Yearly Traffic Safety Analysis

1,582 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Story County, total crashes increased slightly from 1,570 in 2017 to 1,582 in 2018, a change of approximately 0.8%. Despite the stable crash volume, there was a significant improvement in crash outcomes. The most notable year-over-year shift was the 60% reduction in traffic fatalities, which fell from 5 in 2017 to 2 in 2018.

1,582

0.8%was 1,570

Total Crash Events

2

-60.0%was 5

Persons Killed

424

-11.9%was 481

Persons Injured

2

-60.0%was 5

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

Overall crash volume in Story County remained relatively stable, with a slight 0.8% increase from 1,570 incidents in 2017 to 1,582 in 2018. However, the severity of these crashes decreased markedly. The number of people injured fell by 11.8% from 481 to 424, and total fatalities dropped by 60% from 5 to 2.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

2

Motorists Killed

Prior: 5-60.0%

0

Other Killed

Prior: 00.0%

15

Pedestrians Injured

Prior: 1050.0%

16

Cyclists Injured

Prior: 21-23.8%

391

Motorists Injured

Prior: 449-12.9%

2

Other Injured

Prior: 1100.0%

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 a notable shift between the two periods. In 2017, Friday was the peak day for crashes with 338 incidents, but in 2018, Tuesday became the peak day with 255 crashes. The peak hour for collisions remained consistent across both years, occurring at 5 p.m. with 168 crashes in 2017 and 165 in 2018.

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

Crash severity decreased from 2017 to 2018. Fatal crashes dropped from 5 to 2, and the count of serious injury crashes fell from 33 to 19. This shift is reflected in the overall distribution, with the proportion of crashes resulting in any injury (fatal, serious, minor, or possible) decreasing from 24.3% in 2017 to 21.4% in 2018. Consequently, the share of crashes with no reported injuries increased from 75.7% to 78.6%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.1%
-60.0%prior 5
Serious Injury19serious injury crashes1.2%
-42.4%prior 33
Minor Injury121minor injury crashes7.6%
-4.7%prior 127
Possible Injury197possible injury crashes12.5%
-9.2%prior 217
No Injury1,243no injury crashes78.6%
4.6%prior 1,188

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

While "Followed too close" remained the leading contributing factor in both years, its count decreased by 10.9% from 276 in 2017 to 246 in 2018. The most significant change was in crashes attributed to "Driving too fast for conditions," which saw a 57.8% increase in count, rising from 109 incidents in 2017 to 172 in 2018. This change moved it from the third-ranked factor to the second-ranked factor year-over-year. Crashes involving animals also remained a top-three factor, with a slight increase in count from 154 to 163.

Officer-Reported Primary Contributing Cause

Followed too close246 (15.5%)-10.9%prior 276
Driving too fast for conditions172 (10.9%)57.8%prior 109
Animal163 (10.3%)5.8%prior 154
FTYROW: Making left turn113 (7.1%)10.8%prior 102
FTYROW: From stop sign83 (5.2%)0.0%prior 83
Other (explain in narrative): Other80 (5.1%)0.0%prior 80
Lost Control80 (5.1%)25.0%prior 64
Improper or erratic lane changing61 (3.9%)-7.6%prior 66
Ran off road - straight59 (3.7%)-9.2%prior 65
Ran off road - left55 (3.5%)-6.8%prior 59

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 weather and lighting conditions at the time of crashes remained proportionally consistent year-over-year, there was a notable shift in road surface conditions. The share of crashes occurring on dry roads decreased from 70.3% in 2017 to 60.0% in 2018. Conversely, the proportion of crashes on snow-covered roads increased from 5.4% to 9.2% of the total, and crashes on wet surfaces increased from 9.9% to 12.1%, indicating a larger share of crashes occurred during adverse road conditions in 2018.

Weather

Clear893 (62.5%)
-2.5%prior 916
Cloudy284 (19.9%)
-0.4%prior 285
Rain96 (6.7%)
-3.0%prior 99
Snow85 (5.9%)
0.0%prior 85
Freezing rain/drizzle43 (3.0%)
72.0%prior 25
Blowing Snow15 (1.0%)
-6.3%prior 16
Sleet, hail7 (0.5%)
Fog, smoke, smog3 (0.2%)
-72.7%prior 11
Severe Winds2 (0.1%)
Other (explain in narrative)1 (0.1%)

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

Lighting

Daylight1,053 (73.6%)
1.4%prior 1,038
Dark - roadway lighted197 (13.8%)
-9.6%prior 218
Dark - roadway not lighted114 (8.0%)
-5.0%prior 120
Dawn38 (2.7%)
123.5%prior 17
Dusk23 (1.6%)
-42.5%prior 40
Dark - unknown roadway lighting6 (0.4%)

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

Road Surface

Dry949 (66.4%)
-14.0%prior 1,104
Wet191 (13.4%)
22.4%prior 156
Snow145 (10.1%)
70.6%prior 85
Ice/frost91 (6.4%)
33.8%prior 68
Slush35 (2.4%)
337.5%prior 8
Gravel17 (1.2%)
21.4%prior 14
Water (standing or moving)1 (0.1%)

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

Vehicles & Demographics

The top three vehicle makes involved in crashes—Ford, Chevrolet, and Toyota—remained the same across both years. Ford vehicle involvements were nearly unchanged (446 in 2017 vs. 442 in 2018), while Chevrolet (329 to 364) and Toyota (220 to 265) saw increases in their counts. Among persons involved in crashes, the 21-25 age group saw its numbers increase from 582 to 623, while the 16-20 age group decreased slightly from 583 to 574.

Top Vehicle Makes (2,906 vehicles)

1
FORD442 (15.2%)
-0.9%prior 446
2
CHEV364 (12.5%)
10.6%prior 329
3
TOYT265 (9.1%)
20.5%prior 220
4
CHEVROLET165 (5.7%)
-4.1%prior 172
5
HOND153 (5.3%)
16.8%prior 131
6
JEEP106 (3.6%)
17.8%prior 90
7
DODG102 (3.5%)
-8.1%prior 111
8
NISS87 (3%)
-4.4%prior 91
9
TOYOTA84 (2.9%)
-25.0%prior 112
10
HONDA71 (2.4%)
-5.3%prior 75

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

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

Sex Distribution (2,406 persons with recorded sex)

Male1,351 (56.2%)
9.9%prior 1,229
Female1,055 (43.8%)
2.8%prior 1,026

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: 1,582
  • Total persons involved: 3,399
  • Total vehicles involved: 2,906

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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