Yearly Traffic Safety Analysis

247 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Jefferson County, the total number of traffic crashes remained unchanged year-over-year, with 247 incidents recorded in both 2018 and 2017. Despite the stable crash volume, outcomes shifted, as total fatalities increased from 3 to 4. The most notable change was a decrease in total injuries, which fell from 82 in the prior year to 74 in the current period.

247

Total Crash Events

4

▲ 33.3%was 3

Persons Killed

74

▼ -9.8%was 82

Persons Injured

4

▲ 33.3%was 3

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 for crashes in Jefferson County was stable, with an identical count of 247 incidents in 2018 and 2017. However, the severity of these incidents worsened, reflected by a 33.3% increase in fatalities from 3 to 4. In contrast, the number of people injured in crashes declined by 9.8%, from 82 to 74.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 2↑100.0%

0

Other Killed

Prior: 0→0.0%

1

Cyclists Injured

Prior: 3↓-66.7%

72

Motorists Injured

Prior: 78↓-7.7%

1

Other Injured

Prior: 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 timing of crashes showed remarkable consistency between the two periods. Thursday remained the peak day for crashes in both 2018 (44 crashes) and 2017 (49 crashes). Similarly, the 5 p.m. hour was the most frequent time for collisions in both years, accounting for 23 incidents in 2018 and 25 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 the total number of crashes was static, the severity distribution changed. Fatal crashes increased from 3 in 2017 to 4 in 2018, raising the fatal crash rate from 1.2% to 1.6% of all incidents. Conversely, the proportion of crashes resulting in any form of injury (serious, minor, or possible) decreased, with the total count of such crashes falling from 66 in 2017 to 55 in 2018. Consequently, no-injury crashes increased their share from 72.1% to 76.1% of all incidents.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.6%
↑33.3%prior 3
Serious Injury6serious injury crashes2.4%
↓-33.3%prior 9
Minor Injury25minor injury crashes10.1%
↓-10.7%prior 28
Possible Injury24possible injury crashes9.7%
↓-17.2%prior 29
No Injury188no injury crashes76.1%
↑5.6%prior 178

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 involving an animal were the top contributing factor in both periods, with the count increasing from 85 in 2017 to 88 in 2018. 'Lost Control' remained the second-ranked factor, with its count holding steady at 32 crashes. A significant year-over-year shift was seen in crashes attributed to 'Driving too fast for conditions,' where the count more than doubled from 6 to 14. In contrast, crashes due to 'Ran Stop Sign' decreased from 10 to 6.

Officer-Reported Primary Contributing Cause

Animal88 (35.6%)↑3.5%prior 85
Lost Control32 (13%)→0.0%prior 32
FTYROW: From stop sign14 (5.7%)↑16.7%prior 12
Ran off road - straight14 (5.7%)→0.0%prior 14
Driving too fast for conditions14 (5.7%)↑133.3%prior 6
Other (explain in narrative): Other11 (4.5%)↓-31.3%prior 16
Followed too close10 (4%)↓-16.7%prior 12
Ran off road - left7 (2.8%)↑16.7%prior 6
FTYROW: From yield sign7 (2.8%)
Ran Stop Sign6 (2.4%)↓-40.0%prior 10

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

Road & Environmental Conditions

Most crashes in both 2018 and 2017 occurred during daylight (112 and 109, respectively) and in clear weather (120 and 121, respectively). However, there was a notable increase in crashes happening on adverse road surfaces, with incidents on wet, snowy, or icy roads rising from 33 in 2017 to 43 in 2018. This corresponds with a decrease in crashes on dry surfaces, which fell from 130 to 118.

Weather

Clear120 (70.2%)
↓-0.8%prior 121
Cloudy25 (14.6%)
↓-24.2%prior 33
Freezing rain/drizzle8 (4.7%)
Rain8 (4.7%)
↑14.3%prior 7
Snow7 (4.1%)
↑40.0%prior 5
Fog, smoke, smog2 (1.2%)
Severe Winds1 (0.6%)

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

Lighting

Daylight112 (65.1%)
↑2.8%prior 109
Dark - roadway not lighted38 (22.1%)
→0.0%prior 38
Dark - roadway lighted12 (7.0%)
↓-29.4%prior 17
Dusk6 (3.5%)
Dawn4 (2.3%)

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

Road Surface

Dry118 (69.0%)
↓-9.2%prior 130
Wet22 (12.9%)
↑15.8%prior 19
Snow11 (6.4%)
↑10.0%prior 10
Ice/frost8 (4.7%)
Gravel8 (4.7%)
↓-27.3%prior 11
Slush2 (1.2%)
Sand1 (0.6%)
Other (explain in narrative)1 (0.6%)

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

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 348 in 2017 to 336 in 2018. Chevrolet-branded vehicles became the most frequently involved make with 69 incidents, surpassing Ford, which was involved in 53 crashes. The age distribution of persons involved also shifted, with notable increases in the 26-34 age group (from 56 to 82) and the 45-54 age group (from 46 to 72). Meanwhile, involvement of persons aged 16-20 decreased from 61 to 45.

Top Vehicle Makes (336 vehicles)

1
FORD53 (15.8%)
↓-3.6%prior 55
2
CHEV41 (12.2%)
↑46.4%prior 28
3
CHEVROLET28 (8.3%)
↑16.7%prior 24
4
TOYT27 (8%)
→0.0%prior 27
5
DODG22 (6.5%)
↑100.0%prior 11
6
GMC12 (3.6%)
↓-7.7%prior 13
7
KIA9 (2.7%)
↑80.0%prior 5
8
TOYOTA9 (2.7%)
↓-43.8%prior 16
9
DODGE9 (2.7%)
↓-30.8%prior 13
10
BUIC9 (2.7%)
↑12.5%prior 8

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

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

Sex Distribution (262 persons with recorded sex)

Male140 (53.4%)
↓-2.8%prior 144
Female122 (46.6%)
↑8.0%prior 113

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: 247
  • Total persons involved: 436
  • Total vehicles involved: 336

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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