0%prior 10
Other (explain in narrative)4 (0.2%)
Sand2 (0.1%)
Mud, dirt1 (0.1%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes were similar across both years. Ford was the most frequently listed vehicle make in both 2017 (561 vehicles) and 2016 (572 vehicles), followed by Chevrolet. When examining the age of persons involved, the 26-34 age group was the largest in 2017 with 617 individuals, similar to 2016. The share of individuals aged 65 and older involved in crashes increased slightly from 10.0% in 2016 to 10.5% in 2017.

Top Vehicle Makes (3,753 vehicles)

1
FORD561 (14.9%)
↓-1.9%prior 572
2
CHEV371 (9.9%)
↑33.5%prior 278
3
CHEVROLET319 (8.5%)
↓-13.8%prior 370
4
NR161 (4.3%)
↑21.1%prior 133
5
KIA135 (3.6%)
↑16.4%prior 116
6
DODG130 (3.5%)
↑11.1%prior 117
7
DODGE118 (3.1%)
↓-17.5%prior 143
8
NISSAN109 (2.9%)
↑11.2%prior 98
9
JEEP109 (2.9%)
↑12.4%prior 97
10
TOYOTA105 (2.8%)
↑1.9%prior 103

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

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

Sex Distribution (2,585 persons with recorded sex)

Male1,466 (56.7%)
↑3.2%prior 1,421
Female1,119 (43.3%)
↑6.7%prior 1,049

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: 2,163
  • Total persons involved: 4,402
  • Total vehicles involved: 3,753

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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