Monthly Traffic Safety Analysis

6,060 CRASHES IN
IOWA, IA
DECEMBER 2016

All metrics benchmarked againstDecember 2015

In December 2016, Iowa recorded 6,060 traffic crashes, a 13.2% increase from the 5,352 crashes reported in December 2015. This rise was accompanied by a 54.2% increase in fatalities, from 24 to 37. The most notable year-over-year shift was a significant surge in crashes attributed to 'Driving too fast for conditions,' which increased in count by 86.9% and became the leading contributing factor.

6,060

13.2%was 5,352

Total Crash Events

37

54.2%was 24

Persons Killed

1,681

7.1%was 1,570

Persons Injured

28

27.3%was 22

Fatal Crash Events

Note: "Persons Killed" (37) counts individual fatalities across all crash events. "Fatal" in the severity table below (28) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

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

Trend Summary

Traffic collisions in Iowa showed a clear upward trend in December 2016 compared to the same month in the prior year. Total crashes rose by 13.2%, from 5,352 to 6,060. This increase extended to crash severity, with total fatalities climbing 54.2% from 24 to 37, and total injuries increasing by 7.1% from 1,570 to 1,681.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 3-33.3%

0

Cyclists Killed

Prior: 00.0%

35

Motorists Killed

Prior: 2166.7%

0

Other Killed

Prior: 00.0%

34

Pedestrians Injured

Prior: 319.7%

5

Cyclists Injured

Prior: 7-28.6%

1,639

Motorists Injured

Prior: 1,5317.1%

3

Other Injured

Prior: 1200.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-12-01 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. The peak day for collisions moved from Thursday (1,061 crashes) in December 2015 to Friday (1,233 crashes) in December 2016. While the peak hour for crashes remained the 5 p.m. slot in both years, the number of crashes on Fridays and Saturdays increased substantially in 2016, with Friday incidents alone rising by 78.9% from 689 to 1,233.

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

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

Crash Severity Breakdown

Crash severity worsened in December 2016 compared to the previous year. The number of fatal crashes increased from 22 to 28, and the total number of fatalities rose from 24 to 37. The proportion of crashes resulting in any level of injury (possible, minor, or serious) remained stable at approximately 23% for both periods. However, the absolute count of injury-related crashes grew from 1,259 in 2015 to 1,397 in 2016.

Severity is per crash event (most severe injury). 28 fatal crash events resulted in 37 persons killed.

Outcome by Severity (Crash Events)

Fatal28fatal crashes0.5%
27.3%prior 22
Serious Injury78serious injury crashes1.3%
9.9%prior 71
Minor Injury393minor injury crashes6.5%
10.4%prior 356
Possible Injury926possible injury crashes15.3%
11.3%prior 832
No Injury4,635no injury crashes76.5%
13.9%prior 4,071

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

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-12-01 to 2016-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes changed notably year-over-year. 'Driving too fast for conditions' surged from the third-ranked cause to the top position, with its count increasing by 86.9% from 475 to 888 incidents. Conversely, crashes involving an 'Animal,' the top factor in 2015, saw their count decrease by 22.4% from 800 to 621. 'Ran off road - left' also saw a substantial 64.7% increase in count, rising from 343 to 565 incidents.

Officer-Reported Primary Contributing Cause

Driving too fast for conditions888 (14.7%)86.9%prior 475
Animal621 (10.2%)-22.4%prior 800
Ran off road - left565 (9.3%)64.7%prior 343
Followed too close523 (8.6%)9.4%prior 478
Lost Control428 (7.1%)16.6%prior 367
Other (explain in narrative): Other288 (4.8%)-6.8%prior 309
Ran off road - straight275 (4.5%)9.6%prior 251
FTYROW: From stop sign254 (4.2%)-4.5%prior 266
FTYROW: Making left turn218 (3.6%)-7.6%prior 236
Ran Traffic Signal170 (2.8%)6.3%prior 160

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

Road & Environmental Conditions

Adverse road and weather conditions were more prevalent in crashes during December 2016 compared to December 2015. The number of crashes occurring on roads with snow, ice, or slush more than doubled, increasing from 1,194 to 2,361. Similarly, crashes reported during snowy weather rose from 658 to 1,047. The proportion of crashes occurring in daylight increased from 47.9% to 54.2%, while the share of crashes in dark conditions decreased.

Weather

Clear2,603 (47.3%)
25.7%prior 2,070
Cloudy1,278 (23.2%)
0.6%prior 1,270
Snow1,047 (19.0%)
59.1%prior 658
Freezing rain/drizzle276 (5.0%)
84.0%prior 150
Blowing Snow126 (2.3%)
223.1%prior 39
Rain74 (1.3%)
-79.7%prior 364
Fog, smoke, smog65 (1.2%)
-29.3%prior 92
Sleet, hail13 (0.2%)
-51.9%prior 27
Severe Winds12 (0.2%)
140.0%prior 5
Other (explain in narrative)11 (0.2%)

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

Lighting

Daylight3,283 (59.4%)
28.1%prior 2,563
Dark - roadway lighted1,161 (21.0%)
4.3%prior 1,113
Dark - roadway not lighted749 (13.6%)
5.8%prior 708
Dusk189 (3.4%)
21.2%prior 156
Dawn114 (2.1%)
-15.6%prior 135
Dark - unknown roadway lighting27 (0.5%)
28.6%prior 21

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

Road Surface

Dry2,428 (44.0%)
-4.7%prior 2,547
Snow1,250 (22.7%)
118.2%prior 573
Ice/frost892 (16.2%)
75.2%prior 509
Wet660 (12.0%)
-25.5%prior 886
Slush219 (4.0%)
95.5%prior 112
Gravel46 (0.8%)
9.5%prior 42
Other (explain in narrative)11 (0.2%)
10.0%prior 10
Mud, dirt3 (0.1%)
-40.0%prior 5
Sand3 (0.1%)

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

Vehicles & Demographics

Top Vehicle Makes (10,285 vehicles)

1
FORD1,735 (16.9%)
16.9%prior 1,484
2
CHEV1,091 (10.6%)
-8.5%prior 1,192
3
CHEVROLET1,069 (10.4%)
42.0%prior 753
4
TOYT413 (4%)
8.4%prior 381
5
DODGE382 (3.7%)
36.4%prior 280
6
JEEP332 (3.2%)
25.8%prior 264
7
GMC328 (3.2%)
10.1%prior 298
8
TOYOTA323 (3.1%)
26.7%prior 255
9
DODG319 (3.1%)
-17.8%prior 388
10
HOND281 (2.7%)
17.1%prior 240

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

1,159 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (7,757 persons with recorded sex)

Male4,483 (57.8%)
1.4%prior 4,419
Female3,274 (42.2%)
-7.1%prior 3,524

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

Data Coverage

  • Reporting period: 2016-12-01 through 2016-12-31 (31 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 6,060
  • Total persons involved: 10,745
  • Total vehicles involved: 10,285

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