Yearly Traffic Safety Analysis

1,570 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Story County, total vehicle crashes decreased by 2.4% from 1,608 in 2016 to 1,570 in 2017. Despite the overall decline in crash volume, the number of injuries rose by 10.8% from 434 to 481, and fatalities increased from 4 to 5. A notable year-over-year shift was a 50% increase in crashes involving a driver under the influence (DUI), which grew from 30 incidents in 2016 to 45 in 2017.

1,570

▼ -2.4%was 1,608

Total Crash Events

5

▲ 25.0%was 4

Persons Killed

481

▲ 10.8%was 434

Persons Injured

5

▲ 25.0%was 4

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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

While the total number of crashes in Story County saw a slight year-over-year decrease of 2.4%, from 1,608 to 1,570, the severity of these incidents increased. Total injuries rose by 10.8% (from 434 to 481), and the number of fatalities increased from 4 to 5. This suggests a trend towards fewer but more severe collisions in 2017 compared to the prior year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 4↑25.0%

0

Other Killed

Prior: 0→0.0%

10

Pedestrians Injured

Prior: 24↓-58.3%

21

Cyclists Injured

Prior: 25↓-16.0%

449

Motorists Injured

Prior: 383↑17.2%

1

Other Injured

Prior: 2↓-50.0%

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 remained consistent year-over-year, with Friday being the peak day and 5 p.m. the peak hour in both 2016 and 2017. However, the volume of crashes during these peak times intensified. Crashes on Fridays increased from 286 to 338, and incidents during the 5 p.m. hour rose from 151 to 168.

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 increased from 2016 to 2017. The fatal crash rate rose from 0.25 to 0.32 per 100 crashes, with fatal incidents increasing from 4 to 5. The number of serious injury crashes also grew from 28 to 33. Consequently, the proportion of crashes resulting in no injuries decreased from 77.2% in 2016 to 75.7% in 2017.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.3%
↑25.0%prior 4
Serious Injury33serious injury crashes2.1%
↑17.9%prior 28
Minor Injury127minor injury crashes8.1%
↑7.6%prior 118
Possible Injury217possible injury crashes13.8%
↑0.5%prior 216
No Injury1,188no injury crashes75.7%
↓-4.3%prior 1,242

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 remained consistent, with "Followed too close" being the primary cause in both years, increasing slightly from 271 to 276 incidents. Collisions involving animals were the second most common factor, with a slight decrease from 158 to 154 incidents. Notably, crashes attributed to "Driving too fast for conditions" decreased by 24.3% in count, falling from 144 to 109, while incidents involving "Lost Control" increased from 48 to 64.

Officer-Reported Primary Contributing Cause

Followed too close276 (17.6%)↑1.8%prior 271
Animal154 (9.8%)↓-2.5%prior 158
Driving too fast for conditions109 (6.9%)↓-24.3%prior 144
FTYROW: Making left turn102 (6.5%)↓-12.8%prior 117
FTYROW: From stop sign83 (5.3%)↓-3.5%prior 86
Other (explain in narrative): Other80 (5.1%)↓-19.2%prior 99
Improper or erratic lane changing66 (4.2%)↑37.5%prior 48
Ran off road - straight65 (4.1%)↑41.3%prior 46
Lost Control64 (4.1%)↑33.3%prior 48
Ran off road - left59 (3.8%)↑13.5%prior 52

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

Road & Environmental Conditions

The majority of crashes in both 2016 and 2017 occurred in clear weather, during daylight hours, and on dry roads. The proportion of crashes under these favorable conditions remained stable year-over-year. There was a notable decrease in crashes on roads with ice or frost, which fell from 99 incidents in 2016 to 68 in 2017. Crashes on wet roads remained relatively unchanged, with 164 in 2016 and 156 in 2017.

Weather

Clear916 (63.7%)
↑1.8%prior 900
Cloudy285 (19.8%)
↓-13.4%prior 329
Rain99 (6.9%)
↑15.1%prior 86
Snow85 (5.9%)
↑1.2%prior 84
Freezing rain/drizzle25 (1.7%)
↓-16.7%prior 30
Blowing Snow16 (1.1%)
↓-15.8%prior 19
Fog, smoke, smog11 (0.8%)

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

Lighting

Daylight1,038 (72.3%)
↓-4.3%prior 1,085
Dark - roadway lighted218 (15.2%)
↑8.5%prior 201
Dark - roadway not lighted120 (8.4%)
↑12.1%prior 107
Dusk40 (2.8%)
↑8.1%prior 37
Dawn17 (1.2%)
↓-46.9%prior 32
Dark - unknown roadway lighting3 (0.2%)

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

Road Surface

Dry1,104 (76.8%)
↑3.0%prior 1,072
Wet156 (10.8%)
↓-4.9%prior 164
Snow85 (5.9%)
↑4.9%prior 81
Ice/frost68 (4.7%)
↓-31.3%prior 99
Gravel14 (1.0%)
↑7.7%prior 13
Slush8 (0.6%)
↓-73.3%prior 30
Mud, dirt2 (0.1%)
Water (standing or moving)1 (0.1%)

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

Vehicles & Demographics

The demographic profile of persons involved in crashes showed a shift, with the 21-25 age group decreasing from 636 to 582 individuals, while the 16-20 age group increased slightly from 565 to 583. Ford and Chevrolet models remained the most frequently involved vehicle makes in both years. The number of Ford vehicles in crashes rose from 419 to 446, while the combined total for Chevrolet vehicles (coded as 'CHEV' and 'CHEVROLET') decreased from 535 to 501.

Top Vehicle Makes (2,843 vehicles)

1
FORD446 (15.7%)
↑6.4%prior 419
2
CHEV329 (11.6%)
↑20.5%prior 273
3
TOYT220 (7.7%)
↑22.2%prior 180
4
CHEVROLET172 (6%)
↓-34.4%prior 262
5
HOND131 (4.6%)
↑19.1%prior 110
6
TOYOTA112 (3.9%)
↓-13.2%prior 129
7
DODG111 (3.9%)
↑11.0%prior 100
8
NISS91 (3.2%)
↑59.6%prior 57
9
JEEP90 (3.2%)
↑5.9%prior 85
10
HONDA75 (2.6%)
↓-24.2%prior 99

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

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

Sex Distribution (2,255 persons with recorded sex)

Male1,229 (54.5%)
↓-5.7%prior 1,303
Female1,026 (45.5%)
↓-0.6%prior 1,032

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: 1,570
  • Total persons involved: 3,185
  • Total vehicles involved: 2,843

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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