Yearly Traffic Safety Analysis

131 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Fremont County, total crashes decreased by 12.1% from 149 in 2018 to 131 in 2019. During this period, total fatalities remained constant at 3, while total injuries saw a slight decrease from 74 to 73. A notable change was the number of crashes involving a driver under the influence of alcohol or drugs, which doubled from 3 in 2018 to 6 in 2019.

131

▼ -12.1%was 149

Total Crash Events

3

Persons Killed

73

▼ -1.4%was 74

Persons Injured

3

Fatal Crash Events

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 crashes in Fremont County saw a downward trend between 2018 and 2019. The total number of crashes fell from 149 to 131, a decrease of 18 incidents. While the number of fatalities held steady at 3 for both years, the number of people injured decreased slightly from 74 to 73.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 3→0.0%

1

Pedestrians Injured

Prior: 2↓-50.0%

72

Motorists Injured

Prior: 71↑1.4%

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 timing of crashes shifted between 2018 and 2019. The peak day for collisions moved from Friday (27 crashes) in 2018 to Thursday (22 crashes) in 2019. Similarly, the peak hour shifted earlier, from 7 p.m. in the prior year (12 crashes) to the 5 p.m. hour in the current year (14 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 number of fatal crashes remained unchanged at 3 in both 2018 and 2019, though the fatal crash rate increased slightly from 2.01% to 2.29% due to the lower total number of crashes. The proportion of crashes resulting in any level of injury decreased from 36.9% of all crashes in 2018 to 35.1% in 2019. Consequently, the share of crashes with no injuries increased from 61.1% to 62.6% year-over-year.

Outcome by Severity (Crash Events)

Fatal3fatal crashes2.3%
→0.0%prior 3
Serious Injury10serious injury crashes7.6%
↓-9.1%prior 11
Minor Injury19minor injury crashes14.5%
↓-13.6%prior 22
Possible Injury17possible injury crashes13%
↓-22.7%prior 22
No Injury82no injury crashes62.6%
↓-9.9%prior 91

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 ranking of top contributing factors shifted between 2018 and 2019. 'Lost Control' became the leading factor in 2019 with 16 crashes, despite a decrease from 18 crashes in the prior year. Crashes involving an 'Animal' fell from 19 to 15, moving it from the top-ranked cause in 2018 to the second-ranked in 2019. Notably, crashes attributed to 'Driving too fast for conditions' decreased from 13 incidents to 8, while 'Ran off road - straight' held steady with 11 incidents in both years.

Officer-Reported Primary Contributing Cause

Lost Control16 (12.2%)↓-11.1%prior 18
Animal15 (11.5%)↓-21.1%prior 19
Ran off road - straight11 (8.4%)→0.0%prior 11
Followed too close10 (7.6%)↓-9.1%prior 11
Driving too fast for conditions8 (6.1%)↓-38.5%prior 13
Made improper turn8 (6.1%)
Ran off road - left7 (5.3%)↑16.7%prior 6
Ran Stop Sign7 (5.3%)↑40.0%prior 5
FTYROW: From stop sign6 (4.6%)↓-33.3%prior 9
FTYROW: Making left turn6 (4.6%)

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 in clear weather and on dry roads remained the most common scenario in both years, with their proportions staying relatively stable. In 2019, 63.4% of crashes occurred in clear weather, compared to 59.7% in 2018. Crashes on dry road surfaces accounted for 66.4% of incidents in 2019, similar to the 65.8% in 2018. The proportion of crashes happening in darkness (both lighted and unlighted roads) saw a slight decrease from 28.2% in 2018 to 26.0% in 2019.

Weather

Clear83 (68.0%)
↓-6.7%prior 89
Cloudy18 (14.8%)
↑28.6%prior 14
Freezing rain/drizzle7 (5.7%)
↑40.0%prior 5
Rain5 (4.1%)
↓-64.3%prior 14
Blowing Snow3 (2.5%)
Severe Winds2 (1.6%)
Snow2 (1.6%)
↓-84.6%prior 13
Other (explain in narrative)1 (0.8%)
Fog, smoke, smog1 (0.8%)

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

Lighting

Daylight77 (63.1%)
↓-11.5%prior 87
Dark - roadway not lighted23 (18.9%)
↓-30.3%prior 33
Dark - roadway lighted11 (9.0%)
↑22.2%prior 9
Dusk7 (5.7%)
→0.0%prior 7
Dawn4 (3.3%)

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

Road Surface

Dry87 (71.3%)
↓-11.2%prior 98
Wet15 (12.3%)
↓-11.8%prior 17
Snow10 (8.2%)
↓-28.6%prior 14
Ice/frost9 (7.4%)
↑12.5%prior 8
Water (standing or moving)1 (0.8%)

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

Vehicles & Demographics

Chevrolet and Ford vehicles were the most common makes involved in crashes in both periods. In 2019, Chevrolet-badged vehicles were involved in 43 crashes and Fords in 28, compared to 44 and 38 respectively in 2018. The age demographics of people involved in crashes shifted notably; the number of persons in the 16-20 age group increased from 28 to 45, while the 35-44 age group saw a decrease from 50 to 26. The 55-64 age group became the most represented in 2019, with 52 individuals involved, up from 37 in the previous year.

Top Vehicle Makes (203 vehicles)

1
FORD28 (13.8%)
↓-26.3%prior 38
2
CHEVROLET24 (11.8%)
↑20.0%prior 20
3
CHEV19 (9.4%)
↓-20.8%prior 24
4
JEEP10 (4.9%)
↑100.0%prior 5
5
FREIGHTLINER8 (3.9%)
↓-20.0%prior 10
6
BUICK8 (3.9%)
7
DODGE8 (3.9%)
↑14.3%prior 7
8
DODG7 (3.4%)
↓-30.0%prior 10
9
GMC6 (3%)
↓-33.3%prior 9
10
KIA5 (2.5%)

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

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

Sex Distribution (186 persons with recorded sex)

Male117 (62.9%)
↑11.4%prior 105
Female69 (37.1%)
↑25.5%prior 55

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: 131
  • Total persons involved: 299
  • Total vehicles involved: 203

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

ThatCarHitMe.com · An Injuria.ai Company