Yearly Traffic Safety Analysis

949 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Clinton County, total vehicle crashes decreased slightly from 963 in 2016 to 949 in 2017, a 1.5% reduction. While total fatalities remained unchanged at two, and total injuries saw a minor decline, the number of reported cyclist injuries more than doubled, increasing from 5 in 2016 to 13 in 2017.

949

▼ -1.5%was 963

Total Crash Events

2

Persons Killed

338

▼ -1.2%was 342

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 crash trends in Clinton County showed a slight decline between 2016 and 2017. Total crashes fell by 1.5% from 963 to 949. Similarly, the number of people injured decreased by 1.2% from 342 to 338, while the number of fatalities held steady at two for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 2→0.0%

9

Pedestrians Injured

Prior: 3↑200.0%

13

Cyclists Injured

Prior: 5↑160.0%

316

Motorists Injured

Prior: 332↓-4.8%

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 temporal patterns of crashes shifted year-over-year. The peak day for crashes moved from Friday (172 crashes) in 2016 to Thursday (155 crashes) in 2017. The peak hour also changed, shifting from a dual peak at 12 p.m. and 5 p.m. in 2016 (76 crashes each) to a more concentrated peak at 3 p.m. in 2017 (91 crashes).

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

The severity of crashes saw a slight shift towards more injury-involved incidents. While the number of fatal crashes remained constant at two in both 2016 and 2017, the proportion of crashes resulting in no injury decreased from 72.3% to 70.0%. Concurrently, crashes involving serious injuries increased from 21 to 25, and those with possible injuries rose from 154 to 172.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.2%
→0.0%prior 2
Serious Injury25serious injury crashes2.6%
↑19.0%prior 21
Minor Injury86minor injury crashes9.1%
↓-4.4%prior 90
Possible Injury172possible injury crashes18.1%
↑11.7%prior 154
No Injury664no injury crashes70%
↓-4.6%prior 696

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 periods, increasing in count from 150 in 2016 to 160 in 2017. While 'Failure to yield from a stop sign' remained a top-three factor, its count was nearly identical year-over-year (68 vs 69). Notably, crashes attributed to 'Following too close' decreased from 69 to 60, and incidents of 'Ran Traffic Signal' dropped from 34 to 15.

Officer-Reported Primary Contributing Cause

Animal160 (16.9%)↑6.7%prior 150
Other (explain in narrative): Other76 (8%)↑4.1%prior 73
FTYROW: From stop sign69 (7.3%)↑1.5%prior 68
Lost Control65 (6.8%)↓-1.5%prior 66
Followed too close60 (6.3%)↓-13.0%prior 69
FTYROW: Making left turn44 (4.6%)→0.0%prior 44
Driver Distraction: Other interior distraction35 (3.7%)↑45.8%prior 24
Ran Stop Sign34 (3.6%)↓-29.2%prior 48
Ran off road - straight32 (3.4%)→0.0%prior 32
Driving too fast for conditions27 (2.8%)↓-6.9%prior 29

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

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with most incidents occurring in daylight on dry roads. In 2017, crashes on dry surfaces accounted for a slightly larger share of the total (69.1%) compared to 2016 (65.8%). Similarly, crashes in clear weather increased from 508 to 552, representing 58.2% of all crashes in 2017 versus 52.8% in the prior year.

Weather

Clear552 (68.8%)
↑8.7%prior 508
Cloudy169 (21.1%)
↓-23.2%prior 220
Rain36 (4.5%)
↓-7.7%prior 39
Snow22 (2.7%)
↓-24.1%prior 29
Fog, smoke, smog12 (1.5%)
↑100.0%prior 6
Freezing rain/drizzle8 (1.0%)
Severe Winds2 (0.2%)
Sleet, hail1 (0.1%)

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

Lighting

Daylight563 (69.5%)
↓-2.6%prior 578
Dark - roadway not lighted111 (13.7%)
↑12.1%prior 99
Dark - roadway lighted99 (12.2%)
↓-8.3%prior 108
Dusk21 (2.6%)
↓-4.5%prior 22
Dawn10 (1.2%)
↓-28.6%prior 14
Dark - unknown roadway lighting6 (0.7%)

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

Road Surface

Dry656 (81.4%)
↑3.5%prior 634
Wet95 (11.8%)
↑2.2%prior 93
Snow25 (3.1%)
↓-35.9%prior 39
Ice/frost19 (2.4%)
↓-34.5%prior 29
Gravel10 (1.2%)
↓-44.4%prior 18
Slush1 (0.1%)

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

Vehicles & Demographics

The distribution of vehicle makes involved in crashes was stable, with Ford and Chevrolet remaining the top two in both 2016 and 2017. Analysis of persons involved shows a shift in age demographics; the 26-34 age group saw its involvement increase from 234 to 270 individuals. Conversely, the number of persons in the 45-54 age group involved in crashes decreased from 241 to 202.

Top Vehicle Makes (1,580 vehicles)

1
FORD247 (15.6%)
↑3.3%prior 239
2
CHEV204 (12.9%)
↑23.6%prior 165
3
CHEVROLET171 (10.8%)
↓-19.7%prior 213
4
NR75 (4.7%)
↑47.1%prior 51
5
GMC70 (4.4%)
→0.0%prior 70
6
DODG62 (3.9%)
↓-1.6%prior 63
7
DODGE45 (2.8%)
↓-22.4%prior 58
8
TOYOTA42 (2.7%)
↓-23.6%prior 55
9
JEEP40 (2.5%)
→0.0%prior 40
10
TOYT38 (2.4%)
↓-7.3%prior 41

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

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

Sex Distribution (1,032 persons with recorded sex)

Male534 (51.7%)
↓-12.6%prior 611
Female498 (48.3%)
↓-6.2%prior 531

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: 949
  • Total persons involved: 1,865
  • Total vehicles involved: 1,580

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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