Yearly Traffic Safety Analysis

277 CRASHES IN
IOWA, IA
2016

All metrics benchmarked against2015

In Hardin County, total crashes decreased from 289 in 2015 to 277 in 2016, a 4.2% reduction. Despite the overall drop in collisions, the number of people injured rose from 80 to 87, and fatalities increased from 3 to 4.

277

▼ -4.2%was 289

Total Crash Events

4

▲ 33.3%was 3

Persons Killed

87

▲ 8.8%was 80

Persons Injured

3

Fatal Crash Events

Note: "Persons Killed" (4) 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

Overall traffic crashes in Hardin County saw a slight decline in 2016, falling by 12 incidents from 289 to 277 compared to the previous year. However, this decrease in total crashes was accompanied by an increase in severity, with total injuries rising from 80 to 87 and fatalities increasing from 3 to 4 year-over-year.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 1→0.0%

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 2↑50.0%

1

Pedestrians Injured

Prior: 1→0.0%

1

Cyclists Injured

Prior: 0→%

85

Motorists Injured

Prior: 79↑7.6%

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 2015 and 2016. The most frequent day for crashes moved from Thursday (48 crashes) in 2015 to Monday (54 crashes) in 2016. The peak hour for collisions also changed significantly, moving from the 10 p.m. hour (21 crashes) in the prior year to the 7 a.m. morning commute hour (19 crashes) in the current period.

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 number of fatal crashes remained unchanged at 3 in both 2015 and 2016, though the fatal crash rate saw a marginal increase from 1.04% to 1.08% due to fewer total crashes. The number of serious injury crashes increased from 7 to 8, while minor injury crashes decreased from 20 to 17. Overall, the proportion of crashes resulting in no injuries decreased slightly from 75.8% in 2015 to 75.1% in 2016.

Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal3fatal crashes1.1%
→0.0%prior 3
Serious Injury8serious injury crashes2.9%
↑14.3%prior 7
Minor Injury17minor injury crashes6.1%
↓-15.0%prior 20
Possible Injury41possible injury crashes14.8%
↑2.5%prior 40
No Injury208no injury crashes75.1%
↓-5.0%prior 219

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

Collisions with animals remained the top contributing factor in both periods, though the count decreased by 10% from 90 crashes in 2015 to 81 in 2016. 'Lost Control' also remained the second-ranked factor, with its count falling from 32 to 28 incidents. 'Driving too fast for conditions' was the third most common factor in both years, with a slight increase in count from 16 to 17 crashes. The top three contributing factors did not change in rank between the two periods.

Officer-Reported Primary Contributing Cause

Animal81 (29.2%)↓-10.0%prior 90
Lost Control28 (10.1%)↓-12.5%prior 32
Driving too fast for conditions17 (6.1%)↑6.3%prior 16
Ran off road - straight17 (6.1%)↑70.0%prior 10
Ran off road - left15 (5.4%)↑15.4%prior 13
Followed too close12 (4.3%)↑33.3%prior 9
FTYROW: From stop sign11 (4%)↑10.0%prior 10
Other (explain in narrative): Other10 (3.6%)↓-9.1%prior 11
FTYROW: Making left turn8 (2.9%)↑14.3%prior 7
Ran Stop Sign7 (2.5%)↑40.0%prior 5

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

Road & Environmental Conditions

Crash conditions showed some year-over-year shifts, particularly related to weather. Crashes occurring in snowy conditions increased from 9 in 2015 to 17 in 2016, while rain-related crashes decreased from 15 to 5. Regarding lighting, collisions in daylight decreased from 142 to 128, whereas crashes on lighted dark roadways increased from 11 to 19. The number of crashes on dry road surfaces remained nearly identical, with 146 in 2015 and 147 in 2016.

Weather

Clear133 (61.9%)
↑3.9%prior 128
Cloudy51 (23.7%)
↓-5.6%prior 54
Snow17 (7.9%)
↑88.9%prior 9
Rain5 (2.3%)
↓-66.7%prior 15
Blowing Snow3 (1.4%)
Fog, smoke, smog3 (1.4%)
Freezing rain/drizzle3 (1.4%)

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

Lighting

Daylight128 (59.3%)
↓-9.9%prior 142
Dark - roadway not lighted51 (23.6%)
→0.0%prior 51
Dark - roadway lighted19 (8.8%)
↑72.7%prior 11
Dawn11 (5.1%)
↑37.5%prior 8
Dusk7 (3.2%)
↑40.0%prior 5

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

Road Surface

Dry147 (67.7%)
↑0.7%prior 146
Snow22 (10.1%)
↑29.4%prior 17
Wet19 (8.8%)
↓-17.4%prior 23
Ice/frost13 (6.0%)
↓-31.6%prior 19
Gravel11 (5.1%)
→0.0%prior 11
Other (explain in narrative)3 (1.4%)
Slush1 (0.5%)
Mud, dirt1 (0.5%)

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

Vehicles & Demographics

The top three vehicle makes involved in crashes remained Chevrolet, Ford, and Dodge in both periods, with Chevrolet accounting for 104 vehicles in 2016 compared to 106 in 2015. An analysis of persons involved shows the 16-20 age group was the most represented in both years, though their count decreased from 92 in 2015 to 80 in 2016. The number of individuals in the 55-64 age group involved in crashes saw a notable decrease, falling from 81 in the prior period to 50 in the current period.

Top Vehicle Makes (386 vehicles)

1
CHEVROLET58 (15%)
↑34.9%prior 43
2
FORD54 (14%)
↓-19.4%prior 67
3
CHEV46 (11.9%)
↓-27.0%prior 63
4
DODGE19 (4.9%)
↑11.8%prior 17
5
DODG14 (3.6%)
↓-33.3%prior 21
6
JEEP13 (3.4%)
7
PONT13 (3.4%)
↑8.3%prior 12
8
TOYOTA12 (3.1%)
↑33.3%prior 9
9
TOYT12 (3.1%)
↑20.0%prior 10
10
HONDA10 (2.6%)
↑66.7%prior 6

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

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

Sex Distribution (294 persons with recorded sex)

Male187 (63.6%)
↓-25.5%prior 251
Female107 (36.4%)
↓-21.3%prior 136

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: 277
  • Total persons involved: 433
  • Total vehicles involved: 386

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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