Yearly Traffic Safety Analysis

1,165 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Dallas County, total crashes increased by 7.9% from 1,080 in 2015 to 1,165 in 2016. While total crashes rose, the most notable year-over-year shift was a 50% increase in fatalities, which grew from 4 to 6 persons killed. A significant change in collision dynamics was also observed, with crashes attributed to 'Followed too close' increasing by 34.9% in count.

1,165

▲ 7.9%was 1,080

Total Crash Events

6

▲ 50.0%was 4

Persons Killed

349

▲ 4.8%was 333

Persons Injured

3

▼ -25.0%was 4

Fatal Crash Events

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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

Traffic crash data for Dallas County indicates a rising trend year-over-year. Total reported crashes increased from 1,080 in 2015 to 1,165 in 2016, an increase of 7.9%. This was accompanied by a 4.8% rise in total injuries (from 333 to 349) and a 50% increase in fatalities (from 4 to 6).

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 1→0.0%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 3↑66.7%

1

Pedestrians Injured

Prior: 5↓-80.0%

3

Cyclists Injured

Prior: 6↓-50.0%

345

Motorists Injured

Prior: 322↑7.1%

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 distribution of crashes shifted between the two periods. The peak day for collisions moved from Wednesday in 2015, with 181 incidents, to Friday in 2016, which saw 202 crashes. The 5 p.m. hour remained the peak time for crashes in both years, although the number of incidents during this hour decreased from 125 to 113.

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 the number of fatalities increased from 4 to 6, the number of distinct fatal crashes decreased from 4 to 3, resulting in a lower fatal crash rate of 0.26% compared to 0.37% in the prior year. The overall proportion of crashes involving any level of injury (Serious, Minor, or Possible) increased from 22.3% in 2015 to 23.9% in 2016. This was driven by an increase in the share of crashes resulting in 'Possible Injury,' which rose from 12.3% to 13.9% of all incidents.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 6 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.3%
↓-25.0%prior 4
Serious Injury20serious injury crashes1.7%
↓-13.0%prior 23
Minor Injury96minor injury crashes8.2%
↑12.9%prior 85
Possible Injury162possible injury crashes13.9%
↑21.8%prior 133
No Injury884no injury crashes75.9%
↑5.9%prior 835

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 ranking of top contributing factors shifted year-over-year. 'Followed too close' became the primary factor in 2016 with 205 incidents, a 34.9% increase in count from 152 in the previous year. Crashes involving an 'Animal', the top factor in 2015 with 178 incidents, decreased by 10.1% to 160 incidents in 2016. Notably, crashes attributed to a driver losing control saw a 70.6% increase in count, rising from 51 to 87 incidents.

Officer-Reported Primary Contributing Cause

Followed too close205 (17.6%)↑34.9%prior 152
Animal160 (13.7%)↓-10.1%prior 178
Lost Control87 (7.5%)↑70.6%prior 51
Driving too fast for conditions81 (7%)↓-4.7%prior 85
Other (explain in narrative): Other64 (5.5%)↑6.7%prior 60
FTYROW: From stop sign57 (4.9%)↓-10.9%prior 64
FTYROW: Making left turn53 (4.5%)↑17.8%prior 45
Ran off road - left49 (4.2%)↑11.4%prior 44
Ran off road - straight47 (4%)↑9.3%prior 43
Driver Distraction: Other interior distraction38 (3.3%)↑58.3%prior 24

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

Road & Environmental Conditions

Crashes in both years predominantly occurred during daylight hours and on dry road surfaces. The proportion of crashes on dry roads increased from 61.8% in 2015 to 66.1% in 2016. A notable shift was observed in crashes on icy or frosty roads, which increased in count from 45 to 61 incidents year-over-year. Crashes occurring in rainy conditions decreased from 79 to 50 incidents.

Weather

Clear656 (62.8%)
↑16.9%prior 561
Cloudy231 (22.1%)
↑12.7%prior 205
Snow63 (6.0%)
↓-16.0%prior 75
Rain50 (4.8%)
↓-36.7%prior 79
Freezing rain/drizzle18 (1.7%)
↑125.0%prior 8
Blowing Snow14 (1.3%)
↑133.3%prior 6
Fog, smoke, smog5 (0.5%)
↓-37.5%prior 8
Sleet, hail4 (0.4%)
Severe Winds3 (0.3%)
Other (explain in narrative)1 (0.1%)

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

Lighting

Daylight708 (67.6%)
↑5.5%prior 671
Dark - roadway not lighted138 (13.2%)
↑24.3%prior 111
Dark - roadway lighted126 (12.0%)
↑12.5%prior 112
Dusk47 (4.5%)
↑62.1%prior 29
Dawn26 (2.5%)
↑36.8%prior 19
Dark - unknown roadway lighting3 (0.3%)

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

Road Surface

Dry770 (73.5%)
↑15.4%prior 667
Wet125 (11.9%)
→0.0%prior 125
Ice/frost61 (5.8%)
↑35.6%prior 45
Snow52 (5.0%)
↓-35.0%prior 80
Gravel21 (2.0%)
↑31.3%prior 16
Slush14 (1.3%)
↑180.0%prior 5
Mud, dirt3 (0.3%)
Other (explain in narrative)1 (0.1%)

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

Vehicles & Demographics

The most common vehicle makes involved in crashes, primarily Chevrolet and Ford, remained consistent across both years. Analysis of persons involved shows a demographic shift, with the 16-20 age group increasing from 333 individuals in 2015 to 362 in 2016. Conversely, the number of individuals in the 26-34 age group involved in crashes decreased from 411 to 376.

Top Vehicle Makes (2,004 vehicles)

1
FORD327 (16.3%)
↑6.2%prior 308
2
CHEV199 (9.9%)
↓-2.0%prior 203
3
CHEVROLET154 (7.7%)
↑16.7%prior 132
4
TOYOTA94 (4.7%)
↑108.9%prior 45
5
TOYT85 (4.2%)
→0.0%prior 85
6
DODG78 (3.9%)
↓-2.5%prior 80
7
JEEP69 (3.4%)
↑19.0%prior 58
8
DODGE65 (3.2%)
↑30.0%prior 50
9
HOND64 (3.2%)
↓-15.8%prior 76
10
NISS63 (3.1%)
↑16.7%prior 54

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

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

Sex Distribution (1,663 persons with recorded sex)

Male891 (53.6%)
↓-5.4%prior 942
Female772 (46.4%)
→0.0%prior 772

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: 1,165
  • Total persons involved: 2,206
  • Total vehicles involved: 2,004

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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