Yearly Traffic Safety Analysis

510 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Muscatine County recorded 510 total crashes, a 3% decrease from the 526 crashes documented in 2015. While total crashes and the number of injuries (210, down from 250) both declined, the number of fatalities increased significantly, rising from one in the prior year to five in the current year.

510

▼ -3.0%was 526

Total Crash Events

5

▲ 400.0%was 1

Persons Killed

210

▼ -16.0%was 250

Persons Injured

4

▲ 300.0%was 1

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crashes in Muscatine County showed a slight downward trend, decreasing by 3% from 526 in 2015 to 510 in 2016. This decline was accompanied by a 16% reduction in total injuries, from 250 to 210. However, fatal crashes increased from one to four, resulting in five fatalities compared to one in the previous year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Cyclists Killed

Prior: 0→%

4

Motorists Killed

Prior: 1↑300.0%

0

Other Killed

Prior: 0→0.0%

7

Pedestrians Injured

Prior: 3↑133.3%

5

Cyclists Injured

Prior: 4↑25.0%

196

Motorists Injured

Prior: 243↓-19.3%

2

Other Injured

Prior: 0→%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted slightly between the two periods. The peak day for crashes moved from Monday in 2015 (93 crashes) to Friday in 2016 (93 crashes), with the peak volume remaining the same. The peak hour for collisions also shifted later in the afternoon, from the 3 p.m. hour in 2015 (43 crashes) to the 5 p.m. hour in 2016 (45 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While most crash severity categories saw a decrease year-over-year, fatal incidents increased notably. Fatal crashes rose from one in 2015 to four in 2016, representing a shift from 0.2% to 0.8% of all crashes. Conversely, crashes resulting in serious injuries fell from 23 to 17, and the proportion of crashes with no reported injuries increased from 64.6% to 67.5% of all incidents.

Severity is per crash event (most severe injury). 4 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.8%
↑300.0%prior 1
Serious Injury17serious injury crashes3.3%
↓-26.1%prior 23
Minor Injury65minor injury crashes12.7%
↓-8.5%prior 71
Possible Injury80possible injury crashes15.7%
↓-12.1%prior 91
No Injury344no injury crashes67.5%
↑1.2%prior 340

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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 99 crashes in 2015 to 110 in 2016. 'Following too close' also saw a notable increase in count from 32 to 45 incidents. In contrast, crashes attributed to 'Driving too fast for conditions' decreased from 38 to 23, and those related to 'Failure to yield right of way when making a left turn' fell from 38 to 25.

Officer-Reported Primary Contributing Cause

Animal110 (21.6%)↑11.1%prior 99
Followed too close45 (8.8%)↑40.6%prior 32
Lost Control38 (7.5%)↑2.7%prior 37
Ran off road - left27 (5.3%)↑50.0%prior 18
FTYROW: From stop sign27 (5.3%)↓-15.6%prior 32
FTYROW: Making left turn25 (4.9%)↓-34.2%prior 38
Driving too fast for conditions23 (4.5%)↓-39.5%prior 38
Ran off road - straight22 (4.3%)↑10.0%prior 20
Other (explain in narrative): Other17 (3.3%)↓-32.0%prior 25
Ran Traffic Signal15 (2.9%)↑87.5%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes across environmental conditions remained largely consistent year-over-year. The majority of collisions in both 2016 and 2015 occurred in daylight (284 and 296 crashes, respectively) and on dry road surfaces (310 and 313 crashes). There was a decrease in crashes reported during adverse weather, with incidents in the snow falling from 29 to 17.

Weather

Clear277 (64.1%)
↓-0.7%prior 279
Cloudy103 (23.8%)
↑3.0%prior 100
Rain20 (4.6%)
↓-33.3%prior 30
Snow17 (3.9%)
↓-41.4%prior 29
Freezing rain/drizzle9 (2.1%)
Fog, smoke, smog3 (0.7%)
Blowing Snow2 (0.5%)
Sleet, hail1 (0.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight284 (65.6%)
↓-4.1%prior 296
Dark - roadway not lighted68 (15.7%)
↓-20.0%prior 85
Dark - roadway lighted50 (11.5%)
↑2.0%prior 49
Dawn16 (3.7%)
↑60.0%prior 10
Dusk15 (3.5%)
↑25.0%prior 12

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry310 (71.6%)
↓-1.0%prior 313
Wet62 (14.3%)
↓-1.6%prior 63
Ice/frost24 (5.5%)
↑14.3%prior 21
Snow19 (4.4%)
↓-45.7%prior 35
Gravel9 (2.1%)
↓-43.8%prior 16
Slush7 (1.6%)
Other (explain in narrative)2 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes saw some shifts between periods. Chevrolet vehicles (combining 'CHEV' and 'CHEVROLET') were involved in 184 crashes in 2016, up from 166 in the prior year, making it the most frequent make. The age distribution of persons involved also changed, with a decrease in the 16-20 age group (from 173 to 122 persons) and an increase in the 26-34 age group (from 164 to 176 persons).

Top Vehicle Makes (809 vehicles)

1
FORD134 (16.6%)
↓-22.1%prior 172
2
CHEV92 (11.4%)
↓-13.2%prior 106
3
CHEVROLET92 (11.4%)
↑53.3%prior 60
4
DODGE39 (4.8%)
↑69.6%prior 23
5
TOYT32 (4%)
↓-27.3%prior 44
6
GMC32 (4%)
↑14.3%prior 28
7
DODG31 (3.8%)
↓-6.1%prior 33
8
TOYOTA30 (3.7%)
↑7.1%prior 28
9
JEEP19 (2.3%)
↓-40.6%prior 32
10
CHRY18 (2.2%)
↑5.9%prior 17

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · Vehicle unit records

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

Sex Distribution (621 persons with recorded sex)

Male352 (56.7%)
↓-22.8%prior 456
Female269 (43.3%)
↓-15.1%prior 317

Source: Iowa Crash Data · ArcGIS Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 510
  • Total persons involved: 938
  • Total vehicles involved: 809

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: 2016." Published September 9, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2016-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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