Yearly Traffic Safety Analysis

2,602 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Johnson County, total crashes remained stable between 2016 and 2017, with 2,601 and 2,602 incidents recorded, respectively, a change of less than 0.1%. While overall crash volume was flat, the most significant year-over-year shift was a 50% reduction in traffic fatalities, which fell from 16 in 2016 to 8 in 2017.

2,602

Total Crash Events

8

▼ -50.0%was 16

Persons Killed

738

▼ -4.4%was 772

Persons Injured

8

▼ -42.9%was 14

Fatal Crash Events

Note: "Persons Killed" (8) counts individual fatalities across all crash events. "Fatal" in the severity table below (8) 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 crash volume in Johnson County was nearly unchanged year-over-year, increasing by a single incident from 2,601 in 2016 to 2,602 in 2017. Despite the stable crash total, the number of people injured decreased by 4.4% from 772 to 738. Most notably, traffic fatalities were halved, falling from 16 to 8.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 2↓-50.0%

0

Cyclists Killed

Prior: 1↓-100.0%

7

Motorists Killed

Prior: 13↓-46.2%

0

Other Killed

Prior: 0→0.0%

30

Pedestrians Injured

Prior: 36↓-16.7%

32

Cyclists Injured

Prior: 47↓-31.9%

673

Motorists Injured

Prior: 687↓-2.0%

3

Other Injured

Prior: 2↑50.0%

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 temporal patterns of crashes in Johnson County remained consistent between 2016 and 2017. Friday continued to be the day with the most crashes, with incidents on this day increasing from 458 to 515. Similarly, the 5 PM hour remained the peak time for collisions in both periods, with counts of 253 in 2016 and 260 in 2017.

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 outcomes improved from 2016 to 2017, with the number of fatal crashes decreasing from 14 to 8. The share of crashes resulting in any injury declined, while the proportion of no-injury crashes increased from 74.1% to 75.3% of all incidents. Specifically, crashes involving minor injuries fell from 220 to 202, and their share of total crashes decreased from 8.5% to 7.8%.

Outcome by Severity (Crash Events)

Fatal8fatal crashes0.3%
↓-42.9%prior 14
Serious Injury43serious injury crashes1.7%
↓-4.4%prior 45
Minor Injury202minor injury crashes7.8%
↓-8.2%prior 220
Possible Injury389possible injury crashes15%
↓-1.3%prior 394
No Injury1,960no injury crashes75.3%
↑1.7%prior 1,928

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

The leading contributing factor in both periods was 'Followed too close,' though its count decreased by 12.3% from 586 incidents in 2016 to 514 in 2017. While the count for most top factors decreased, crashes involving animals increased from 147 to 161. Additionally, incidents attributed to 'Failure to yield right-of-way from a stop sign' rose from 115 to 138.

Officer-Reported Primary Contributing Cause

Followed too close514 (19.8%)↓-12.3%prior 586
Other (explain in narrative): Other192 (7.4%)↑44.4%prior 133
Driving too fast for conditions163 (6.3%)↓-8.4%prior 178
Animal161 (6.2%)↑9.5%prior 147
FTYROW: From stop sign138 (5.3%)↑20.0%prior 115
Improper or erratic lane changing105 (4%)↑10.5%prior 95
FTYROW: Making left turn99 (3.8%)↑10.0%prior 90
Ran off road - left98 (3.8%)↑4.3%prior 94
Made improper turn97 (3.7%)↑12.8%prior 86
Lost Control90 (3.5%)↓-20.4%prior 113

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

Road & Environmental Conditions

The proportion of crashes occurring in clear weather and daylight conditions remained largely stable year-over-year. However, there was a notable shift in crashes related to precipitation, with the number of incidents during rainy conditions increasing from 108 in 2016 to 207 in 2017. Correspondingly, collisions on wet road surfaces increased from 270 to 375, while crashes on snowy or icy surfaces decreased.

Weather

Clear1,558 (62.2%)
↑1.5%prior 1,535
Cloudy605 (24.1%)
↓-15.4%prior 715
Rain207 (8.3%)
↑91.7%prior 108
Snow73 (2.9%)
↓-37.1%prior 116
Fog, smoke, smog26 (1.0%)
↑136.4%prior 11
Freezing rain/drizzle22 (0.9%)
↑10.0%prior 20
Blowing Snow7 (0.3%)
↓-12.5%prior 8
Other (explain in narrative)4 (0.2%)
Sleet, hail2 (0.1%)
Severe Winds1 (0.0%)

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

Lighting

Daylight1,866 (74.1%)
↓-0.4%prior 1,874
Dark - roadway lighted301 (11.9%)
↓-0.3%prior 302
Dark - roadway not lighted243 (9.6%)
↑2.1%prior 238
Dusk63 (2.5%)
↑6.8%prior 59
Dawn38 (1.5%)
↓-20.8%prior 48
Dark - unknown roadway lighting8 (0.3%)
↓-20.0%prior 10

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

Road Surface

Dry1,983 (79.0%)
↓-0.9%prior 2,001
Wet375 (14.9%)
↑38.9%prior 270
Snow91 (3.6%)
↓-24.2%prior 120
Ice/frost37 (1.5%)
↓-47.9%prior 71
Gravel16 (0.6%)
↓-5.9%prior 17
Slush3 (0.1%)
↓-90.6%prior 32
Sand1 (0.0%)
Other (explain in narrative)1 (0.0%)
Oil1 (0.0%)
Mud, dirt1 (0.0%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes were consistent year-over-year, with Ford, Chevrolet, and Toyota vehicles being the most common in both 2016 and 2017. The distribution of persons involved in crashes by age group also showed little change. The 26-34 age group represented the largest cohort in both periods, accounting for 895 individuals in 2016 and 896 in 2017, indicating a stable demographic profile of crash participants.

Top Vehicle Makes (4,902 vehicles)

1
FORD688 (14%)
↑1.6%prior 677
2
CHEV451 (9.2%)
↑10.5%prior 408
3
TOYT447 (9.1%)
↑19.2%prior 375
4
HOND278 (5.7%)
↑36.3%prior 204
5
CHEVROLET248 (5.1%)
↓-17.9%prior 302
6
TOYOTA185 (3.8%)
↓-22.3%prior 238
7
JEEP142 (2.9%)
↓-4.7%prior 149
8
NISS140 (2.9%)
↑13.8%prior 123
9
DODG138 (2.8%)
↑13.1%prior 122
10
NR126 (2.6%)
↑16.7%prior 108

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

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

Sex Distribution (3,980 persons with recorded sex)

Male2,155 (54.1%)
↓-0.7%prior 2,171
Female1,825 (45.9%)
↑2.9%prior 1,773

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: 2,602
  • Total persons involved: 5,450
  • Total vehicles involved: 4,902

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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