Yearly Traffic Safety Analysis

3,940 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Scott County, total vehicle crashes decreased by 3.7% from 4,090 in 2017 to 3,940 in 2018. Despite the overall reduction in crashes and a 12.5% drop in injuries, the number of fatalities increased from 8 to 13 year-over-year. The most notable shift was a 77.5% increase in crashes attributed to 'Driving too fast for conditions'.

3,940

▼ -3.7%was 4,090

Total Crash Events

13

▲ 62.5%was 8

Persons Killed

1,339

▼ -12.5%was 1,530

Persons Injured

12

▲ 50.0%was 8

Fatal Crash Events

Note: "Persons Killed" (13) counts individual fatalities across all crash events. "Fatal" in the severity table below (12) 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 in Scott County shows a decrease in total traffic incidents, falling from 4,090 crashes in 2017 to 3,940 in 2018. This represents a 3.7% year-over-year reduction. While total injuries also declined from 1,530 to 1,339, fatalities saw a contrasting increase from 8 to 13.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 1↑100.0%

1

Cyclists Killed

Prior: 0→%

10

Motorists Killed

Prior: 7↑42.9%

39

Pedestrians Injured

Prior: 31↑25.8%

23

Cyclists Injured

Prior: 17↑35.3%

1,277

Motorists Injured

Prior: 1,481↓-13.8%

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 temporal patterns of crashes remained largely consistent between the two periods. Friday was the peak day for crashes in both 2018 (674 crashes) and 2017 (711 crashes). The peak hour for collisions shifted slightly earlier, from the 5 PM hour in 2017 (375 crashes) to the 4 PM hour in 2018 (359 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

While total crashes declined, the severity of outcomes worsened in 2018. The number of fatal crashes rose from 8 to 12, and the total number of people killed increased from 8 to 13. Consequently, the fatal crash rate increased from 0.2% to 0.3% of all crashes. The share of crashes resulting in possible injuries decreased from 22.7% to 21.4%, while the proportion of no-injury crashes grew from 67.6% to 68.9%.

Severity is per crash event (most severe injury). 12 fatal crash events resulted in 13 persons killed.

Outcome by Severity (Crash Events)

Fatal12fatal crashes0.3%
↑50.0%prior 8
Serious Injury51serious injury crashes1.3%
↓-13.6%prior 59
Minor Injury318minor injury crashes8.1%
↓-3.9%prior 331
Possible Injury845possible injury crashes21.4%
↓-8.8%prior 927
No Injury2,714no injury crashes68.9%
↓-1.8%prior 2,765

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 'Followed too close,' though its count decreased from 729 in 2017 to 637 in 2018. 'Ran off road - left' remained the second-most cited factor, with its count slightly increasing from 394 to 410. A significant change occurred with 'Driving too fast for conditions,' which saw its count jump by 77.5% from 129 crashes in 2017 to 229 in 2018, making it the third leading factor in the current period.

Officer-Reported Primary Contributing Cause

Followed too close637 (16.2%)↓-12.6%prior 729
Ran off road - left410 (10.4%)↑4.1%prior 394
Driving too fast for conditions229 (5.8%)↑77.5%prior 129
Other (explain in narrative): Other204 (5.2%)↓-1.0%prior 206
FTYROW: Making left turn203 (5.2%)↓-13.2%prior 234
Ran Traffic Signal198 (5%)↓-7.9%prior 215
FTYROW: From stop sign193 (4.9%)↓-6.3%prior 206
Animal184 (4.7%)↓-8.0%prior 200
Operating vehicle in an reckless, erratic, careless, negligent manner152 (3.9%)↓-12.6%prior 174
Lost Control133 (3.4%)↓-16.4%prior 159

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

Road & Environmental Conditions

The distribution of crashes by lighting conditions remained stable year-over-year, with daylight being the most common condition in both periods. However, there was a notable shift in crashes related to adverse weather and road surfaces. Crashes occurring in snow conditions increased from 99 in 2017 to 157 in 2018, and collisions on roads with snow, ice, or slush rose from 169 to 392.

Weather

Clear2,463 (65.3%)
↓-1.4%prior 2,498
Cloudy773 (20.5%)
↓-22.1%prior 992
Rain233 (6.2%)
↓-11.1%prior 262
Snow157 (4.2%)
↑58.6%prior 99
Freezing rain/drizzle100 (2.6%)
↑170.3%prior 37
Fog, smoke, smog23 (0.6%)
↑9.5%prior 21
Blowing Snow17 (0.5%)
Sleet, hail5 (0.1%)
Other (explain in narrative)2 (0.1%)
Severe Winds1 (0.0%)

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

Lighting

Daylight2,698 (71.4%)
↓-1.6%prior 2,741
Dark - roadway lighted757 (20.0%)
↓-10.4%prior 845
Dark - roadway not lighted196 (5.2%)
↓-1.0%prior 198
Dusk75 (2.0%)
↓-1.3%prior 76
Dawn47 (1.2%)
↑9.3%prior 43
Dark - unknown roadway lighting4 (0.1%)
↓-69.2%prior 13

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

Road Surface

Dry2,777 (73.5%)
↓-12.7%prior 3,180
Wet593 (15.7%)
↑8.4%prior 547
Snow193 (5.1%)
↑93.0%prior 100
Ice/frost162 (4.3%)
↑179.3%prior 58
Slush37 (1.0%)
↑236.4%prior 11
Gravel8 (0.2%)
↓-46.7%prior 15
Other (explain in narrative)5 (0.1%)
Water (standing or moving)1 (0.0%)

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

Vehicles & Demographics

The composition of vehicles involved in crashes was consistent across both years, with Ford and Chevrolet models being the most frequently involved makes in both 2017 and 2018. The demographic distribution of persons involved in crashes also showed little change. The representation of most age groups remained stable, with only minor shifts such as a slight decrease in the proportion of individuals aged 16-20 and a small increase for those aged 55-64.

Top Vehicle Makes (7,511 vehicles)

1
FORD1,407 (18.7%)
↓-0.1%prior 1,409
2
CHEV713 (9.5%)
↑3.8%prior 687
3
CHEVROLET567 (7.5%)
↓-5.7%prior 601
4
TOYT351 (4.7%)
↓-2.8%prior 361
5
NR303 (4%)
↑11.4%prior 272
6
HOND284 (3.8%)
↓-12.6%prior 325
7
TOYOTA269 (3.6%)
↓-1.5%prior 273
8
JEEP241 (3.2%)
↑4.8%prior 230
9
GMC224 (3%)
↑0.9%prior 222
10
HONDA208 (2.8%)
↑0.5%prior 207

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

1,738 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (5,639 persons with recorded sex)

Male3,015 (53.5%)
↑0.4%prior 3,002
Female2,624 (46.5%)
↓-1.5%prior 2,664

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: 3,940
  • Total persons involved: 9,063
  • Total vehicles involved: 7,511

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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