Yearly Traffic Safety Analysis

832 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Clinton County, total traffic crashes decreased by 12.3% from 949 in 2017 to 832 in 2018. While the number of crashes and resulting injuries declined, the most notable year-over-year shift was a significant increase in fatalities, which rose from 2 in 2017 to 6 in 2018.

832

▼ -12.3%was 949

Total Crash Events

6

▲ 200.0%was 2

Persons Killed

298

▼ -11.8%was 338

Persons Injured

6

▲ 200.0%was 2

Fatal Crash Events

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (6) 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

The overall trend shows a decrease in traffic incidents, with total crashes falling by 12.3% and injuries dropping by 11.8% year-over-year. However, this downward trend in volume was contrasted by a sharp rise in crash severity, as total fatalities increased from 2 to 6 during the same period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

6

Motorists Killed

Prior: 2↑200.0%

5

Pedestrians Injured

Prior: 9↓-44.4%

4

Cyclists Injured

Prior: 13↓-69.2%

289

Motorists Injured

Prior: 316↓-8.5%

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

Temporal crash patterns remained consistent between the two periods. Thursday was the peak day for crashes in both 2018 (132 crashes) and 2017 (155 crashes). Similarly, the 3 p.m. hour was the peak time for incidents in both years, accounting for 76 crashes in 2018 and 91 in 2017.

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

While total crashes declined, the severity of outcomes worsened in 2018. The number of fatal crashes tripled from 2 to 6, and the fatal crash rate increased from 0.21 to 0.72. The total count of crashes involving any level of injury (serious, minor, or possible) decreased from 283 in 2017 to 221 in 2018, driven primarily by a reduction in 'possible injury' incidents.

Outcome by Severity (Crash Events)

Fatal6fatal crashes0.7%
↑200.0%prior 2
Serious Injury21serious injury crashes2.5%
↓-16.0%prior 25
Minor Injury78minor injury crashes9.4%
↓-9.3%prior 86
Possible Injury122possible injury crashes14.7%
↓-29.1%prior 172
No Injury605no injury crashes72.7%
↓-8.9%prior 664

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 with animals remained the leading contributing factor in both years, though the count of such incidents fell from 160 in 2017 to 145 in 2018. The top factors saw a shift in ranking, with 'Lost Control' (55 crashes) surpassing 'Failure to Yield from a Stop Sign' (51 crashes) to become the third-most common factor in 2018. Notably, crashes attributed to 'Ran Stop Sign' increased in count from 34 to 37, running counter to the overall decline in most other factors.

Officer-Reported Primary Contributing Cause

Animal145 (17.4%)↓-9.4%prior 160
Other (explain in narrative): Other63 (7.6%)↓-17.1%prior 76
Lost Control55 (6.6%)↓-15.4%prior 65
FTYROW: From stop sign51 (6.1%)↓-26.1%prior 69
Followed too close47 (5.6%)↓-21.7%prior 60
Ran Stop Sign37 (4.4%)↑8.8%prior 34
Ran off road - left36 (4.3%)↑50.0%prior 24
Ran off road - straight34 (4.1%)↑6.3%prior 32
FTYROW: Making left turn34 (4.1%)↓-22.7%prior 44
Driving too fast for conditions24 (2.9%)↓-11.1%prior 27

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 majority of crashes in both years occurred during daylight on dry roads, there was a shift in the prevalence of adverse conditions. In 2018, crashes on dry roads accounted for a smaller share of the total (61.5%) compared to 2017 (69.1%). Concurrently, the proportion of crashes on snowy or icy surfaces increased, accounting for a combined 8.2% of crashes in 2018, up from 4.6% in the previous year.

Weather

Clear468 (67.0%)
↓-15.2%prior 552
Cloudy137 (19.6%)
↓-18.9%prior 169
Rain37 (5.3%)
↑2.8%prior 36
Snow27 (3.9%)
↑22.7%prior 22
Freezing rain/drizzle15 (2.1%)
↑87.5%prior 8
Fog, smoke, smog10 (1.4%)
↓-16.7%prior 12
Blowing Snow3 (0.4%)
Other (explain in narrative)1 (0.1%)
Severe Winds1 (0.1%)

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

Lighting

Daylight469 (67.0%)
↓-16.7%prior 563
Dark - roadway not lighted100 (14.3%)
↓-9.9%prior 111
Dark - roadway lighted90 (12.9%)
↓-9.1%prior 99
Dusk20 (2.9%)
↓-4.8%prior 21
Dawn18 (2.6%)
↑80.0%prior 10
Dark - unknown roadway lighting3 (0.4%)
↓-50.0%prior 6

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

Road Surface

Dry512 (73.4%)
↓-22.0%prior 656
Wet83 (11.9%)
↓-12.6%prior 95
Snow40 (5.7%)
↑60.0%prior 25
Ice/frost28 (4.0%)
↑47.4%prior 19
Gravel19 (2.7%)
↑90.0%prior 10
Slush15 (2.1%)
Sand1 (0.1%)

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

Vehicles & Demographics

Ford and Chevrolet were the most common vehicle makes involved in crashes in both 2018 and 2017, with the counts for both decreasing in line with the overall trend. The age distribution of persons involved in crashes remained largely consistent year-over-year. There was a slight proportional increase in the 16-20 age group, which represented 13.4% of persons in 2018 compared to 12.1% in 2017.

Top Vehicle Makes (1,362 vehicles)

1
FORD215 (15.8%)
↓-13.0%prior 247
2
CHEV205 (15.1%)
↑0.5%prior 204
3
CHEVROLET115 (8.4%)
↓-32.7%prior 171
4
NR60 (4.4%)
↓-20.0%prior 75
5
GMC58 (4.3%)
↓-17.1%prior 70
6
TOYT52 (3.8%)
↑36.8%prior 38
7
DODG45 (3.3%)
↓-27.4%prior 62
8
TOYOTA44 (3.2%)
↑4.8%prior 42
9
DODGE33 (2.4%)
↓-26.7%prior 45
10
CHRY32 (2.3%)
↓-11.1%prior 36

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

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

Sex Distribution (925 persons with recorded sex)

Male518 (56.0%)
↓-3.0%prior 534
Female407 (44.0%)
↓-18.3%prior 498

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: 832
  • Total persons involved: 1,706
  • Total vehicles involved: 1,362

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

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