Yearly Traffic Safety Analysis

1,608 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Story County, total traffic crashes decreased by 4.7%, from 1,687 in 2015 to 1,608 in 2016. Despite this overall decline in collisions, the number of crashes resulting in serious injuries increased from 22 to 28, a rise of over 27%. Fatalities saw a slight decrease from 5 in the prior year to 4 in the current year.

1,608

▼ -4.7%was 1,687

Total Crash Events

4

▼ -20.0%was 5

Persons Killed

434

▼ -2.7%was 446

Persons Injured

4

▼ -20.0%was 5

Fatal Crash Events

Note: "Persons Killed" (4) 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

Crash data for Story County indicates a modest downward trend year-over-year. The total number of crashes fell by 79 incidents, from 1,687 in 2015 to 1,608 in 2016. This was accompanied by slight decreases in total fatalities (from 5 to 4) and total injuries (from 446 to 434).

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 4→0.0%

0

Other Killed

Prior: 0→0.0%

24

Pedestrians Injured

Prior: 15↑60.0%

25

Cyclists Injured

Prior: 21↑19.0%

383

Motorists Injured

Prior: 408↓-6.1%

2

Other Injured

Prior: 2→0.0%

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 remained consistent between 2015 and 2016. Friday was the peak day for crashes in both years, and 5 p.m. was the peak hour. However, the volume of incidents during these peak times was lower in 2016, with Friday crashes dropping from 309 to 286 and crashes during the 5 p.m. hour decreasing from 177 to 151.

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 total crashes decreased, the severity profile shifted slightly. The fatal crash rate saw a minor reduction from 0.3% to 0.2% of all crashes. In contrast, crashes resulting in serious injuries increased, rising from 22 incidents (1.3% of total) in 2015 to 28 incidents (1.7% of total) in 2016. The number of minor injury crashes was unchanged at 118 for both years.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.2%
↓-20.0%prior 5
Serious Injury28serious injury crashes1.7%
↑27.3%prior 22
Minor Injury118minor injury crashes7.3%
→0.0%prior 118
Possible Injury216possible injury crashes13.4%
↓-6.1%prior 230
No Injury1,242no injury crashes77.2%
↓-5.3%prior 1,312

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 shifted between the two periods. While "Followed too close" remained the top factor and its count increased from 253 to 271, other major factors saw significant changes. Crashes attributed to an "Animal" decreased in count by 23.7% (from 207 to 158), and those related to "FTYROW: Making left turn" fell by 27.3% (from 161 to 117).

Officer-Reported Primary Contributing Cause

Followed too close271 (16.9%)↑7.1%prior 253
Animal158 (9.8%)↓-23.7%prior 207
Driving too fast for conditions144 (9%)↓-8.3%prior 157
FTYROW: Making left turn117 (7.3%)↓-27.3%prior 161
Other (explain in narrative): Other99 (6.2%)↓-2.9%prior 102
FTYROW: From stop sign86 (5.3%)↓-2.3%prior 88
Ran off road - left52 (3.2%)↓-22.4%prior 67
Improper or erratic lane changing48 (3%)↓-4.0%prior 50
Lost Control48 (3%)↓-7.7%prior 52
Ran off road - straight46 (2.9%)↑12.2%prior 41

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 was largely stable year-over-year. The proportion of crashes occurring in daylight increased slightly from 63.9% in 2015 to 67.5% in 2016. Similarly, crashes on dry road surfaces made up a larger share of the total in 2016 (66.7%) compared to 2015 (61.9%), while the share of crashes on wet roads decreased.

Weather

Clear900 (61.7%)
↓-6.2%prior 959
Cloudy329 (22.5%)
↑15.8%prior 284
Rain86 (5.9%)
↓-21.1%prior 109
Snow84 (5.8%)
↓-12.5%prior 96
Freezing rain/drizzle30 (2.1%)
↑66.7%prior 18
Blowing Snow19 (1.3%)
↑18.8%prior 16
Sleet, hail4 (0.3%)
Severe Winds3 (0.2%)
Fog, smoke, smog3 (0.2%)
↓-78.6%prior 14
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

Daylight1,085 (74.2%)
↑0.6%prior 1,079
Dark - roadway lighted201 (13.7%)
↓-18.6%prior 247
Dark - roadway not lighted107 (7.3%)
↓-8.5%prior 117
Dusk37 (2.5%)
↑19.4%prior 31
Dawn32 (2.2%)
↑45.5%prior 22
Dark - unknown roadway lighting1 (0.1%)
↓-80.0%prior 5

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

Road Surface

Dry1,072 (73.3%)
↑2.7%prior 1,044
Wet164 (11.2%)
↓-13.7%prior 190
Ice/frost99 (6.8%)
↑37.5%prior 72
Snow81 (5.5%)
↓-46.7%prior 152
Slush30 (2.1%)
↑20.0%prior 25
Gravel13 (0.9%)
↓-7.1%prior 14
Other (explain in narrative)3 (0.2%)
Mud, dirt1 (0.1%)

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

Vehicles & Demographics

Vehicle and person demographics showed little change year-over-year. The top vehicle makes involved in crashes were consistent, with Chevrolet and Ford leading in both periods. The age distribution of persons involved also remained stable; the 21-25 age group was the largest cohort in both 2016 and 2015, accounting for 20.1% and 20.2% of all persons, respectively.

Top Vehicle Makes (2,886 vehicles)

1
FORD419 (14.5%)
↓-6.7%prior 449
2
CHEV273 (9.5%)
↓-26.8%prior 373
3
CHEVROLET262 (9.1%)
↑49.7%prior 175
4
TOYT180 (6.2%)
↓-18.9%prior 222
5
TOYOTA129 (4.5%)
↑14.2%prior 113
6
HOND110 (3.8%)
↓-20.9%prior 139
7
DODG100 (3.5%)
↓-14.5%prior 117
8
HONDA99 (3.4%)
↑43.5%prior 69
9
JEEP85 (2.9%)
↓-7.6%prior 92
10
DODGE79 (2.7%)
↑29.5%prior 61

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

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

Sex Distribution (2,335 persons with recorded sex)

Male1,303 (55.8%)
↓-13.9%prior 1,514
Female1,032 (44.2%)
↓-14.1%prior 1,202

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: 1,608
  • Total persons involved: 3,166
  • Total vehicles involved: 2,886

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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