Yearly Traffic Safety Analysis

132 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Lucas County recorded 132 total crashes, a 7.3% increase from the 123 crashes documented in 2016. While overall crashes rose, the number of reported injuries decreased from 51 to 47. The most significant year-over-year change was the doubling of fatalities, which increased from one in 2016 to two in 2017.

132

▲ 7.3%was 123

Total Crash Events

2

▲ 100.0%was 1

Persons Killed

47

▼ -7.8%was 51

Persons Injured

2

▲ 100.0%was 1

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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends in Lucas County show an increase year-over-year. Total crashes rose from 123 in 2016 to 132 in 2017, representing a 7.3% increase. While the total number of injuries decreased from 51 to 47, the number of fatalities increased from one to two during the same period.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

1

Pedestrians Injured

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

45

Motorists Injured

Prior: 46↓-2.2%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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 the two periods. In 2017, the peak day for crashes was Thursday with 23 incidents, which is consistent with the prior year when Thursday and Friday were the peak days with 24 incidents each. However, the peak hour for crashes shifted from 8 p.m. in 2016 (13 crashes) to the 5 p.m. hour in 2017 (16 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity worsened year-over-year, with fatal crashes doubling from one in 2016 to two in 2017, and the corresponding fatal crash rate increasing from 0.81% to 1.52%. While the number of crashes involving serious injuries decreased from 9 to 5, and minor injury crashes fell from 18 to 13, the count of crashes with possible injuries rose significantly from 12 in 2016 to 23 in 2017.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.5%
↑100.0%prior 1
Serious Injury5serious injury crashes3.8%
↓-44.4%prior 9
Minor Injury13minor injury crashes9.8%
↓-27.8%prior 18
Possible Injury23possible injury crashes17.4%
↑91.7%prior 12
No Injury89no injury crashes67.4%
↑7.2%prior 83

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor in both years, although the count of such incidents decreased from 59 in 2016 to 54 in 2017. The second-ranked factor, 'Lost Control,' saw its crash count increase from 9 in 2016 to 14 in 2017. In contrast, crashes where 'Followed too close' was a factor decreased from 7 incidents in the prior year to 3 in the current year.

Officer-Reported Primary Contributing Cause

Animal54 (40.9%)↓-8.5%prior 59
Lost Control14 (10.6%)↑55.6%prior 9
Other (explain in narrative): Other10 (7.6%)↑66.7%prior 6
Swerving/Evasive Action5 (3.8%)
Driving too fast for conditions4 (3%)↓-33.3%prior 6
Driver Distraction: Other interior distraction4 (3%)
Ran off road - straight4 (3%)↓-20.0%prior 5
Driver Distraction: Exterior distraction3 (2.3%)
Followed too close3 (2.3%)↓-57.1%prior 7
Ran off road - left3 (2.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather and daylight conditions increased year-over-year. In 2017, 70 crashes occurred in clear weather, up from 50 in 2016, and 54 crashes happened during daylight, an increase from 40 in the prior year. Incidents on wet roads also rose from 7 to 13. Conversely, crashes on icy or frosty road surfaces decreased from 7 in 2016 to 5 in 2017.

Weather

Clear70 (78.7%)
↑40.0%prior 50
Cloudy9 (10.1%)
↓-43.8%prior 16
Rain6 (6.7%)
Severe Winds1 (1.1%)
Fog, smoke, smog1 (1.1%)
Freezing rain/drizzle1 (1.1%)
↓-80.0%prior 5
Blowing Snow1 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight54 (60.7%)
↑35.0%prior 40
Dark - roadway not lighted28 (31.5%)
↓-3.4%prior 29
Dark - roadway lighted6 (6.7%)
Dusk1 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry62 (69.7%)
↑17.0%prior 53
Wet13 (14.6%)
↑85.7%prior 7
Gravel6 (6.7%)
↓-14.3%prior 7
Ice/frost5 (5.6%)
↓-28.6%prior 7
Mud, dirt1 (1.1%)
Slush1 (1.1%)
Snow1 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes showed some changes between periods. The number of Ford vehicles involved remained stable with 33 in both years, while the count of Chevrolet-branded vehicles (CHEV and CHEVROLET) decreased from a combined 47 in 2016 to 34 in 2017. Regarding persons involved, there was a notable increase in the 55-64 age group, from 19 individuals in 2016 to 31 in 2017, while the 45-54 age group saw a decrease from 35 to 27.

Top Vehicle Makes (170 vehicles)

1
FORD33 (19.4%)
→0.0%prior 33
2
CHEVROLET17 (10%)
↓-51.4%prior 35
3
CHEV17 (10%)
↑41.7%prior 12
4
TOYOTA9 (5.3%)
5
GMC8 (4.7%)
→0.0%prior 8
6
PONTIAC7 (4.1%)
7
DODGE6 (3.5%)
↓-50.0%prior 12
8
JEEP6 (3.5%)
9
DODG6 (3.5%)
→0.0%prior 6
10
PETERBILT5 (2.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (117 persons with recorded sex)

Male73 (62.4%)
→0.0%prior 73
Female44 (37.6%)
↓-4.3%prior 46

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 132
  • Total persons involved: 207
  • Total vehicles involved: 170

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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