Yearly Traffic Safety Analysis

404 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Cedar County recorded 404 total traffic crashes, a 6.7% decrease from the 433 crashes reported in 2015. While total crashes declined, the most significant year-over-year change was a reduction in fatalities, which fell from five in 2015 to two in 2016. Conversely, the total number of injuries increased by 10%, from 100 to 110.

404

▼ -6.7%was 433

Total Crash Events

2

▼ -60.0%was 5

Persons Killed

110

▲ 10.0%was 100

Persons Injured

2

▼ -50.0%was 4

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 · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crash trends in Cedar County show a modest decline year-over-year. Total crashes decreased by 29 incidents, from 433 in 2015 to 404 in 2016. While the number of fatalities was lower, the number of persons injured in collisions increased from 100 to 110 over the same period.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 5↓-60.0%

1

Cyclists Injured

Prior: 3↓-66.7%

109

Motorists Injured

Prior: 94↑16.0%

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 between the two periods. In 2016, the peak day for crashes was Sunday with 76 incidents, a change from 2015 when Tuesday was the peak day with 75 crashes. The peak hour for collisions also shifted slightly, moving from the 4 p.m. hour (33 crashes) in 2015 to the 5 p.m. hour (35 crashes) in 2016.

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

The severity of crashes decreased year-over-year, with the fatal crash rate falling from 0.9% of all crashes in 2015 to 0.5% in 2016. Crashes involving serious injuries also declined, from seven incidents to four. However, the proportion of crashes resulting in minor injuries rose from 6.9% to 8.4%, and possible injury crashes increased from 10.4% to 12.1% of the total.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.5%
↓-50.0%prior 4
Serious Injury4serious injury crashes1%
↓-42.9%prior 7
Minor Injury34minor injury crashes8.4%
↑13.3%prior 30
Possible Injury49possible injury crashes12.1%
↑8.9%prior 45
No Injury315no injury crashes78%
↓-9.2%prior 347

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

The primary contributing factors cited in crashes remained consistent across both years, with 'Animal' being the most common factor in both 2016 (96 crashes) and 2015 (101 crashes). The count of crashes attributed to the top three factors—Animal, Ran off road - straight, and Lost Control—all decreased from 2015 to 2016. Incidents involving an animal fell by a count of 5, while crashes from running off the road straight decreased from a count of 63 to 52.

Officer-Reported Primary Contributing Cause

Animal96 (23.8%)↓-5.0%prior 101
Ran off road - straight52 (12.9%)↓-17.5%prior 63
Lost Control36 (8.9%)↓-7.7%prior 39
Ran off road - left30 (7.4%)↑114.3%prior 14
Driving too fast for conditions28 (6.9%)↓-15.2%prior 33
Followed too close26 (6.4%)↑13.0%prior 23
FTYROW: From stop sign13 (3.2%)↓-7.1%prior 14
Driver Distraction: Inattentive/lost in thought12 (3%)↑9.1%prior 11
Driver Distraction: Other interior distraction10 (2.5%)↓-23.1%prior 13
Other (explain in narrative): No improper action9 (2.2%)↑50.0%prior 6

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

Road & Environmental Conditions

There was a notable decrease in crashes occurring under adverse conditions in 2016 compared to the prior year. Collisions on roads with snow, ice, or slush dropped from a combined 82 incidents in 2015 to 46 in 2016. Similarly, crashes in the dark on unlit roadways decreased from 88 to 67, while crashes in daylight remained relatively stable with 196 incidents in 2016 versus 205 in 2015.

Weather

Clear179 (58.5%)
↓-10.5%prior 200
Cloudy68 (22.2%)
↑58.1%prior 43
Snow25 (8.2%)
↓-40.5%prior 42
Rain21 (6.9%)
↑23.5%prior 17
Freezing rain/drizzle5 (1.6%)
↓-16.7%prior 6
Blowing Snow4 (1.3%)
↓-66.7%prior 12
Fog, smoke, smog3 (1.0%)
↓-57.1%prior 7
Sleet, hail1 (0.3%)

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

Lighting

Daylight196 (64.1%)
↓-4.4%prior 205
Dark - roadway not lighted67 (21.9%)
↓-23.9%prior 88
Dark - roadway lighted18 (5.9%)
↑5.9%prior 17
Dusk12 (3.9%)
↑50.0%prior 8
Dawn7 (2.3%)
→0.0%prior 7
Dark - unknown roadway lighting6 (2.0%)
↑20.0%prior 5

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

Road Surface

Dry195 (63.9%)
↑0.5%prior 194
Wet42 (13.8%)
↑82.6%prior 23
Snow28 (9.2%)
↓-37.8%prior 45
Gravel21 (6.9%)
↓-32.3%prior 31
Ice/frost16 (5.2%)
↓-51.5%prior 33
Slush2 (0.7%)
Other (explain in narrative)1 (0.3%)

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

Vehicles & Demographics

Ford and Chevrolet were the top two vehicle makes involved in crashes in both periods, though the count of Fords involved decreased from 122 in 2015 to 100 in 2016. The age demographics of persons involved in crashes also shifted; there was a notable decrease in individuals aged 16-20 (from 105 to 74) and an increase in those aged 26-34 (from 100 to 114) and 45-54 (from 92 to 107).

Top Vehicle Makes (589 vehicles)

1
FORD100 (17%)
↓-18.0%prior 122
2
CHEVROLET73 (12.4%)
↑12.3%prior 65
3
CHEV45 (7.6%)
↓-8.2%prior 49
4
TOYOTA25 (4.2%)
↑4.2%prior 24
5
HONDA18 (3.1%)
↑12.5%prior 16
6
FREIGHTLINER18 (3.1%)
↓-33.3%prior 27
7
NISSAN14 (2.4%)
↑27.3%prior 11
8
GMC14 (2.4%)
↓-22.2%prior 18
9
DODGE14 (2.4%)
↓-26.3%prior 19
10
JEEP14 (2.4%)
↑40.0%prior 10

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

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

Sex Distribution (432 persons with recorded sex)

Male275 (63.7%)
↓-15.4%prior 325
Female157 (36.3%)
↓-18.2%prior 192

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: 404
  • Total persons involved: 655
  • Total vehicles involved: 589

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

ThatCarHitMe.com · An Injuria.ai Company