Yearly Traffic Safety Analysis

2,229 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Pottawattamie County recorded 2,229 total vehicle crashes, a 3.0% increase from the 2,164 crashes reported in 2018. While overall crashes and injuries saw modest changes, the number of fatalities increased by 30%, from 10 in 2018 to 13 in 2019.

2,229

▲ 3.0%was 2,164

Total Crash Events

13

▲ 30.0%was 10

Persons Killed

710

▼ -6.0%was 755

Persons Injured

12

▲ 20.0%was 10

Fatal Crash Events

Note: "Persons Killed" (13) counts individual fatalities across all crash events. "Fatal" in the severity table below (12) 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

Crash trends in Pottawattamie County showed a slight increase year-over-year, with total collisions rising by 3.0% from 2,164 in 2018 to 2,229 in 2019. Despite the rise in total crashes, the number of persons injured decreased by 6.0% to 710. However, fatalities increased from 10 in 2018 to 13 in 2019.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Cyclists Killed

Prior: 0→%

12

Motorists Killed

Prior: 10↑20.0%

0

Other Killed

Prior: 0→0.0%

16

Pedestrians Injured

Prior: 16→0.0%

22

Cyclists Injured

Prior: 18↑22.2%

670

Motorists Injured

Prior: 720↓-6.9%

2

Other Injured

Prior: 1↑100.0%

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 remained largely consistent year-over-year. Friday was the day with the most crashes in both 2019 (405 crashes) and 2018 (392 crashes). The peak hour for collisions shifted slightly earlier, from 4 p.m. in 2018 (180 crashes) to 3 p.m. in 2019 (165 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 shifted slightly year-over-year, with the fatal crash rate increasing from 0.46% in 2018 to 0.54% in 2019. The number of fatal crashes rose from 10 to 12. The proportion of crashes resulting in possible injuries decreased from 20.4% of all incidents in 2018 to 18.1% in 2019. Conversely, the share of no-injury crashes increased from 68.7% to 70.9% over the same period.

Severity is per crash event (most severe injury). 12 fatal crash events resulted in 13 persons killed.

Outcome by Severity (Crash Events)

Fatal12fatal crashes0.5%
↑20.0%prior 10
Serious Injury46serious injury crashes2.1%
↑4.5%prior 44
Minor Injury188minor injury crashes8.4%
↑3.9%prior 181
Possible Injury403possible injury crashes18.1%
↓-8.8%prior 442
No Injury1,580no injury crashes70.9%
↑6.3%prior 1,487

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 for crashes remained consistent between periods, with 'Followed too close' ranking as the top factor in both 2019 (335 crashes) and 2018 (308 crashes). The count of crashes attributed to this factor increased by 8.8%. Crashes involving an animal, the second-ranked factor, increased in count by 19.6% from 163 to 195. Incidents where a vehicle 'Ran off road - left' also saw a significant 23.7% increase in count, rising from 135 to 167.

Officer-Reported Primary Contributing Cause

Followed too close335 (15%)↑8.8%prior 308
Animal195 (8.7%)↑19.6%prior 163
Ran off road - left167 (7.5%)↑23.7%prior 135
Lost Control165 (7.4%)↑1.2%prior 163
Other (explain in narrative): Other118 (5.3%)↓-7.1%prior 127
Ran off road - straight86 (3.9%)↓-16.5%prior 103
Driving too fast for conditions83 (3.7%)↓-16.2%prior 99
FTYROW: From stop sign83 (3.7%)↓-19.4%prior 103
Made improper turn82 (3.7%)↓-1.2%prior 83
FTYROW: Making left turn80 (3.6%)↑14.3%prior 70

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

Road & Environmental Conditions

The conditions under which crashes occurred shifted toward more favorable environments in 2019 compared to 2018. The proportion of crashes on dry road surfaces increased from 63.9% to 67.8%, while crashes on wet roads decreased from 16.3% to 11.9%. Similarly, a larger share of incidents happened in clear weather (63.6% in 2019 vs. 59.2% in 2018). The distribution of crashes by lighting conditions remained relatively stable, with most incidents in both years occurring during daylight.

Weather

Clear1,417 (68.7%)
↑10.6%prior 1,281
Cloudy342 (16.6%)
↓-6.6%prior 366
Snow118 (5.7%)
↓-4.8%prior 124
Rain99 (4.8%)
↓-36.1%prior 155
Freezing rain/drizzle51 (2.5%)
↑8.5%prior 47
Blowing Snow17 (0.8%)
↓-5.6%prior 18
Fog, smoke, smog11 (0.5%)
↑83.3%prior 6
Other (explain in narrative)3 (0.1%)
Severe Winds2 (0.1%)
↓-80.0%prior 10
Sleet, hail2 (0.1%)

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

Lighting

Daylight1,407 (68.0%)
↑2.9%prior 1,368
Dark - roadway lighted379 (18.3%)
↑9.5%prior 346
Dark - roadway not lighted189 (9.1%)
↓-12.9%prior 217
Dusk56 (2.7%)
↑40.0%prior 40
Dawn32 (1.5%)
↓-23.8%prior 42
Dark - unknown roadway lighting6 (0.3%)
↓-33.3%prior 9

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

Road Surface

Dry1,512 (73.3%)
↑9.4%prior 1,382
Wet266 (12.9%)
↓-24.6%prior 353
Snow124 (6.0%)
↑5.1%prior 118
Ice/frost116 (5.6%)
↑8.4%prior 107
Slush20 (1.0%)
↓-23.1%prior 26
Gravel15 (0.7%)
↓-16.7%prior 18
Other (explain in narrative)5 (0.2%)
Sand2 (0.1%)
Oil1 (0.0%)
Mud, dirt1 (0.0%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Chevrolet and Ford vehicles being the most common in both 2019 and 2018. An analysis of persons involved in crashes shows an increase in representation from the 35-44 and 55-64 age demographics. The 35-44 age group's share of involved persons with a known age rose from 16.5% in 2018 to 18.1% in 2019, while the 55-64 age group's share increased from 13.0% to 14.5%.

Top Vehicle Makes (3,867 vehicles)

1
FORD586 (15.2%)
↑4.6%prior 560
2
CHEV377 (9.7%)
↓-5.8%prior 400
3
CHEVROLET303 (7.8%)
↑13.9%prior 266
4
NR216 (5.6%)
↑45.0%prior 149
5
JEEP137 (3.5%)
↑1.5%prior 135
6
DODG127 (3.3%)
↓-10.6%prior 142
7
DODGE125 (3.2%)
↓-7.4%prior 135
8
HOND116 (3%)
↑45.0%prior 80
9
TOYOTA115 (3%)
↑15.0%prior 100
10
KIA115 (3%)
↓-16.1%prior 137

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

1,047 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (3,219 persons with recorded sex)

Male1,866 (58.0%)
↑16.6%prior 1,600
Female1,353 (42.0%)
↑14.4%prior 1,183

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: 2,229
  • Total persons involved: 5,233
  • Total vehicles involved: 3,867

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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