Yearly Traffic Safety Analysis

1,663 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Story County, traffic crashes increased from 1,582 in 2018 to 1,663 in 2019, a 5.1% rise. Total injuries also saw a slight increase from 424 to 464. The most significant year-over-year change was a sharp increase in traffic fatalities, which rose from 2 in the prior period to 9 in the current period, accompanied by a rise in fatal crashes from 2 to 9.

1,663

▲ 5.1%was 1,582

Total Crash Events

9

▲ 350.0%was 2

Persons Killed

464

▲ 9.4%was 424

Persons Injured

9

▲ 350.0%was 2

Fatal Crash Events

Note: "Persons Killed" (9) counts individual fatalities across all crash events. "Fatal" in the severity table below (9) 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, traffic collisions in Story County showed an upward trend year-over-year. Total crashes increased by 5.1%, from 1,582 in 2018 to 1,663 in 2019. This increase was mirrored in the number of people injured, which grew by 9.4% from 424 to 464, and a notable rise in fatalities from 2 to 9.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

9

Motorists Killed

Prior: 2↑350.0%

0

Other Killed

Prior: 0→0.0%

14

Pedestrians Injured

Prior: 15↓-6.7%

14

Cyclists Injured

Prior: 16↓-12.5%

435

Motorists Injured

Prior: 391↑11.3%

1

Other Injured

Prior: 2↓-50.0%

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 temporal patterns of crashes showed some shifts between the two periods. The peak day for crashes moved from Tuesday (255 crashes) in 2018 to Friday (265 crashes) in 2019. However, the most frequent time for crashes remained consistent, with the 5 p.m. hour being the peak in both 2018 (165 crashes) and 2019 (160 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 severity of crashes worsened significantly year-over-year. The number of fatal crashes increased from 2 to 9, and the corresponding fatal crash rate more than tripled, rising from 0.13 per 100 crashes in 2018 to 0.54 in 2019. The count of crashes resulting in serious, minor, or possible injuries also increased, rising from a combined 337 in 2018 to 363 in 2019.

Outcome by Severity (Crash Events)

Fatal9fatal crashes0.5%
↑350.0%prior 2
Serious Injury22serious injury crashes1.3%
↑15.8%prior 19
Minor Injury129minor injury crashes7.8%
↑6.6%prior 121
Possible Injury212possible injury crashes12.7%
↑7.6%prior 197
No Injury1,291no injury crashes77.6%
↑3.9%prior 1,243

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

The leading contributing factors remained broadly consistent, though some notable shifts occurred. "Followed too close" was the top factor in both periods, with a slight decrease in count from 246 crashes in 2018 to 241 in 2019. The most significant change was in crashes attributed to "Driving too fast for conditions," which saw a 27.3% increase in count, rising from 172 incidents in 2018 to 219 in 2019, making it the second-leading factor.

Officer-Reported Primary Contributing Cause

Followed too close241 (14.5%)↓-2.0%prior 246
Driving too fast for conditions219 (13.2%)↑27.3%prior 172
Animal168 (10.1%)↑3.1%prior 163
FTYROW: Making left turn109 (6.6%)↓-3.5%prior 113
Other (explain in narrative): Other102 (6.1%)↑27.5%prior 80
Ran off road - left84 (5.1%)↑52.7%prior 55
FTYROW: From stop sign83 (5%)→0.0%prior 83
Improper or erratic lane changing53 (3.2%)↓-13.1%prior 61
Ran off road - straight47 (2.8%)↓-20.3%prior 59
Other (explain in narrative): No improper action44 (2.6%)↑2.3%prior 43

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

Road & Environmental Conditions

While clear weather and dry roads remained the most common conditions for crashes in both years, there was a notable increase in crashes occurring in adverse winter conditions. Crashes on roads with snow increased from 145 in 2018 to 198 in 2019, and incidents on icy or frosty roads rose from 91 to 159. Similarly, crashes during snowfall increased from 85 to 115 year-over-year.

Weather

Clear921 (60.5%)
↑3.1%prior 893
Cloudy306 (20.1%)
↑7.7%prior 284
Snow115 (7.6%)
↑35.3%prior 85
Rain103 (6.8%)
↑7.3%prior 96
Blowing Snow27 (1.8%)
↑80.0%prior 15
Freezing rain/drizzle27 (1.8%)
↓-37.2%prior 43
Severe Winds8 (0.5%)
Sleet, hail6 (0.4%)
↓-14.3%prior 7
Fog, smoke, smog5 (0.3%)
Other (explain in narrative)4 (0.3%)

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

Lighting

Daylight1,087 (71.3%)
↑3.2%prior 1,053
Dark - roadway lighted235 (15.4%)
↑19.3%prior 197
Dark - roadway not lighted139 (9.1%)
↑21.9%prior 114
Dawn32 (2.1%)
↓-15.8%prior 38
Dusk27 (1.8%)
↑17.4%prior 23
Dark - unknown roadway lighting4 (0.3%)
↓-33.3%prior 6

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

Road Surface

Dry937 (61.3%)
↓-1.3%prior 949
Snow198 (13.0%)
↑36.6%prior 145
Wet196 (12.8%)
↑2.6%prior 191
Ice/frost159 (10.4%)
↑74.7%prior 91
Slush26 (1.7%)
↓-25.7%prior 35
Gravel9 (0.6%)
↓-47.1%prior 17
Other (explain in narrative)3 (0.2%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, including Ford, Chevrolet, and Toyota, remained consistent across both periods with only minor fluctuations in their total counts. An analysis of person demographics shows that the 16-20 and 21-25 age groups were the most frequently involved in both years. The number of individuals from the 21-25 age group involved in crashes increased from 623 in 2018 to 697 in 2019.

Top Vehicle Makes (3,019 vehicles)

1
FORD478 (15.8%)
↑8.1%prior 442
2
CHEV362 (12%)
↓-0.5%prior 364
3
TOYT210 (7%)
↓-20.8%prior 265
4
CHEVROLET174 (5.8%)
↑5.5%prior 165
5
HOND134 (4.4%)
↓-12.4%prior 153
6
TOYOTA123 (4.1%)
↑46.4%prior 84
7
JEEP118 (3.9%)
↑11.3%prior 106
8
DODG107 (3.5%)
↑4.9%prior 102
9
NISS98 (3.2%)
↑12.6%prior 87
10
HONDA85 (2.8%)
↑19.7%prior 71

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

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

Sex Distribution (2,757 persons with recorded sex)

Male1,541 (55.9%)
↑14.1%prior 1,351
Female1,216 (44.1%)
↑15.3%prior 1,055

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: 1,663
  • Total persons involved: 3,844
  • Total vehicles involved: 3,019

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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