Yearly Traffic Safety Analysis

123 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Lucas County, total traffic crashes increased by 3.4%, from 119 in 2015 to 123 in 2016. While total injuries decreased from 56 to 51, the most significant year-over-year change was the recording of one fatal crash in 2016, compared to zero in the prior year. Collisions involving animals remained the primary contributing factor in both periods, accounting for 59 of the 123 crashes in 2016.

123

▲ 3.4%was 119

Total Crash Events

1

Persons Killed

51

▼ -8.9%was 56

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Lucas County saw a slight increase in 2016, rising to 123 from 119 in the previous year. Despite this 3.4% increase in crash volume, the number of people injured fell by 8.9% from 56 to 51. However, the county recorded one fatality in 2016, whereas there were none in 2015.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 0→%

0

Other Killed

Prior: 0→0.0%

46

Motorists Injured

Prior: 56↓-17.9%

5

Other Injured

Prior: 0→%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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 the two periods. In 2016, the peak days for crashes were Thursday and Friday, with 24 incidents each, a change from 2015 when Tuesday and Thursday were the peak days with 20 crashes each. The peak hour also moved later into the evening, from 5 p.m. in 2015 (15 crashes) to 8 p.m. in 2016 (13 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity worsened in 2016 with the occurrence of one fatal crash, representing 0.8% of all incidents, compared to zero fatal crashes in 2015. The number of serious injury crashes also increased from 6 to 9. Conversely, crashes resulting in possible injuries saw a significant drop from 24 in 2015 to 12 in 2016, and no-injury crashes increased from 71 to 83.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.8%
Serious Injury9serious injury crashes7.3%
↑50.0%prior 6
Minor Injury18minor injury crashes14.6%
→0.0%prior 18
Possible Injury12possible injury crashes9.8%
↓-50.0%prior 24
No Injury83no injury crashes67.5%
↑16.9%prior 71

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the top contributing factor in both years, increasing in count from 55 in 2015 to 59 in 2016. The most notable change was in crashes attributed to 'Followed too close,' which increased from 2 incidents in 2015 to 7 in 2016. Meanwhile, crashes due to 'Driving too fast for conditions' decreased from 8 to 6 over the same period.

Officer-Reported Primary Contributing Cause

Animal59 (48%)↑7.3%prior 55
Lost Control9 (7.3%)↑12.5%prior 8
Followed too close7 (5.7%)
Driving too fast for conditions6 (4.9%)↓-25.0%prior 8
FTYROW: From stop sign6 (4.9%)→0.0%prior 6
Other (explain in narrative): Other6 (4.9%)
Ran off road - straight5 (4.1%)
Driver Distraction: Other interior distraction3 (2.4%)
Ran off road - left2 (1.6%)
FTYROW: From yield sign2 (1.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in adverse conditions increased in 2016 compared to the prior year. While crashes on dry roads decreased from 72 to 53, the number of incidents on wet, icy, or snow-covered surfaces remained relatively stable (19 in 2016 vs. 21 in 2015), representing a larger share of total crashes. Similarly, crashes in clear weather decreased from 76 to 50, while the count of crashes in rain, snow, or freezing rain was identical at 12 for both years, indicating a proportional increase in adverse weather-related incidents.

Weather

Clear50 (63.3%)
↓-34.2%prior 76
Cloudy16 (20.3%)
↑23.1%prior 13
Freezing rain/drizzle5 (6.3%)
Rain4 (5.1%)
↓-42.9%prior 7
Snow3 (3.8%)
Other (explain in narrative)1 (1.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight40 (51.9%)
↓-23.1%prior 52
Dark - roadway not lighted29 (37.7%)
↓-25.6%prior 39
Dark - roadway lighted4 (5.2%)
↓-20.0%prior 5
Dark - unknown roadway lighting3 (3.9%)
Dusk1 (1.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry53 (67.1%)
↓-26.4%prior 72
Ice/frost7 (8.9%)
↑40.0%prior 5
Gravel7 (8.9%)
↓-30.0%prior 10
Wet7 (8.9%)
↓-46.2%prior 13
Snow3 (3.8%)
Mud, dirt1 (1.3%)
Slush1 (1.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

An analysis of persons involved in crashes shows a shift in age demographics. The number of people in the 16-20 age group increased from 27 to 31, and those in the 65+ group rose from 19 to 22, while involvement from the 45-54 age group decreased from 41 to 35. Regarding vehicle makes, Chevrolet and Ford vehicles were the most frequently involved in crashes in both years, with both makes seeing an increase in crash counts from 2015 to 2016.

Top Vehicle Makes (167 vehicles)

1
CHEVROLET35 (21%)
↑118.8%prior 16
2
FORD33 (19.8%)
↑32.0%prior 25
3
DODGE12 (7.2%)
↑140.0%prior 5
4
CHEV12 (7.2%)
↓-45.5%prior 22
5
GMC8 (4.8%)
6
DODG6 (3.6%)
↓-25.0%prior 8
7
CHRYSLER6 (3.6%)
8
BUIC5 (3%)
9
HONDA5 (3%)
10
JEEP4 (2.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (119 persons with recorded sex)

Male73 (61.3%)
↓-25.5%prior 98
Female46 (38.7%)
↓-19.3%prior 57

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 123
  • Total persons involved: 199
  • Total vehicles involved: 167

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: 2016." Published September 9, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-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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