Yearly Traffic Safety Analysis

11,346 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

Total crashes in Polk County increased from 10,645 in 2018 to 11,346 in 2019, marking a 6.58% rise. The most significant year-over-year shift was a 133.33% increase in pedestrian fatalities, rising from 3 in 2018 to 7 in 2019.

11,346

▲ 6.6%was 10,645

Total Crash Events

35

▲ 29.6%was 27

Persons Killed

3,945

▲ 8.5%was 3,635

Persons Injured

35

▲ 45.8%was 24

Fatal Crash Events

Note: "Persons Killed" (35) counts individual fatalities across all crash events. "Fatal" in the severity table below (35) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash metrics in Polk County showed an upward trend from 2018 to 2019. Total crashes increased by 6.58%, total fatalities rose by 29.63%, and total injuries saw an 8.53% increase.

Vulnerable Road User Casualties

7

Pedestrians Killed

Prior: 3↑133.3%

0

Cyclists Killed

Prior: 1↓-100.0%

28

Motorists Killed

Prior: 23↑21.7%

0

Other Killed

Prior: 0→0.0%

90

Pedestrians Injured

Prior: 92↓-2.2%

64

Cyclists Injured

Prior: 67↓-4.5%

3,789

Motorists Injured

Prior: 3,467↑9.3%

2

Other Injured

Prior: 9↓-77.8%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Friday in 2018 to Monday in 2019, with both days recording 1,838 crashes in their respective peak years. The peak hour also changed, moving from 5 PM in 2018 with 1,030 crashes to 4 PM in 2019 with 1,109 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The fatal crash rate increased from 0.23% in 2018 to 0.31% in 2019, with the number of fatal crashes rising from 24 to 35. While serious injury crashes (severity A) decreased slightly from 1.4% to 1.2% of total crashes, minor and possible injury crashes (severity B and C) saw slight increases in their proportions of total crashes.

Outcome by Severity (Crash Events)

Fatal35fatal crashes0.3%
↑45.8%prior 24
Serious Injury137serious injury crashes1.2%
↓-8.1%prior 149
Minor Injury975minor injury crashes8.6%
↑13.0%prior 863
Possible Injury2,424possible injury crashes21.4%
↑9.4%prior 2,216
No Injury7,775no injury crashes68.5%
↑5.2%prior 7,393

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

Among contributing factors, 'Driving too fast for conditions' saw a notable increase of 200 crashes, rising from 700 in 2018 to 900 in 2019, a 28.6% change in count. 'Ran off road - left' also increased by 152 crashes, from 539 to 691, representing a 28.2% change in count. Conversely, crashes attributed to 'Lost Control' decreased by 52, from 325 to 273, a 16.0% reduction in count.

Officer-Reported Primary Contributing Cause

Followed too close1,926 (17%)↑1.0%prior 1,907
Driving too fast for conditions900 (7.9%)↑28.6%prior 700
Other (explain in narrative): Other804 (7.1%)↑6.6%prior 754
Ran off road - left691 (6.1%)↑28.2%prior 539
FTYROW: Making left turn656 (5.8%)↓-0.2%prior 657
Ran Traffic Signal606 (5.3%)↓-0.5%prior 609
FTYROW: From stop sign484 (4.3%)↑11.0%prior 436
Operating vehicle in an reckless, erratic, careless, negligent manner426 (3.8%)↑3.6%prior 411
Improper or erratic lane changing356 (3.1%)↓-1.7%prior 362
Driver Distraction: Other interior distraction339 (3%)↑10.1%prior 308

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in snowy weather conditions increased by 176, from 337 in 2018 to 513 in 2019, a 52.2% change. Similarly, crashes on icy/frosty road surfaces increased by 259, from 494 to 753, a 52.4% change. Daylight crashes increased by 699, from 7,425 in 2018 to 8,124 in 2019.

Weather

Clear6,729 (60.7%)
↑2.7%prior 6,550
Cloudy2,774 (25.0%)
↑12.5%prior 2,466
Rain739 (6.7%)
↓-6.6%prior 791
Snow513 (4.6%)
↑52.2%prior 337
Freezing rain/drizzle142 (1.3%)
↓-17.0%prior 171
Blowing Snow125 (1.1%)
↑204.9%prior 41
Fog, smoke, smog27 (0.2%)
↑12.5%prior 24
Severe Winds25 (0.2%)
↑177.8%prior 9
Sleet, hail10 (0.1%)
↓-9.1%prior 11
Other (explain in narrative)10 (0.1%)
↓-16.7%prior 12

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight8,124 (72.9%)
↑9.4%prior 7,425
Dark - roadway lighted2,106 (18.9%)
↑0.6%prior 2,093
Dark - roadway not lighted400 (3.6%)
↓-4.5%prior 419
Dusk273 (2.4%)
↑5.4%prior 259
Dawn219 (2.0%)
↓-1.4%prior 222
Dark - unknown roadway lighting29 (0.3%)
↓-3.3%prior 30

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry7,782 (70.1%)
↑2.2%prior 7,612
Wet1,711 (15.4%)
↓-2.2%prior 1,749
Ice/frost753 (6.8%)
↑52.4%prior 494
Snow684 (6.2%)
↑53.7%prior 445
Slush141 (1.3%)
↑67.9%prior 84
Other (explain in narrative)10 (0.1%)
Gravel7 (0.1%)
↓-12.5%prior 8
Mud, dirt5 (0.0%)
↓-16.7%prior 6
Sand3 (0.0%)
Water (standing or moving)2 (0.0%)
↓-71.4%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased by 7.15%, from 20,641 in 2018 to 22,117 in 2019. The 26-34 age group saw the largest increase in persons involved in crashes, rising by 797 (19.06%). Ford and Chevrolet remained the top two vehicle makes involved in crashes, both showing slight increases in their counts.

Top Vehicle Makes (22,117 vehicles)

1
FORD3,180 (14.4%)
↑3.7%prior 3,066
2
CHEV2,855 (12.9%)
↑3.4%prior 2,761
3
CHEVROLET1,215 (5.5%)
↑32.1%prior 920
4
TOYT1,130 (5.1%)
↓-9.7%prior 1,251
5
NR1,023 (4.6%)
↑24.8%prior 820
6
JEEP881 (4%)
↑22.4%prior 720
7
HOND858 (3.9%)
↓-16.3%prior 1,025
8
DODG834 (3.8%)
↓-6.2%prior 889
9
NISS823 (3.7%)
↑1.5%prior 811
10
KIA596 (2.7%)
↑10.6%prior 539

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

4,335 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (19,624 persons with recorded sex)

Male10,925 (55.7%)
↑17.8%prior 9,274
Female8,699 (44.3%)
↑14.2%prior 7,615

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 11,346
  • Total persons involved: 28,379
  • Total vehicles involved: 22,117

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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