Yearly Traffic Safety Analysis

607 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Lee County, total vehicle crashes decreased by 3.8% from 631 in 2017 to 607 in 2018. While the number of fatalities remained unchanged at four, the total number of injuries saw a significant year-over-year reduction of 30.2%, falling from 202 to 141. The most notable change was a nearly 50% drop in crashes involving driving under the influence (DUI), which fell from 31 incidents in 2017 to 16 in 2018.

607

▼ -3.8%was 631

Total Crash Events

4

Persons Killed

141

▼ -30.2%was 202

Persons Injured

3

▼ -25.0%was 4

Fatal Crash Events

Note: "Persons Killed" (4) 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 · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Lee County showed a downward trend from 2017 to 2018. The total number of crashes fell by 3.8% from 631 to 607. This decrease was accompanied by a substantial 30.2% drop in injuries, from 202 to 141, while fatalities held steady at four for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 3↑33.3%

3

Pedestrians Injured

Prior: 2↑50.0%

5

Cyclists Injured

Prior: 4↑25.0%

133

Motorists Injured

Prior: 195↓-31.8%

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 timing of crashes shifted between the two periods. In 2018, the peak day for crashes was Thursday with 96 incidents, a change from Friday (111 incidents) in the prior year. A more significant shift occurred in the peak hour, which moved from the morning commute at 6 a.m. in 2017 (46 crashes) to the evening at 8 p.m. in 2018 (45 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

The severity of crashes lessened from 2017 to 2018. The fatal crash rate decreased from 0.63% to 0.49% of all crashes. The proportion of crashes resulting in any injury also declined, from 24.6% in 2017 to 18.8% in 2018. Specifically, serious injury crashes fell from 24 incidents (3.8% of total) to 13 incidents (2.1% of total).

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.5%
↓-25.0%prior 4
Serious Injury13serious injury crashes2.1%
↓-45.8%prior 24
Minor Injury43minor injury crashes7.1%
↓-28.3%prior 60
Possible Injury58possible injury crashes9.6%
↓-18.3%prior 71
No Injury490no injury crashes80.7%
↑3.8%prior 472

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

The leading contributing factor in both years was collisions with an animal, with the count of such incidents increasing by 8.4% from 249 in 2017 to 270 in 2018. Other top factors included 'Lost Control', which saw a slight decrease in count from 38 to 36, and 'Failure to Yield Right of Way from a stop sign', which increased from 32 to 35 incidents. The number of crashes attributed to 'Followed too close' decreased from 31 to 28.

Officer-Reported Primary Contributing Cause

Animal270 (44.5%)↑8.4%prior 249
Lost Control36 (5.9%)↓-5.3%prior 38
FTYROW: From stop sign35 (5.8%)↑9.4%prior 32
Followed too close28 (4.6%)↓-9.7%prior 31
Other (explain in narrative): Other25 (4.1%)↓-34.2%prior 38
Ran off road - straight21 (3.5%)↓-34.4%prior 32
Ran off road - left20 (3.3%)↓-25.9%prior 27
Driving too fast for conditions17 (2.8%)→0.0%prior 17
FTYROW: Making left turn13 (2.1%)↑85.7%prior 7
Ran Stop Sign12 (2%)↓-42.9%prior 21

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

Road & Environmental Conditions

While the distribution of crashes across lighting and weather conditions remained relatively stable year-over-year, there was a notable shift in road surface conditions. In 2018, 72.7% of crashes with a recorded road condition occurred on dry surfaces, down from 81.3% in 2017. Correspondingly, the share of crashes on non-dry surfaces like wet, icy, or snow-covered roads increased from 18.7% to 27.3%.

Weather

Clear230 (66.3%)
↓-10.2%prior 256
Cloudy62 (17.9%)
↓-31.1%prior 90
Rain24 (6.9%)
→0.0%prior 24
Snow12 (3.5%)
↑33.3%prior 9
Freezing rain/drizzle9 (2.6%)
Fog, smoke, smog4 (1.2%)
Blowing Snow3 (0.9%)
Severe Winds2 (0.6%)
Other (explain in narrative)1 (0.3%)

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

Lighting

Daylight231 (66.0%)
↓-3.3%prior 239
Dark - roadway not lighted60 (17.1%)
↓-18.9%prior 74
Dark - roadway lighted33 (9.4%)
↓-13.2%prior 38
Dawn12 (3.4%)
↑100.0%prior 6
Dark - unknown roadway lighting8 (2.3%)
↓-63.6%prior 22
Dusk6 (1.7%)
↓-50.0%prior 12

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

Road Surface

Dry253 (72.7%)
↓-20.4%prior 318
Wet49 (14.1%)
↑19.5%prior 41
Ice/frost21 (6.0%)
↑110.0%prior 10
Snow16 (4.6%)
↑45.5%prior 11
Gravel5 (1.4%)
↓-16.7%prior 6
Slush3 (0.9%)
Other (explain in narrative)1 (0.3%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Chevrolet (172 vehicles) and Ford (171 vehicles) leading in 2018, similar to 2017 (177 and 182 vehicles, respectively). Analysis of persons involved shows a shift in age distribution; the 16-20 age group's involvement decreased from 131 to 101 individuals. Conversely, the number of individuals in the 35-44 and 55-64 age groups involved in crashes increased from 129 to 168 and 135 to 168, respectively.

Top Vehicle Makes (861 vehicles)

1
FORD171 (19.9%)
↓-6.0%prior 182
2
CHEVROLET87 (10.1%)
↓-17.1%prior 105
3
CHEV85 (9.9%)
↑18.1%prior 72
4
DODGE48 (5.6%)
↑29.7%prior 37
5
GMC35 (4.1%)
↓-10.3%prior 39
6
DODG34 (3.9%)
↑13.3%prior 30
7
TOYOTA33 (3.8%)
↓-5.7%prior 35
8
CHRYSLER32 (3.7%)
↑23.1%prior 26
9
KIA27 (3.1%)
↑12.5%prior 24
10
JEEP26 (3%)
↓-18.8%prior 32

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

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

Sex Distribution (584 persons with recorded sex)

Male340 (58.2%)
↓-4.5%prior 356
Female244 (41.8%)
↑2.5%prior 238

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: 607
  • Total persons involved: 1,093
  • Total vehicles involved: 861

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