Yearly Traffic Safety Analysis

304 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Hardin County recorded 304 total crashes, an increase of 9.8% from the 277 crashes reported in 2016. Despite the rise in total incidents, the most significant change was a decrease in traffic fatalities, which fell from four in 2016 to zero in 2017.

304

▲ 9.7%was 277

Total Crash Events

0

▼ -100.0%was 4

Persons Killed

87

Persons Injured

0

▼ -100.0%was 3

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Hardin County increased by 9.8% from 2016 to 2017, rising from 277 to 304 incidents. While the total number of crashes grew, the number of resulting injuries remained unchanged at 87 for both years. Notably, there were no fatalities in 2017, a decrease from the four recorded in the prior year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 1↓-100.0%

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 3↓-100.0%

3

Pedestrians Injured

Prior: 1↑200.0%

1

Cyclists Injured

Prior: 1→0.0%

83

Motorists Injured

Prior: 85↓-2.4%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted between the two periods. In 2017, the peak day for crashes was Friday with 54 incidents, a shift from Monday which saw the same peak volume in 2016. The peak hour for crashes moved from 7 a.m. (19 crashes) in 2016 to 5 p.m. (29 crashes) in 2017, indicating a change from the morning to the evening commute as the most frequent time for incidents.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity saw a notable improvement year-over-year, with fatal crashes dropping from three in 2016 to zero in 2017. The number of serious injury crashes also decreased from eight to five. While the total number of injuries remained constant, the distribution shifted, with minor injury crashes increasing from 17 to 30, while possible injury crashes decreased from 41 to 35.

Outcome by Severity (Crash Events)

Serious Injury5serious injury crashes1.6%
↓-37.5%prior 8
Minor Injury30minor injury crashes9.9%
↑76.5%prior 17
Possible Injury35possible injury crashes11.5%
↓-14.6%prior 41
No Injury234no injury crashes77%
↑12.5%prior 208

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the top contributing factor in both years, increasing in count by 27.2% from 81 incidents in 2016 to 103 in 2017. 'Lost Control' was the second-most cited factor in both periods, though its count decreased from 28 to 25. Incidents involving 'Failure to Yield Right of Way from a stop sign' increased from 11 to 14, while crashes attributed to 'Followed too close' decreased from 12 to 8.

Officer-Reported Primary Contributing Cause

Animal103 (33.9%)↑27.2%prior 81
Lost Control25 (8.2%)↓-10.7%prior 28
Ran off road - straight20 (6.6%)↑17.6%prior 17
Ran off road - left19 (6.3%)↑26.7%prior 15
Other (explain in narrative): Other17 (5.6%)↑70.0%prior 10
Driving too fast for conditions15 (4.9%)↓-11.8%prior 17
FTYROW: From stop sign14 (4.6%)↑27.3%prior 11
Followed too close8 (2.6%)↓-33.3%prior 12
Driver Distraction: Other interior distraction7 (2.3%)↑16.7%prior 6
Improper Backing6 (2%)→0.0%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The majority of crashes in both 2017 (49.0%) and 2016 (48.0%) occurred in clear weather. There was a notable increase in crashes during rainy conditions, rising from 5 in 2016 to 14 in 2017. Conversely, crashes on snowy roads decreased from 22 to 15. Crashes in daylight conditions remained proportionally stable, accounting for 45.1% of incidents in 2017 compared to 46.2% in 2016.

Weather

Clear149 (68.3%)
↑12.0%prior 133
Cloudy35 (16.1%)
↓-31.4%prior 51
Rain14 (6.4%)
↑180.0%prior 5
Snow12 (5.5%)
↓-29.4%prior 17
Freezing rain/drizzle4 (1.8%)
Severe Winds2 (0.9%)
Fog, smoke, smog1 (0.5%)
Blowing Snow1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight137 (63.1%)
↑7.0%prior 128
Dark - roadway not lighted44 (20.3%)
↓-13.7%prior 51
Dark - roadway lighted17 (7.8%)
↓-10.5%prior 19
Dusk8 (3.7%)
↑14.3%prior 7
Dawn6 (2.8%)
↓-45.5%prior 11
Dark - unknown roadway lighting5 (2.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry149 (68.0%)
↑1.4%prior 147
Wet20 (9.1%)
↑5.3%prior 19
Gravel15 (6.8%)
↑36.4%prior 11
Snow15 (6.8%)
↓-31.8%prior 22
Ice/frost14 (6.4%)
↑7.7%prior 13
Mud, dirt5 (2.3%)
Sand1 (0.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes saw a shift in ranking. Ford became the most common make with 99 vehicles in 2017, an increase from 54 in 2016. Chevrolet, the top make in 2016 with 104 vehicles, saw its involvement decrease to 77 vehicles in 2017. Regarding driver and passenger demographics, the number of individuals aged 16-20 involved in crashes decreased from 80 to 67, representing a smaller share of the total persons involved compared to the prior year.

Top Vehicle Makes (404 vehicles)

1
FORD99 (24.5%)
↑83.3%prior 54
2
CHEV40 (9.9%)
↓-13.0%prior 46
3
CHEVROLET37 (9.2%)
↓-36.2%prior 58
4
TOYT17 (4.2%)
↑41.7%prior 12
5
GMC15 (3.7%)
↑200.0%prior 5
6
PONT13 (3.2%)
→0.0%prior 13
7
CHRY13 (3.2%)
↑116.7%prior 6
8
DODG13 (3.2%)
↓-7.1%prior 14
9
TOYOTA11 (2.7%)
↓-8.3%prior 12
10
CHRYSLER8 (2%)
↓-20.0%prior 10

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (278 persons with recorded sex)

Male178 (64.0%)
↓-4.8%prior 187
Female100 (36.0%)
↓-6.5%prior 107

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 304
  • Total persons involved: 477
  • Total vehicles involved: 404

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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