Yearly Traffic Safety Analysis

10,648 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Polk County, 2017 saw a slight decrease in total crashes, with 10,648 incidents compared to 10,675 in 2016, representing a 0.3% reduction. Despite this overall decrease, the number of total injuries rose by 7.9%, from 3,660 in 2016 to 3,950 in 2017. This indicates a shift towards a higher proportion of injury-involved crashes, even as total crash volume and fatalities saw a decline.

10,648

▼ -0.3%was 10,675

Total Crash Events

26

▼ -7.1%was 28

Persons Killed

3,950

▲ 7.9%was 3,660

Persons Injured

24

▼ -11.1%was 27

Fatal Crash Events

Note: "Persons Killed" (26) counts individual fatalities across all crash events. "Fatal" in the severity table below (24) 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, the total number of crashes in Polk County remained relatively stable year-over-year, decreasing slightly by 0.3% from 10,675 in 2016 to 10,648 in 2017. While total fatalities decreased by 7.1% (from 28 to 26), total injuries increased by 7.9% (from 3,660 to 3,950). This suggests that crashes, though slightly fewer in number, resulted in more injuries in 2017.

Vulnerable Road User Casualties

4

Pedestrians Killed

Prior: 5↓-20.0%

1

Cyclists Killed

Prior: 0→%

21

Motorists Killed

Prior: 23↓-8.7%

0

Other Killed

Prior: 0→0.0%

95

Pedestrians Injured

Prior: 110↓-13.6%

61

Cyclists Injured

Prior: 74↓-17.6%

3,791

Motorists Injured

Prior: 3,469↑9.3%

3

Other Injured

Prior: 7↓-57.1%

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 peak day for crashes remained Friday in both periods, with 1,920 crashes in 2017 and 1,845 in 2016. Similarly, the peak hour for crashes was 5 p.m. in both years, recording 1,077 crashes in 2017 and 1,135 in 2016. There were no significant shifts in the overall monthly distribution of crashes, with higher volumes generally observed in the later months of the year for both periods.

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

Fatal crashes decreased by 11.1%, from 27 incidents in 2016 to 24 in 2017, and the fatal crash rate decreased from 0.25% to 0.23%. Conversely, serious injury crashes (severity 'A') increased by 16.4% in count, rising from 134 to 156. Minor injury crashes (severity 'B') also increased by 7.9% in count, from 853 to 920, indicating a higher incidence of non-fatal injuries.

Severity is per crash event (most severe injury). 24 fatal crash events resulted in 26 persons killed.

Outcome by Severity (Crash Events)

Fatal24fatal crashes0.2%
↓-11.1%prior 27
Serious Injury156serious injury crashes1.5%
↑16.4%prior 134
Minor Injury920minor injury crashes8.6%
↑7.9%prior 853
Possible Injury2,418possible injury crashes22.7%
↑1.6%prior 2,379
No Injury7,130no injury crashes67%
↓-2.1%prior 7,282

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 top contributing factor, 'Followed too close,' remained consistent, though its count decreased slightly by 1.1% from 1,893 to 1,873 crashes. 'Driving too fast for conditions' saw a notable decrease of 93 crashes, a 15.2% reduction in count, falling from 612 to 519. In contrast, 'Ran Traffic Signal' incidents increased by 29 crashes, a 4.9% increase in count, rising from 586 to 615.

Officer-Reported Primary Contributing Cause

Followed too close1,873 (17.6%)↓-1.1%prior 1,893
Other (explain in narrative): Other746 (7%)↑5.8%prior 705
FTYROW: Making left turn724 (6.8%)↑8.2%prior 669
Ran Traffic Signal615 (5.8%)↑4.9%prior 586
Ran off road - left547 (5.1%)↓-13.6%prior 633
Driving too fast for conditions519 (4.9%)↓-15.2%prior 612
FTYROW: From stop sign498 (4.7%)↑3.5%prior 481
Operating vehicle in an reckless, erratic, careless, negligent manner420 (3.9%)↑2.2%prior 411
Lost Control345 (3.2%)↓-0.6%prior 347
Improper or erratic lane changing312 (2.9%)↑3.0%prior 303

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 proportion of crashes occurring in clear weather conditions increased from 58.8% to 62.0% year-over-year. Crashes occurring during snowy weather decreased by 28.5% in count, from 404 to 289. Incidents on icy or frosty road surfaces saw an even more substantial decrease of 46.4% in count, falling from 384 to 206, suggesting fewer adverse weather-related crashes in 2017.

Weather

Clear6,604 (63.5%)
↑5.1%prior 6,281
Cloudy2,602 (25.0%)
↓-7.0%prior 2,798
Rain685 (6.6%)
↓-0.7%prior 690
Snow289 (2.8%)
↓-28.5%prior 404
Freezing rain/drizzle105 (1.0%)
↓-24.5%prior 139
Fog, smoke, smog66 (0.6%)
↑46.7%prior 45
Blowing Snow24 (0.2%)
↓-36.8%prior 38
Severe Winds9 (0.1%)
↓-18.2%prior 11
Sleet, hail5 (0.0%)
↓-28.6%prior 7
Other (explain in narrative)4 (0.0%)

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

Lighting

Daylight7,489 (71.7%)
→0.0%prior 7,486
Dark - roadway lighted1,999 (19.1%)
↓-4.2%prior 2,087
Dark - roadway not lighted413 (4.0%)
↑3.3%prior 400
Dusk288 (2.8%)
↑6.3%prior 271
Dawn232 (2.2%)
↑19.0%prior 195
Dark - unknown roadway lighting22 (0.2%)
↓-12.0%prior 25

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

Road Surface

Dry8,337 (80.2%)
↑4.1%prior 8,007
Wet1,421 (13.7%)
↓-5.2%prior 1,499
Snow338 (3.3%)
↓-22.1%prior 434
Ice/frost206 (2.0%)
↓-46.4%prior 384
Slush59 (0.6%)
↓-28.9%prior 83
Gravel16 (0.2%)
↑45.5%prior 11
Sand6 (0.1%)
Mud, dirt6 (0.1%)
↑20.0%prior 5
Water (standing or moving)3 (0.0%)
Oil1 (0.0%)

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, such as Ford, Chevrolet, and Toyota, maintained their leading positions with relatively stable counts year-over-year. Analysis of persons involved in crashes revealed an increase in the '0-15' age group by 16.8% and the '65+' age group by 9.1%. Other age groups showed minor changes, with male involvement increasing slightly by 0.9% and female involvement decreasing by 0.3%.

Top Vehicle Makes (20,609 vehicles)

1
FORD2,970 (14.4%)
↓-4.3%prior 3,103
2
CHEV2,601 (12.6%)
↑20.3%prior 2,162
3
CHEVROLET1,225 (5.9%)
↓-26.6%prior 1,670
4
TOYT1,211 (5.9%)
↑19.5%prior 1,013
5
DODG864 (4.2%)
↑19.3%prior 724
6
HOND842 (4.1%)
↑23.8%prior 680
7
NR792 (3.8%)
↑1.9%prior 777
8
JEEP746 (3.6%)
↑4.2%prior 716
9
NISS702 (3.4%)
↑26.3%prior 556
10
TOYOTA572 (2.8%)
↓-21.4%prior 728

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

3,049 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (16,213 persons with recorded sex)

Male8,884 (54.8%)
↑0.9%prior 8,807
Female7,329 (45.2%)
↓-0.3%prior 7,351

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: 10,648
  • Total persons involved: 23,691
  • Total vehicles involved: 20,609

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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