Yearly Traffic Safety Analysis

136 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Palo Alto County recorded 136 total crashes, a 16.6% decrease from the 163 crashes reported in 2017. This downward trend was accompanied by a significant reduction in traffic fatalities, which fell from 5 in 2017 to 2 in 2018. The total number of people injured, however, saw a slight increase from 50 to 54.

136

▼ -16.6%was 163

Total Crash Events

2

▼ -60.0%was 5

Persons Killed

54

▲ 8.0%was 50

Persons Injured

2

▼ -60.0%was 5

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Palo Alto County decreased by 16.6% from 2017 to 2018, with the total number of incidents falling from 163 to 136. Fatalities saw a substantial drop from 5 to 2, representing a 60% decrease. In contrast, the total number of injuries recorded a slight 8% increase from 50 to 54.

Vulnerable Road User Casualties

1

Cyclists Killed

Prior: 1→0.0%

1

Motorists Killed

Prior: 4↓-75.0%

2

Cyclists Injured

Prior: 1↑100.0%

52

Motorists Injured

Prior: 47↑10.6%

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted between 2017 and 2018. The peak day for crashes moved from Friday (32 crashes) in 2017 to Tuesday (29 crashes) in 2018. A more pronounced change occurred in the peak hour, which shifted from the 7 a.m. morning hour in 2017 (13 crashes) to the 9 p.m. evening hour in 2018 (10 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity improved year-over-year, with fatal crashes decreasing from 5 in 2017 to 2 in 2018, and their share of all incidents dropping from 3.1% to 1.5%. The number of serious injury crashes remained constant at 5 in both periods. However, minor injury crashes increased from 14 to 17, contributing to a slight rise in total persons injured from 50 to 54.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.5%
↓-60.0%prior 5
Serious Injury5serious injury crashes3.7%
→0.0%prior 5
Minor Injury17minor injury crashes12.5%
↑21.4%prior 14
Possible Injury18possible injury crashes13.2%
↓-14.3%prior 21
No Injury94no injury crashes69.1%
↓-20.3%prior 118

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor in both periods, though the count decreased from 44 crashes in 2017 to 38 in 2018. Incidents attributed to 'Lost Control' saw a notable drop, falling from 13 to 8. Meanwhile, crashes related to 'Driving too fast for conditions' increased slightly from 11 to 12. The count for 'Ran off road - straight' and 'FTYROW: At uncontrolled intersection' remained stable at 10 and 9 crashes, respectively.

Officer-Reported Primary Contributing Cause

Animal38 (27.9%)↓-13.6%prior 44
Driving too fast for conditions12 (8.8%)↑9.1%prior 11
Ran off road - straight10 (7.4%)→0.0%prior 10
FTYROW: At uncontrolled intersection9 (6.6%)→0.0%prior 9
Lost Control8 (5.9%)↓-38.5%prior 13
Made improper turn6 (4.4%)
Ran Stop Sign6 (4.4%)↓-14.3%prior 7
Other (explain in narrative): Other4 (2.9%)↓-42.9%prior 7
FTYROW: From stop sign4 (2.9%)↓-33.3%prior 6
Passing: Other passing (explain in narrative)3 (2.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in adverse road conditions increased from 2017 to 2018. Crashes on snowy roads rose from 8 to 19, and crashes on icy or frosty roads increased from 7 to 10. The share of crashes occurring during daylight hours decreased from 53.4% in 2017 to 44.9% in 2018, while crashes in dark, unlighted conditions remained proportionally stable at around 16-17%.

Weather

Clear61 (58.7%)
↓-10.3%prior 68
Cloudy25 (24.0%)
↓-40.5%prior 42
Snow11 (10.6%)
Freezing rain/drizzle2 (1.9%)
Fog, smoke, smog2 (1.9%)
Rain2 (1.9%)
Blowing Snow1 (1.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Weather condition at time of crash

Lighting

Daylight61 (57.5%)
↓-29.9%prior 87
Dark - roadway not lighted23 (21.7%)
↓-11.5%prior 26
Dark - roadway lighted11 (10.4%)
↑120.0%prior 5
Dawn5 (4.7%)
Dusk5 (4.7%)
Dark - unknown roadway lighting1 (0.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Lighting condition field

Road Surface

Dry59 (56.2%)
↓-33.0%prior 88
Snow19 (18.1%)
↑137.5%prior 8
Ice/frost10 (9.5%)
↑42.9%prior 7
Wet7 (6.7%)
↓-36.4%prior 11
Gravel7 (6.7%)
↓-30.0%prior 10
Slush2 (1.9%)
Mud, dirt1 (1.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet remained the top two vehicle makes involved in crashes, though both saw a reduction in count; Ford-involved vehicles dropped from 58 to 40. Regarding the age of persons involved in crashes, there was a notable decrease in the 16-20 age group, from 53 individuals in 2017 to 33 in 2018. Conversely, the number of people in the 55-64 and 65+ age groups involved in crashes increased from 34 to 42 and 34 to 39, respectively.

Top Vehicle Makes (189 vehicles)

1
FORD40 (21.2%)
↓-31.0%prior 58
2
CHEV27 (14.3%)
→0.0%prior 27
3
GMC11 (5.8%)
→0.0%prior 11
4
DODG8 (4.2%)
↓-27.3%prior 11
5
BUIC7 (3.7%)
↑16.7%prior 6
6
TOYT7 (3.7%)
↓-22.2%prior 9
7
DODGE7 (3.7%)
→0.0%prior 7
8
JEEP6 (3.2%)
→0.0%prior 6
9
FREIGHTLINER6 (3.2%)
10
CHEVROLET6 (3.2%)
↓-70.0%prior 20

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records

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

Sex Distribution (139 persons with recorded sex)

Male89 (64.0%)
↓-11.9%prior 101
Female50 (36.0%)
↓-18.0%prior 61

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 136
  • Total persons involved: 235
  • Total vehicles involved: 189

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: 2018." Published September 9, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2018-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