Yearly Traffic Safety Analysis

247 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Jefferson County recorded 247 total crashes, a 3.4% increase from the 239 crashes reported in 2016. While total crashes saw a slight rise, the number of fatalities increased from one in 2016 to three in 2017. Similarly, total injuries rose by 26.2%, from 65 to 82, over the same period.

247

▲ 3.3%was 239

Total Crash Events

3

▲ 200.0%was 1

Persons Killed

82

▲ 26.2%was 65

Persons Injured

3

▲ 200.0%was 1

Fatal Crash Events

Note: "Persons Killed" (3) 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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Jefferson County trended upward from 2016 to 2017, with total incidents increasing by 3.4% from 239 to 247. This rise was accompanied by a more significant increase in negative outcomes, as total injuries grew by 26.2% and fatalities tripled from one to three.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 1↑100.0%

1

Pedestrians Injured

Prior: 1→0.0%

3

Cyclists Injured

Prior: 1↑200.0%

78

Motorists Injured

Prior: 62↑25.8%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The time of day with the highest crash frequency remained consistent year-over-year, with the 5 p.m. hour being the peak in both 2017 (25 crashes) and 2016 (24 crashes). However, the peak day for crashes shifted from Friday in 2016, with 45 crashes, to Thursday in 2017, with 49 crashes. Both years saw a concentration of crashes in the later months, particularly November and December.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity worsened in 2017 compared to the prior year, with the number of fatal crashes tripling from one to three. The count of serious injury crashes more than doubled from 4 to 9, representing a rise in share from 1.7% to 3.6% of all crashes. Correspondingly, the proportion of crashes resulting in no injuries decreased from 77.0% in 2016 to 72.1% in 2017.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.2%
↑200.0%prior 1
Serious Injury9serious injury crashes3.6%
↑125.0%prior 4
Minor Injury28minor injury crashes11.3%
↑33.3%prior 21
Possible Injury29possible injury crashes11.7%
→0.0%prior 29
No Injury178no injury crashes72.1%
↓-3.3%prior 184

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the leading contributing factor in both periods, with the count increasing from 71 in 2016 to 85 in 2017. 'Lost Control' also saw an increase in incidents, rising from 26 to 32. Conversely, crashes attributed to 'Driving too fast for conditions' decreased significantly, falling from 16 incidents in 2016 to 6 in 2017. 'Failure to yield from a stop sign' also saw a decline, with the count dropping from 16 to 12.

Officer-Reported Primary Contributing Cause

Animal85 (34.4%)↑19.7%prior 71
Lost Control32 (13%)↑23.1%prior 26
Other (explain in narrative): Other16 (6.5%)↑77.8%prior 9
Ran off road - straight14 (5.7%)→0.0%prior 14
Followed too close12 (4.9%)↑20.0%prior 10
FTYROW: From stop sign12 (4.9%)↓-25.0%prior 16
Ran Stop Sign10 (4%)↓-23.1%prior 13
Ran off road - left6 (2.4%)↓-40.0%prior 10
Driver Distraction: Other interior distraction6 (2.4%)
Driving too fast for conditions6 (2.4%)↓-62.5%prior 16

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

In both 2017 and 2016, the majority of crashes occurred in clear weather and on dry road surfaces. Despite an overall increase in total crashes, the number of incidents on adverse road conditions like snow, ice, or slush decreased from 27 in 2016 to 14 in 2017. Crashes in darkness on unlighted roadways saw an increase, rising from 30 incidents in 2016 to 38 in 2017.

Weather

Clear121 (69.5%)
↑7.1%prior 113
Cloudy33 (19.0%)
↑17.9%prior 28
Rain7 (4.0%)
↓-12.5%prior 8
Snow5 (2.9%)
↓-44.4%prior 9
Fog, smoke, smog4 (2.3%)
Freezing rain/drizzle2 (1.1%)
↓-71.4%prior 7
Severe Winds2 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight109 (63.0%)
↓-13.5%prior 126
Dark - roadway not lighted38 (22.0%)
↑26.7%prior 30
Dark - roadway lighted17 (9.8%)
↑54.5%prior 11
Dawn3 (1.7%)
↓-40.0%prior 5
Dusk3 (1.7%)
Dark - unknown roadway lighting3 (1.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry130 (74.7%)
↑9.2%prior 119
Wet19 (10.9%)
↑18.8%prior 16
Gravel11 (6.3%)
↑22.2%prior 9
Snow10 (5.7%)
↑11.1%prior 9
Ice/frost4 (2.3%)
↓-73.3%prior 15

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Ford, Chevrolet, and Toyota being the top three in both 2016 and 2017. An analysis of persons involved in crashes reveals a significant demographic shift, with the 16-20 age group seeing its count increase from 37 individuals in 2016 to 61 in 2017. The number of individuals aged 65 and older involved in crashes also rose from 51 to 60 over the same period.

Top Vehicle Makes (348 vehicles)

1
FORD55 (15.8%)
↓-8.3%prior 60
2
CHEV28 (8%)
↑40.0%prior 20
3
TOYT27 (7.8%)
↑22.7%prior 22
4
CHEVROLET24 (6.9%)
↓-38.5%prior 39
5
TOYOTA16 (4.6%)
↑6.7%prior 15
6
GMC13 (3.7%)
↓-18.8%prior 16
7
DODGE13 (3.7%)
↓-13.3%prior 15
8
DODG11 (3.2%)
↓-8.3%prior 12
9
PONT9 (2.6%)
↑80.0%prior 5
10
CHRY8 (2.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (257 persons with recorded sex)

Male144 (56.0%)
↑0.7%prior 143
Female113 (44.0%)
↓-15.0%prior 133

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 247
  • Total persons involved: 422
  • Total vehicles involved: 348

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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