Monthly Traffic Safety Analysis

5,637 CRASHES IN
IOWA, IA
JANUARY 2019

All metrics benchmarked againstJanuary 2018

In January 2019, Iowa recorded 5,637 total vehicle crashes, an 11.2% increase from the 5,068 crashes documented in January 2018. While total fatalities decreased by 16% from 25 to 21, the number of reported injuries rose by 11.6%. The most notable year-over-year shift was a 54.9% increase in crashes attributed to 'Driving too fast for conditions,' which grew from 610 to 945 incidents.

5,637

▲ 11.2%was 5,068

Total Crash Events

21

▼ -16.0%was 25

Persons Killed

1,478

▲ 11.6%was 1,324

Persons Injured

19

▼ -13.6%was 22

Fatal Crash Events

Note: "Persons Killed" (21) counts individual fatalities across all crash events. "Fatal" in the severity table below (19) 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 2019-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic collisions showed an upward trend in January 2019 compared to the previous year. The total number of crashes increased by 11.2%, rising from 5,068 to 5,637. Similarly, the number of individuals injured in these incidents grew by 11.6% from 1,324 to 1,478, while fatalities saw a 16% decrease from 25 to 21.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

21

Motorists Killed

Prior: 25↓-16.0%

17

Pedestrians Injured

Prior: 40↓-57.5%

4

Cyclists Injured

Prior: 5↓-20.0%

1,457

Motorists Injured

Prior: 1,278↑14.0%

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

When Crashes Happen

The weekly pattern of crashes shifted between the two periods, with the peak day moving from Tuesday (930 crashes) in January 2018 to Saturday (1,049 crashes) in January 2019. The evening commute hour remained the most frequent time for crashes, with the 5 p.m. hour being the peak in both years. However, the volume of crashes during this peak hour increased from 393 to 443 year-over-year.

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

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

Crash Severity Breakdown

While the total number of fatal crashes decreased slightly from 22 to 19, the data shows a significant increase in crash severity otherwise. Crashes resulting in serious injuries increased by 54%, from 50 incidents in January 2018 to 77 in January 2019, representing a proportional rise from 1.0% to 1.4% of all crashes. The proportion of crashes with minor injuries remained stable at 6.5% for both periods.

Severity is per crash event (most severe injury). 19 fatal crash events resulted in 21 persons killed.

Outcome by Severity (Crash Events)

Fatal19fatal crashes0.3%
↓-13.6%prior 22
Serious Injury77serious injury crashes1.4%
↑54.0%prior 50
Minor Injury364minor injury crashes6.5%
↑11.3%prior 327
Possible Injury820possible injury crashes14.5%
↑3.4%prior 793
No Injury4,357no injury crashes77.3%
↑12.4%prior 3,876

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

Severity Distribution (Crash Events)

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

Top Contributing Factors

The leading contributing factor in both periods was 'Driving too fast for conditions,' which saw its count increase by 54.9% from 610 to 945 incidents year-over-year. 'Ran off road - left' also saw a significant increase in count, rising 47.2% from 396 to 583 crashes and becoming the second-leading factor in January 2019. Conversely, crashes attributed to 'Followed too close' decreased by 16.4% from 456 to 381, dropping from the third to the fifth-ranked cause.

Officer-Reported Primary Contributing Cause

Driving too fast for conditions945 (16.8%)↑54.9%prior 610
Ran off road - left583 (10.3%)↑47.2%prior 396
Animal471 (8.4%)↓-3.9%prior 490
Lost Control407 (7.2%)↑28.4%prior 317
Followed too close381 (6.8%)↓-16.4%prior 456
Other (explain in narrative): Other300 (5.3%)↓-12.8%prior 344
Ran off road - straight270 (4.8%)↑22.7%prior 220
FTYROW: From stop sign260 (4.6%)↑7.9%prior 241
FTYROW: Making left turn173 (3.1%)↓-19.9%prior 216
Ran Traffic Signal166 (2.9%)↓-11.7%prior 188

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

Road & Environmental Conditions

There was a substantial shift in the reported conditions under which crashes occurred, pointing to more severe winter weather in January 2019. Crashes occurring in snow conditions more than tripled, increasing from 326 to 1,029 incidents year-over-year. Correspondingly, collisions on snowy road surfaces rose from 904 to 1,678, and crashes on icy or frosty roads increased from 805 to 986.

Weather

Clear2,235 (42.8%)
↓-23.2%prior 2,910
Cloudy1,290 (24.7%)
↑31.1%prior 984
Snow1,029 (19.7%)
↑215.6%prior 326
Blowing Snow294 (5.6%)
↑169.7%prior 109
Freezing rain/drizzle248 (4.7%)
↑57.0%prior 158
Severe Winds44 (0.8%)
↑340.0%prior 10
Rain38 (0.7%)
↓-42.4%prior 66
Fog, smoke, smog23 (0.4%)
↓-59.6%prior 57
Other (explain in narrative)14 (0.3%)
↓-30.0%prior 20
Sleet, hail12 (0.2%)

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

Lighting

Daylight3,195 (61.0%)
↑19.9%prior 2,665
Dark - roadway lighted1,030 (19.6%)
↓-4.1%prior 1,074
Dark - roadway not lighted704 (13.4%)
↑17.1%prior 601
Dusk164 (3.1%)
↑9.3%prior 150
Dawn122 (2.3%)
↓-10.9%prior 137
Dark - unknown roadway lighting27 (0.5%)
↑28.6%prior 21

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

Road Surface

Dry1,808 (34.5%)
↓-5.4%prior 1,911
Snow1,678 (32.1%)
↑85.6%prior 904
Ice/frost986 (18.8%)
↑22.5%prior 805
Wet528 (10.1%)
↓-31.9%prior 775
Slush190 (3.6%)
↑1.1%prior 188
Gravel30 (0.6%)
↓-18.9%prior 37
Other (explain in narrative)7 (0.1%)
↓-53.3%prior 15
Mud, dirt4 (0.1%)
↓-33.3%prior 6
Sand3 (0.1%)

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

Vehicles & Demographics

The composition of vehicles involved in crashes remained largely consistent year-over-year. Ford and Chevrolet were the top two vehicle makes in both January 2019 and January 2018, with both seeing an increase in total crash involvements. The age demographics of persons involved in crashes also showed stability, with the 26-34 age group being the largest cohort in both periods, accounting for 16.2% of persons in 2019 and 15.9% in 2018.

Top Vehicle Makes (9,552 vehicles)

1
FORD1,590 (16.6%)
↑8.8%prior 1,462
2
CHEV1,319 (13.8%)
↑6.9%prior 1,234
3
CHEVROLET601 (6.3%)
↑13.0%prior 532
4
TOYT442 (4.6%)
↓-1.3%prior 448
5
DODG402 (4.2%)
↑3.6%prior 388
6
JEEP383 (4%)
↑31.6%prior 291
7
HOND303 (3.2%)
↑2.4%prior 296
8
GMC301 (3.2%)
↑13.2%prior 266
9
NR254 (2.7%)
↑8.1%prior 235
10
CHRY232 (2.4%)
↑4.5%prior 222

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

1,625 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (8,477 persons with recorded sex)

Male5,025 (59.3%)
↑13.6%prior 4,425
Female3,452 (40.7%)
↑5.0%prior 3,287

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

Data Coverage

  • Reporting period: 2019-01-01 through 2019-01-31 (31 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 5,637
  • Total persons involved: 12,207
  • Total vehicles involved: 9,552

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