Yearly Traffic Safety Analysis

288 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Jones County recorded 288 total crashes, a 5.9% increase from the 272 crashes documented in 2017. While total fatalities remained unchanged at one and total injuries slightly decreased from 76 to 74, the overall volume of collisions grew. A notable change was the shift in the daily peak crash time from the morning commute (6 a.m.) in 2017 to the evening (6 p.m.) in 2018.

288

▲ 5.9%was 272

Total Crash Events

1

Persons Killed

74

▼ -2.6%was 76

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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

Crash trends in Jones County showed a slight increase year-over-year, with total collisions rising by 5.9% from 272 in 2017 to 288 in 2018. Despite the rise in crash volume, key severity metrics remained stable or saw minor improvement. The number of fatalities held steady at one, and the total number of people injured decreased slightly from 76 to 74.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 0→%

74

Motorists Injured

Prior: 75↓-1.3%

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 shifted notably between the two years. The peak day for crashes moved from Wednesday (50 crashes) in 2017 to Monday (50 crashes) in 2018. More significantly, the peak hour for collisions changed from 6 a.m. in the prior year (22 crashes) to 6 p.m. in the current year (29 crashes), indicating a shift from a morning to an evening rush hour peak.

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

The overall severity of crashes showed some improvement despite an increase in total incidents. The number of fatal crashes remained constant at one in both 2018 and 2017. However, the count of serious injury crashes decreased from 14 in 2017 to 8 in 2018, and possible injury crashes fell from 34 to 22. Consequently, the share of crashes resulting in any injury or fatality dropped from 25.0% in 2017 to 20.1% in 2018.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
→0.0%prior 1
Serious Injury8serious injury crashes2.8%
↓-42.9%prior 14
Minor Injury27minor injury crashes9.4%
↑42.1%prior 19
Possible Injury22possible injury crashes7.6%
↓-35.3%prior 34
No Injury230no injury crashes79.9%
↑12.7%prior 204

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 periods, with the count increasing from 126 in 2017 to 137 in 2018. 'Lost Control' also saw an increase, rising from 20 to 25 incidents, and remained the second-most cited factor. 'FTYROW: From stop sign' became the third most common factor in 2018 with 15 crashes, up from 13 in the previous year.

Officer-Reported Primary Contributing Cause

Animal137 (47.6%)↑8.7%prior 126
Lost Control25 (8.7%)↑25.0%prior 20
FTYROW: From stop sign15 (5.2%)↑15.4%prior 13
Ran off road - straight13 (4.5%)↓-7.1%prior 14
Other (explain in narrative): Other12 (4.2%)↑71.4%prior 7
Followed too close10 (3.5%)↑66.7%prior 6
Driving too fast for conditions10 (3.5%)↑100.0%prior 5
Driver Distraction: Other interior distraction7 (2.4%)↓-12.5%prior 8
Ran off road - left7 (2.4%)↓-12.5%prior 8
Driver Distraction: Inattentive/lost in thought4 (1.4%)

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 most crashes in both years occurred in clear weather, there was a notable increase in incidents under adverse winter conditions. The number of crashes on icy or snowy road surfaces more than doubled, increasing from a combined 10 incidents in 2017 to 26 in 2018. Conversely, crashes on dry roads decreased from 128 to 110. The distribution of crashes by lighting conditions remained largely consistent year-over-year.

Weather

Clear107 (62.2%)
↑8.1%prior 99
Cloudy28 (16.3%)
↓-40.4%prior 47
Rain13 (7.6%)
↑8.3%prior 12
Freezing rain/drizzle11 (6.4%)
Snow8 (4.7%)
↑33.3%prior 6
Blowing Snow3 (1.7%)
Fog, smoke, smog2 (1.2%)

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

Lighting

Daylight104 (60.1%)
↑6.1%prior 98
Dark - roadway not lighted48 (27.7%)
↓-9.4%prior 53
Dark - roadway lighted10 (5.8%)
↓-9.1%prior 11
Dawn8 (4.6%)
Dusk3 (1.7%)
↓-57.1%prior 7

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

Road Surface

Dry110 (64.3%)
↓-14.1%prior 128
Wet20 (11.7%)
↓-4.8%prior 21
Ice/frost14 (8.2%)
Snow12 (7.0%)
↑100.0%prior 6
Gravel8 (4.7%)
↓-11.1%prior 9
Slush4 (2.3%)
Other (explain in narrative)2 (1.2%)
Mud, dirt1 (0.6%)

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 similar, with Ford and Chevrolet continuing to be the most common. The number of Ford vehicles in crashes rose from 57 to 87, while Chevrolet (CHEV) vehicles increased from 55 to 63. Analysis of person demographics shows increased involvement for several age groups; notably, the number of people aged 35-44 involved in crashes grew from 54 to 85, and the 65+ age group increased from 45 to 64.

Top Vehicle Makes (386 vehicles)

1
FORD87 (22.5%)
↑52.6%prior 57
2
CHEV63 (16.3%)
↑14.5%prior 55
3
DODG19 (4.9%)
↑11.8%prior 17
4
CHRY16 (4.1%)
↑6.7%prior 15
5
CHEVROLET13 (3.4%)
↓-45.8%prior 24
6
GMC11 (2.8%)
↓-26.7%prior 15
7
TOYO10 (2.6%)
↑100.0%prior 5
8
JEEP10 (2.6%)
↓-16.7%prior 12
9
KIA9 (2.3%)
↑80.0%prior 5
10
NISS9 (2.3%)
↑28.6%prior 7

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

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

Sex Distribution (274 persons with recorded sex)

Male175 (63.9%)
↑24.1%prior 141
Female99 (36.1%)
↑32.0%prior 75

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: 288
  • Total persons involved: 504
  • Total vehicles involved: 386

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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