Yearly Traffic Safety Analysis

560 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Marion County recorded 560 total crashes, a 13.4% increase from the 494 crashes reported in 2017. This period also saw a notable rise in traffic fatalities, which increased from 1 in 2017 to 5 in 2018. Total injuries also rose from 151 to 179 during the same period.

560

▲ 13.4%was 494

Total Crash Events

5

▲ 400.0%was 1

Persons Killed

179

▲ 18.5%was 151

Persons Injured

5

▲ 400.0%was 1

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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 Marion County show an upward trajectory year-over-year. Total crashes rose by 13.4%, from 494 in 2017 to 560 in 2018. This increase was accompanied by an 18.5% rise in total injuries (from 151 to 179) and a significant increase in fatalities from 1 to 5.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 1↑400.0%

1

Pedestrians Injured

Prior: 1→0.0%

6

Cyclists Injured

Prior: 0→%

172

Motorists Injured

Prior: 150↑14.7%

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 showed some shifts between 2017 and 2018. While Friday remained the peak day for crashes in both years, with incidents on that day increasing from 84 to 95, the peak hour changed. In 2018, the most crashes occurred at 3 p.m. with 45 incidents, a shift from the 6 p.m. peak observed in 2017, which had 38 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

The severity of crashes increased in 2018 compared to the previous year. The number of fatal crashes rose from 1 to 5, which increased the fatal crash rate from 0.2% to 0.9% of all incidents. The proportion of crashes resulting in serious injuries also saw a slight increase from 3.4% to 3.9%, while the share of crashes with no reported injuries decreased from 74.9% in 2017 to 72.9% in 2018.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.9%
↑400.0%prior 1
Serious Injury22serious injury crashes3.9%
↑29.4%prior 17
Minor Injury65minor injury crashes11.6%
↑58.5%prior 41
Possible Injury60possible injury crashes10.7%
↓-7.7%prior 65
No Injury408no injury crashes72.9%
↑10.3%prior 370

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 remained the leading contributing factor in both periods, with the count of such incidents rising from 157 in 2017 to 170 in 2018. While crashes attributed to 'Lost Control' decreased in count from 46 to 42, incidents of 'Followed too close' increased substantially from 21 to 40. This rise elevated 'Followed too close' to the third-most common factor in 2018, replacing 'FTYROW: From stop sign,' which saw its count decrease from 34 to 28.

Officer-Reported Primary Contributing Cause

Animal170 (30.4%)↑8.3%prior 157
Lost Control42 (7.5%)↓-8.7%prior 46
Followed too close40 (7.1%)↑90.5%prior 21
Other (explain in narrative): Other34 (6.1%)↑21.4%prior 28
FTYROW: From stop sign28 (5%)↓-17.6%prior 34
Driver Distraction: Other interior distraction26 (4.6%)↑136.4%prior 11
Driving too fast for conditions24 (4.3%)↑71.4%prior 14
Ran off road - straight16 (2.9%)↓-36.0%prior 25
Ran Stop Sign15 (2.7%)↑87.5%prior 8
Improper Backing12 (2.1%)↑140.0%prior 5

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 majority of crashes in both years occurred in clear weather and on dry roads. There was a noticeable shift in lighting conditions, with the proportion of crashes occurring in daylight increasing from 46.0% in 2017 to 52.0% in 2018. Conversely, the share of crashes in dark, unlighted conditions decreased from 21.7% to 15.2%. The proportion of crashes on roads affected by snow or ice also saw a slight increase year-over-year.

Weather

Clear296 (69.0%)
↑8.8%prior 272
Cloudy89 (20.7%)
↑7.2%prior 83
Rain15 (3.5%)
↓-21.1%prior 19
Snow11 (2.6%)
↑83.3%prior 6
Freezing rain/drizzle7 (1.6%)
Blowing Snow6 (1.4%)
Fog, smoke, smog4 (0.9%)
↓-20.0%prior 5
Other (explain in narrative)1 (0.2%)

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

Lighting

Daylight291 (67.7%)
↑28.2%prior 227
Dark - roadway not lighted85 (19.8%)
↓-20.6%prior 107
Dark - roadway lighted31 (7.2%)
↓-11.4%prior 35
Dusk15 (3.5%)
↓-11.8%prior 17
Dawn6 (1.4%)
↓-33.3%prior 9
Dark - unknown roadway lighting2 (0.5%)

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

Road Surface

Dry317 (73.9%)
↑2.3%prior 310
Wet45 (10.5%)
↑21.6%prior 37
Gravel24 (5.6%)
↑26.3%prior 19
Snow23 (5.4%)
↑91.7%prior 12
Ice/frost14 (3.3%)
↑7.7%prior 13
Slush3 (0.7%)
Other (explain in narrative)2 (0.5%)
Mud, dirt1 (0.2%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Ford, Chevrolet, and Dodge leading in both periods, and their counts increasing in line with the overall rise in crashes. An analysis of persons involved shows that the 26-34 age group represented a larger share of individuals in 2018 (18.0%) compared to 2017 (15.6%). The representation of other age groups, such as the 16-20 cohort, remained relatively stable year-over-year.

Top Vehicle Makes (827 vehicles)

1
FORD168 (20.3%)
↑7.7%prior 156
2
CHEV125 (15.1%)
↑22.5%prior 102
3
CHEVROLET53 (6.4%)
↑10.4%prior 48
4
DODG47 (5.7%)
↑23.7%prior 38
5
HOND32 (3.9%)
↑60.0%prior 20
6
JEEP32 (3.9%)
↑23.1%prior 26
7
NISS29 (3.5%)
↑81.3%prior 16
8
TOYT29 (3.5%)
↑26.1%prior 23
9
BUIC23 (2.8%)
↑4.5%prior 22
10
CHRY21 (2.5%)
↓-8.7%prior 23

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

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

Sex Distribution (607 persons with recorded sex)

Male335 (55.2%)
↑3.7%prior 323
Female272 (44.8%)
↑14.8%prior 237

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: 560
  • Total persons involved: 998
  • Total vehicles involved: 827

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

ThatCarHitMe.com · An Injuria.ai Company