Yearly Traffic Safety Analysis

3,763 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Linn County, total traffic crashes increased by 8.6% from 3,465 in 2017 to 3,763 in 2018. While total fatalities decreased from 16 to 14, the number of serious injury crashes saw a significant year-over-year rise of 45.8%, increasing from 48 to 70 incidents. This increase in crash severity represents the most notable shift in the data.

3,763

▲ 8.6%was 3,465

Total Crash Events

14

▼ -12.5%was 16

Persons Killed

1,241

▲ 10.8%was 1,120

Persons Injured

12

▼ -20.0%was 15

Fatal Crash Events

Note: "Persons Killed" (14) 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 Linn County shows an increase in traffic collisions from 2017 to 2018. Total crashes rose by 8.6% (from 3,465 to 3,763), and the number of people injured increased by 10.8% (from 1,120 to 1,241). Conversely, the number of fatalities saw a slight decline from 16 to 14.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 1→0.0%

1

Cyclists Killed

Prior: 0→%

12

Motorists Killed

Prior: 15↓-20.0%

0

Other Killed

Prior: 0→0.0%

27

Pedestrians Injured

Prior: 20↑35.0%

20

Cyclists Injured

Prior: 21↓-4.8%

1,190

Motorists Injured

Prior: 1,078↑10.4%

4

Other Injured

Prior: 1↑300.0%

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 time of day when crashes were most frequent remained consistent, with the 5 PM hour being the peak in both 2017 (336 crashes) and 2018 (348 crashes). However, the peak day for crashes shifted from Friday in 2017 (645 crashes) to Tuesday in 2018 (643 crashes). Weekday afternoon commute hours continued to be the highest-volume periods in both years.

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 the number of fatal crashes decreased from 15 in 2017 to 12 in 2018, the data shows a notable increase in other severe outcomes. The count of serious injury crashes rose by 45.8%, from 48 incidents in 2017 to 70 in 2018. The proportion of crashes resulting in possible injuries decreased from 16.8% to 14.8%, while crashes with no injuries increased their share from 73.1% to 74.6% of all incidents.

Severity is per crash event (most severe injury). 12 fatal crash events resulted in 14 persons killed.

Outcome by Severity (Crash Events)

Fatal12fatal crashes0.3%
↓-20.0%prior 15
Serious Injury70serious injury crashes1.9%
↑45.8%prior 48
Minor Injury317minor injury crashes8.4%
↑10.5%prior 287
Possible Injury558possible injury crashes14.8%
↓-4.1%prior 582
No Injury2,806no injury crashes74.6%
↑10.8%prior 2,533

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 periods was 'Followed too close,' with incidents increasing by 8.7% from 414 in 2017 to 450 in 2018. Crashes involving animals saw a significant 23.5% increase in count, rising from 260 to 321 and moving from the fourth to the third most common factor. Conversely, crashes attributed to 'Failure to yield right of way when making a left turn' decreased by 8.9% in count, falling from 281 to 256 incidents.

Officer-Reported Primary Contributing Cause

Followed too close450 (12%)↑8.7%prior 414
Ran off road - left335 (8.9%)↑9.5%prior 306
Animal321 (8.5%)↑23.5%prior 260
FTYROW: From stop sign271 (7.2%)↑18.9%prior 228
FTYROW: Making left turn256 (6.8%)↓-8.9%prior 281
Ran Traffic Signal203 (5.4%)↑9.1%prior 186
Other (explain in narrative): Other201 (5.3%)↓-1.5%prior 204
Driving too fast for conditions179 (4.8%)↑53.0%prior 117
Lost Control153 (4.1%)↑34.2%prior 114
Ran Stop Sign104 (2.8%)↑15.6%prior 90

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

Road & Environmental Conditions

Crash data indicates a significant increase in collisions occurring during adverse conditions. Crashes on roads with snow, ice, or slush increased from 144 in 2017 to 384 in 2018, and their share of total crashes grew from 4.2% to 10.2%. Similarly, collisions in rainy weather increased from 212 to 239. Lighting conditions remained relatively stable, with daylight crashes accounting for approximately 67% of incidents in both years.

Weather

Clear2,088 (59.9%)
↑4.6%prior 1,996
Cloudy880 (25.3%)
↑0.9%prior 872
Rain239 (6.9%)
↑12.7%prior 212
Snow127 (3.6%)
↑64.9%prior 77
Freezing rain/drizzle103 (3.0%)
↑157.5%prior 40
Fog, smoke, smog15 (0.4%)
↓-48.3%prior 29
Sleet, hail9 (0.3%)
Blowing Snow9 (0.3%)
↑80.0%prior 5
Other (explain in narrative)7 (0.2%)
Severe Winds5 (0.1%)

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

Lighting

Daylight2,522 (72.3%)
↑7.3%prior 2,350
Dark - roadway lighted560 (16.1%)
↑9.4%prior 512
Dark - roadway not lighted205 (5.9%)
↓-0.5%prior 206
Dusk114 (3.3%)
↑6.5%prior 107
Dawn76 (2.2%)
↑46.2%prior 52
Dark - unknown roadway lighting12 (0.3%)
↑9.1%prior 11

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

Road Surface

Dry2,505 (71.8%)
↓-3.6%prior 2,598
Wet566 (16.2%)
↑22.8%prior 461
Ice/frost173 (5.0%)
↑276.1%prior 46
Snow161 (4.6%)
↑73.1%prior 93
Slush50 (1.4%)
↑900.0%prior 5
Gravel20 (0.6%)
↓-16.7%prior 24
Sand4 (0.1%)
↓-20.0%prior 5
Other (explain in narrative)4 (0.1%)
Mud, dirt2 (0.1%)
Water (standing or moving)2 (0.1%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent year-over-year, with Ford and Chevrolet models being the most frequent in both 2017 and 2018. The number of Ford vehicles in crashes grew from 1,075 to 1,128, while the top make rankings saw no significant changes. The age distribution of persons involved also showed broad stability, although the 26-34 age group saw its representation increase slightly from 16.2% of all persons in 2017 to 17.4% in 2018.

Top Vehicle Makes (6,952 vehicles)

1
FORD1,128 (16.2%)
↑4.9%prior 1,075
2
CHEV908 (13.1%)
↑22.2%prior 743
3
TOYT522 (7.5%)
↑9.4%prior 477
4
DODG322 (4.6%)
↑27.8%prior 252
5
CHEVROLET305 (4.4%)
↓-20.6%prior 384
6
HOND278 (4%)
↑8.6%prior 256
7
NISS213 (3.1%)
↑19.7%prior 178
8
KIA208 (3%)
↑28.4%prior 162
9
JEEP197 (2.8%)
↓-8.4%prior 215
10
GMC194 (2.8%)
↑11.5%prior 174

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

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

Sex Distribution (5,980 persons with recorded sex)

Male3,197 (53.5%)
↑14.5%prior 2,792
Female2,783 (46.5%)
↑9.6%prior 2,539

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: 3,763
  • Total persons involved: 8,070
  • Total vehicles involved: 6,952

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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