Yearly Traffic Safety Analysis

132 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Lucas County recorded 132 total crashes, a 12.8% increase from the 117 crashes reported in 2018. The most significant year-over-year change was the occurrence of one fatal crash resulting in one death in 2019, whereas there were no fatalities in the prior year. The total number of injuries remained unchanged at 33 for both periods.

132

▲ 12.8%was 117

Total Crash Events

1

Persons Killed

33

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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in Lucas County shows an increase in traffic crashes year-over-year. Total collisions rose by 12.8%, from 117 in 2018 to 132 in 2019. While the total number of injuries remained constant at 33, the county experienced one fatality in 2019 after having zero in the previous year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 0→%

1

Pedestrians Injured

Prior: 0→%

32

Motorists Injured

Prior: 33↓-3.0%

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 showed some shifts between 2018 and 2019. While Tuesday was a peak day for crashes in both years (22 in 2018, 23 in 2019), Thursday also emerged as a peak day in 2019 with 23 incidents. The peak hour for collisions shifted an hour earlier, from a tie at 5 p.m. and 7 p.m. in 2018 (13 crashes each) to 6 p.m. in 2019 (13 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

Crash severity saw a notable shift in 2019 with the recording of one fatal crash, which accounted for 0.8% of all incidents, compared to zero fatal crashes in 2018. The proportion of non-injury crashes increased from 74.4% in 2018 to 78.8% in 2019. Concurrently, crashes classified with 'Possible Injury' decreased from a 10.3% share to 5.3%, and the two 'Serious Injury' crashes from 2018 were not repeated in 2019.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.8%
Minor Injury20minor injury crashes15.2%
↑25.0%prior 16
Possible Injury7possible injury crashes5.3%
↓-41.7%prior 12
No Injury104no injury crashes78.8%
↑19.5%prior 87

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

Collisions involving an 'Animal' remained the leading contributing factor in both periods, with the count of such incidents increasing by 34.8% from 46 crashes in 2018 to 62 in 2019. This factor's share of total crashes also grew from 39.3% to 47.0%. In contrast, crashes attributed to 'Driving too fast for conditions' saw a significant drop, falling from 11 incidents in 2018 to 5 in 2019.

Officer-Reported Primary Contributing Cause

Animal62 (47%)↑34.8%prior 46
Other (explain in narrative): Other15 (11.4%)↑50.0%prior 10
Lost Control8 (6.1%)↑14.3%prior 7
Driving too fast for conditions5 (3.8%)↓-54.5%prior 11
FTYROW: From stop sign3 (2.3%)
Ran off road - straight3 (2.3%)
FTYROW: From yield sign3 (2.3%)
FTYROW: From driveway3 (2.3%)
Exceeded authorized speed3 (2.3%)
Driver Distraction: Other interior distraction3 (2.3%)↓-50.0%prior 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

Crash conditions remained relatively stable year-over-year, with the majority of incidents in both periods occurring in 'Clear' weather and on 'Dry' roads. Crashes in 'Clear' weather increased from 49 to 54, while those under 'Cloudy' skies decreased from 19 to 10. Incidents in 'Dark - roadway not lighted' conditions also saw a reduction, from 22 in 2018 to 16 in 2019.

Weather

Clear54 (69.2%)
↑10.2%prior 49
Cloudy10 (12.8%)
↓-47.4%prior 19
Rain5 (6.4%)
→0.0%prior 5
Snow3 (3.8%)
↓-50.0%prior 6
Fog, smoke, smog2 (2.6%)
Freezing rain/drizzle2 (2.6%)
Severe Winds1 (1.3%)
Other (explain in narrative)1 (1.3%)

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

Lighting

Daylight51 (65.4%)
↑4.1%prior 49
Dark - roadway not lighted16 (20.5%)
↓-27.3%prior 22
Dark - roadway lighted6 (7.7%)
Dawn3 (3.8%)
Dark - unknown roadway lighting2 (2.6%)

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

Road Surface

Dry58 (72.5%)
↑3.6%prior 56
Snow7 (8.8%)
↑16.7%prior 6
Wet6 (7.5%)
→0.0%prior 6
Ice/frost4 (5.0%)
↓-55.6%prior 9
Gravel2 (2.5%)
↓-60.0%prior 5
Sand1 (1.3%)
Mud, dirt1 (1.3%)
Slush1 (1.3%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford (38 in 2018, 39 in 2019) and Chevrolet (31 vs. 34, combining 'CHEV' and 'CHEVROLET') being the most common. The demographic profile of individuals involved in crashes shifted, with the 45-54 age group becoming the most represented in 2019, increasing from 34 to 51 persons. Conversely, the 26-34 age group, which was the largest in 2018 with 36 individuals, saw its count decrease to 29 in 2019.

Top Vehicle Makes (178 vehicles)

1
FORD39 (21.9%)
↑2.6%prior 38
2
CHEV26 (14.6%)
→0.0%prior 26
3
DODG10 (5.6%)
↓-28.6%prior 14
4
DODGE8 (4.5%)
↑14.3%prior 7
5
CHEVROLET8 (4.5%)
↑60.0%prior 5
6
GMC7 (3.9%)
7
TOYT6 (3.4%)
→0.0%prior 6
8
VOLVO5 (2.8%)
9
INTERNATIONA4 (2.2%)
10
BUIC4 (2.2%)
↓-33.3%prior 6

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

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

Sex Distribution (162 persons with recorded sex)

Male107 (66.0%)
↑55.1%prior 69
Female55 (34.0%)
↓-19.1%prior 68

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: 132
  • Total persons involved: 271
  • Total vehicles involved: 178

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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