Yearly Traffic Safety Analysis

4,081 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In 2016, Scott County recorded 4,081 vehicle crashes, a 1.6% increase from the 4,018 crashes documented in 2015. While total crashes remained relatively stable and fatalities decreased from 21 to 19, the number of people injured in these incidents rose by 14.2%, from 1,369 in 2015 to 1,563 in 2016. The most significant shift in crash circumstances was a notable reduction in collisions occurring on snowy or icy roads.

4,081

▲ 1.6%was 4,018

Total Crash Events

19

▼ -9.5%was 21

Persons Killed

1,563

▲ 14.2%was 1,369

Persons Injured

16

▼ -11.1%was 18

Fatal Crash Events

Note: "Persons Killed" (19) counts individual fatalities across all crash events. "Fatal" in the severity table below (16) 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 volume in Scott County saw a slight increase year-over-year. The total number of crashes rose by 1.6%, from 4,018 in 2015 to 4,081 in 2016. This represents a net increase of 63 reported crashes.

Vulnerable Road User Casualties

3

Pedestrians Killed

Prior: 3→0.0%

0

Cyclists Killed

Prior: 0→0.0%

16

Motorists Killed

Prior: 18↓-11.1%

0

Other Killed

Prior: 0→0.0%

47

Pedestrians Injured

Prior: 35↑34.3%

26

Cyclists Injured

Prior: 24↑8.3%

1,486

Motorists Injured

Prior: 1,308↑13.6%

4

Other Injured

Prior: 2↑100.0%

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 in Scott County remained largely consistent, with Friday being the peak day for crashes in both 2016 (684 crashes) and 2015 (665 crashes). However, the single busiest hour for collisions shifted earlier in the afternoon, from the 5 PM hour in 2015 (377 crashes) to the 3 PM hour in 2016 (399 crashes). The afternoon commute period consistently saw the highest frequency of crashes in both years.

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 showed a mixed trend between 2015 and 2016. The number of fatal crashes decreased from 18 to 16, and the corresponding fatal crash rate per 100 crashes dropped from 0.45 to 0.39. While the share of serious injury crashes also saw a minor decline from 1.3% to 1.2%, the proportion of crashes resulting in minor injuries increased from 6.9% in 2015 to 8.2% in 2016.

Severity is per crash event (most severe injury). 16 fatal crash events resulted in 19 persons killed.

Outcome by Severity (Crash Events)

Fatal16fatal crashes0.4%
↓-11.1%prior 18
Serious Injury49serious injury crashes1.2%
↓-9.3%prior 54
Minor Injury335minor injury crashes8.2%
↑20.5%prior 278
Possible Injury888possible injury crashes21.8%
↑4.8%prior 847
No Injury2,793no injury crashes68.4%
↓-1.0%prior 2,821

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 leading contributing factors to crashes in Scott County were consistent year-over-year, though their counts shifted. 'Followed too close' remained the top factor, with incidents increasing from 602 to 686. 'Ran off road - left' held its position as the second-most common factor, rising from 408 to 437 incidents. Crashes attributed to 'Failure to yield right of way from a stop sign' saw a notable increase in count, rising from 172 in 2015 to 208 in 2016, moving it into the top five reported factors.

Officer-Reported Primary Contributing Cause

Followed too close686 (16.8%)↑14.0%prior 602
Ran off road - left437 (10.7%)↑7.1%prior 408
FTYROW: Making left turn267 (6.5%)↓-1.5%prior 271
Ran Traffic Signal220 (5.4%)→0.0%prior 220
FTYROW: From stop sign208 (5.1%)↑20.9%prior 172
Animal199 (4.9%)↓-0.5%prior 200
Other (explain in narrative): Other196 (4.8%)↓-24.9%prior 261
Driving too fast for conditions155 (3.8%)↓-3.1%prior 160
Operating vehicle in an reckless, erratic, careless, negligent manner147 (3.6%)↑8.1%prior 136
Lost Control127 (3.1%)↓-6.6%prior 136

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 majority of crashes in both periods occurred in clear weather and daylight on dry roads. In 2016, the proportion of crashes on dry road surfaces increased to 76.1% from 70.4% in 2015. Correspondingly, there was a notable decrease in collisions occurring on adverse road surfaces; the number of crashes on snow, ice, or slush-covered roads fell from a combined 392 in 2015 to 234 in 2016.

Weather

Clear2,436 (62.5%)
↑0.5%prior 2,425
Cloudy1,040 (26.7%)
↑21.4%prior 857
Rain233 (6.0%)
↓-25.3%prior 312
Snow129 (3.3%)
↓-11.0%prior 145
Freezing rain/drizzle20 (0.5%)
↓-50.0%prior 40
Blowing Snow15 (0.4%)
↓-21.1%prior 19
Fog, smoke, smog15 (0.4%)
↓-6.3%prior 16
Severe Winds6 (0.2%)
Sleet, hail4 (0.1%)
↓-20.0%prior 5
Other (explain in narrative)1 (0.0%)

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

Lighting

Daylight2,792 (71.5%)
↑3.3%prior 2,703
Dark - roadway lighted786 (20.1%)
↑0.6%prior 781
Dark - roadway not lighted202 (5.2%)
↓-8.2%prior 220
Dusk84 (2.2%)
↑33.3%prior 63
Dawn35 (0.9%)
↓-35.2%prior 54
Dark - unknown roadway lighting6 (0.2%)
↓-60.0%prior 15

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

Road Surface

Dry3,104 (79.6%)
↑9.7%prior 2,829
Wet536 (13.7%)
↓-8.2%prior 584
Snow144 (3.7%)
↓-25.8%prior 194
Ice/frost70 (1.8%)
↓-53.3%prior 150
Gravel22 (0.6%)
↑4.8%prior 21
Slush20 (0.5%)
↓-58.3%prior 48
Other (explain in narrative)1 (0.0%)
Oil1 (0.0%)
Water (standing or moving)1 (0.0%)
Mud, dirt1 (0.0%)

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

Vehicles & Demographics

The demographic profile of persons involved in crashes and the most common vehicle makes remained consistent year-over-year. The top makes involved in collisions were Ford, Chevrolet, and Toyota in both 2016 and 2015, with their rankings unchanged. The age distribution of individuals involved in crashes also showed little variation, with the 26-34 age group representing the largest cohort in both periods, accounting for 16.1% of persons in 2016 and 15.5% in 2015.

Top Vehicle Makes (7,774 vehicles)

1
FORD1,407 (18.1%)
↑1.4%prior 1,387
2
CHEVROLET767 (9.9%)
↑25.9%prior 609
3
CHEV564 (7.3%)
↓-12.4%prior 644
4
TOYT351 (4.5%)
↑4.5%prior 336
5
TOYOTA309 (4%)
↑16.2%prior 266
6
HOND279 (3.6%)
↑7.7%prior 259
7
HONDA265 (3.4%)
↑30.5%prior 203
8
DODGE241 (3.1%)
↑20.5%prior 200
9
NR238 (3.1%)
↓-8.5%prior 260
10
GMC200 (2.6%)
↓-7.4%prior 216

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

1,281 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (5,634 persons with recorded sex)

Male3,012 (53.5%)
↓-8.9%prior 3,307
Female2,622 (46.5%)
↓-10.3%prior 2,923

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: 4,081
  • Total persons involved: 8,786
  • Total vehicles involved: 7,774

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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