Yearly Traffic Safety Analysis

2,337 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Woodbury County, there were 2,337 total crashes in 2017, a slight decrease of 0.7% from the 2,354 crashes recorded in 2016. While total crashes remained relatively stable, the number of people injured in these incidents fell by 15.2%, from 934 in 2016 to 792 in 2017. Crashes involving a driver under the influence, however, increased from 86 to 98, a rise of 14.0%.

2,337

▼ -0.7%was 2,354

Total Crash Events

8

▼ -20.0%was 10

Persons Killed

792

▼ -15.2%was 934

Persons Injured

6

▼ -33.3%was 9

Fatal Crash Events

Note: "Persons Killed" (8) counts individual fatalities across all crash events. "Fatal" in the severity table below (6) 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 crash volume in Woodbury County was stable, with a minor 0.7% decrease from 2,354 crashes in 2016 to 2,337 in 2017. Despite the stability in total incidents, outcomes improved, as total injuries dropped by 15.2% and the number of people killed in crashes decreased from 10 to 8 year-over-year.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

7

Motorists Killed

Prior: 10↓-30.0%

0

Other Killed

Prior: 0→0.0%

26

Pedestrians Injured

Prior: 24↑8.3%

20

Cyclists Injured

Prior: 21↓-4.8%

740

Motorists Injured

Prior: 885↓-16.4%

6

Other Injured

Prior: 4↑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 in Woodbury County showed consistency year-over-year. Friday remained the peak day for crashes in both 2017 (417 crashes) and 2016 (433 crashes). The peak hour for collisions shifted slightly earlier, moving from 5 p.m. in 2016 (199 crashes) to 4 p.m. in 2017 (210 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 saw a notable shift between 2016 and 2017. The fatal crash rate decreased from 0.38% to 0.26%, with the number of fatal crashes falling from 9 to 6. While the count of serious injury crashes increased from 32 to 39, crashes resulting in possible injuries saw a significant drop from 564 to 486. This contributed to a decrease in the overall proportion of injury-related crashes.

Severity is per crash event (most severe injury). 6 fatal crash events resulted in 8 persons killed.

Outcome by Severity (Crash Events)

Fatal6fatal crashes0.3%
↓-33.3%prior 9
Serious Injury39serious injury crashes1.7%
↑21.9%prior 32
Minor Injury208minor injury crashes8.9%
↑1.5%prior 205
Possible Injury486possible injury crashes20.8%
↓-13.8%prior 564
No Injury1,598no injury crashes68.4%
↑3.5%prior 1,544

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 for crashes showed some shifts between periods. 'Followed too close' remained the primary factor in both years, with a slight increase in count from 306 incidents in 2016 to 312 in 2017. The count for 'Ran off road - left' crashes decreased from 184 to 154, while incidents attributed to 'Driving too fast for conditions' rose from 122 to 150. 'Failure to yield from a stop sign' also saw an increase, growing from 150 to 164 incidents and becoming the second-most cited factor in 2017.

Officer-Reported Primary Contributing Cause

Followed too close312 (13.4%)↑2.0%prior 306
FTYROW: From stop sign164 (7%)↑9.3%prior 150
Ran off road - left154 (6.6%)↓-16.3%prior 184
Driving too fast for conditions150 (6.4%)↑23.0%prior 122
Ran Traffic Signal121 (5.2%)↑2.5%prior 118
Animal120 (5.1%)→0.0%prior 120
Other (explain in narrative): Other120 (5.1%)↓-17.2%prior 145
Lost Control118 (5%)↑15.7%prior 102
FTYROW: Making left turn109 (4.7%)↓-18.7%prior 134
Ran Stop Sign105 (4.5%)↑14.1%prior 92

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

Road & Environmental Conditions

Crash conditions remained largely consistent between 2016 and 2017. In both years, the majority of incidents occurred in clear weather (1,430 in 2017 vs. 1,446 in 2016) and on dry road surfaces (1,667 vs. 1,652). Crashes during daylight hours also represented a stable majority, accounting for 1,519 incidents in 2017 compared to 1,542 in the prior year. There were no significant shifts in the proportions of crashes occurring under adverse conditions.

Weather

Clear1,430 (64.3%)
↓-1.1%prior 1,446
Cloudy548 (24.6%)
↑0.7%prior 544
Rain115 (5.2%)
↑25.0%prior 92
Snow80 (3.6%)
↓-9.1%prior 88
Freezing rain/drizzle31 (1.4%)
↑29.2%prior 24
Fog, smoke, smog6 (0.3%)
↓-40.0%prior 10
Blowing Snow5 (0.2%)
↓-50.0%prior 10
Sleet, hail5 (0.2%)
Severe Winds2 (0.1%)
Other (explain in narrative)2 (0.1%)

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

Lighting

Daylight1,519 (68.1%)
↓-1.5%prior 1,542
Dark - roadway lighted434 (19.5%)
↑8.0%prior 402
Dark - roadway not lighted150 (6.7%)
↓-2.0%prior 153
Dusk77 (3.5%)
↑5.5%prior 73
Dawn37 (1.7%)
↓-30.2%prior 53
Dark - unknown roadway lighting12 (0.5%)
↑71.4%prior 7

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

Road Surface

Dry1,667 (74.9%)
↑0.9%prior 1,652
Wet271 (12.2%)
↑7.1%prior 253
Snow136 (6.1%)
↓-14.5%prior 159
Ice/frost83 (3.7%)
↑1.2%prior 82
Gravel36 (1.6%)
↑20.0%prior 30
Slush29 (1.3%)
↓-29.3%prior 41
Other (explain in narrative)3 (0.1%)
Sand1 (0.0%)
Mud, dirt1 (0.0%)

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

Vehicles & Demographics

The demographics of persons involved in crashes showed a notable increase in the 16-20 age group, which grew from 579 individuals in 2016 to 653 in 2017. In contrast, the 26-34 and 45-54 age brackets saw decreases in involvement. The distribution of vehicle makes involved in crashes remained stable, with Ford (675 vehicles in 2017 vs. 662 in 2016) and Chevrolet (895 vs. 874, combining 'CHEV' and 'CHEVROLET' entries) consistently being the most common makes.

Top Vehicle Makes (4,364 vehicles)

1
FORD675 (15.5%)
↑2.0%prior 662
2
CHEV507 (11.6%)
↑31.3%prior 386
3
CHEVROLET388 (8.9%)
↓-20.5%prior 488
4
HOND156 (3.6%)
↑52.9%prior 102
5
GMC151 (3.5%)
↓-9.6%prior 167
6
JEEP149 (3.4%)
↓-13.4%prior 172
7
TOYT145 (3.3%)
↑40.8%prior 103
8
DODGE140 (3.2%)
↓-16.7%prior 168
9
NR140 (3.2%)
↓-4.1%prior 146
10
DODG132 (3%)
↓-8.3%prior 144

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

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

Sex Distribution (3,164 persons with recorded sex)

Male1,771 (56.0%)
↑3.4%prior 1,712
Female1,393 (44.0%)
↑8.1%prior 1,289

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: 2,337
  • Total persons involved: 5,082
  • Total vehicles involved: 4,364

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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