Yearly Traffic Safety Analysis

385 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Cedar County, total traffic crashes decreased by 4.7% from 404 in 2016 to 385 in 2017. While overall crashes and resulting injuries (109 in 2017 vs. 110 in 2016) and fatalities (2 in both years) remained relatively stable, the number of crashes resulting in serious injuries more than doubled, increasing from 4 to 10 incidents year-over-year. The most notable shift in contributing factors was a 19.8% increase in the count of crashes involving animals, which remained the leading cause in both periods.

385

▼ -4.7%was 404

Total Crash Events

2

Persons Killed

109

▼ -0.9%was 110

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

Traffic safety trends in Cedar County showed a slight improvement, with total crashes declining from 404 in 2016 to 385 in 2017, a 4.7% decrease. Despite this reduction in crash volume, the number of people injured (109) and killed (2) was nearly identical to the previous year's totals of 110 injuries and 2 fatalities.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 2→0.0%

1

Cyclists Injured

Prior: 1→0.0%

108

Motorists Injured

Prior: 109↓-0.9%

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. In 2017, Friday was the peak day for crashes with 79 incidents, a change from 2016 when Sunday was the peak day with 76 crashes. The peak hour also shifted from 5 p.m. in 2016 (35 crashes) to 3 p.m. in 2017 (32 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

While the total number of fatal crashes remained unchanged at 2 incidents in both 2016 and 2017, the severity of injury crashes worsened. The count of crashes involving serious injuries increased by 150%, rising from 4 in 2016 to 10 in 2017. Conversely, crashes with possible injuries decreased from 49 to 43, and property-damage-only crashes fell from 315 to 295.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.5%
→0.0%prior 2
Serious Injury10serious injury crashes2.6%
↑150.0%prior 4
Minor Injury35minor injury crashes9.1%
↑2.9%prior 34
Possible Injury43possible injury crashes11.2%
↓-12.2%prior 49
No Injury295no injury crashes76.6%
↓-6.3%prior 315

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, with the count increasing by 19.8% from 96 crashes in 2016 to 115 in 2017. The second-ranked factor in 2017 was 'driving too fast for conditions', which saw its count increase by 53.6% from 28 to 43 incidents. Meanwhile, 'ran off road - straight', the second-leading factor in 2016 with 52 crashes, saw its count decrease by 26.9% to 38 crashes in 2017.

Officer-Reported Primary Contributing Cause

Animal115 (29.9%)↑19.8%prior 96
Driving too fast for conditions43 (11.2%)↑53.6%prior 28
Ran off road - straight38 (9.9%)↓-26.9%prior 52
Lost Control33 (8.6%)↓-8.3%prior 36
Followed too close29 (7.5%)↑11.5%prior 26
Ran off road - left18 (4.7%)↓-40.0%prior 30
Driver Distraction: Other interior distraction12 (3.1%)↑20.0%prior 10
FTYROW: From stop sign9 (2.3%)↓-30.8%prior 13
Operating vehicle in an reckless, erratic, careless, negligent manner8 (2.1%)↑33.3%prior 6
Improper or erratic lane changing8 (2.1%)↓-11.1%prior 9

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

Road & Environmental Conditions

Year-over-year, there was a shift in the conditions under which crashes occurred. Crashes on dry roads decreased from 195 to 168, and incidents in clear weather fell from 179 to 172. In contrast, crashes during adverse conditions increased, with incidents on snow-covered roads rising from 28 to 40 and crashes in snowy weather increasing from 25 to 39.

Weather

Clear172 (61.2%)
↓-3.9%prior 179
Snow39 (13.9%)
↑56.0%prior 25
Cloudy39 (13.9%)
↓-42.6%prior 68
Rain18 (6.4%)
↓-14.3%prior 21
Blowing Snow4 (1.4%)
Freezing rain/drizzle4 (1.4%)
↓-20.0%prior 5
Fog, smoke, smog3 (1.1%)
Severe Winds1 (0.4%)
Blowing sand, soil, dirt1 (0.4%)

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

Lighting

Daylight180 (64.3%)
↓-8.2%prior 196
Dark - roadway not lighted65 (23.2%)
↓-3.0%prior 67
Dark - roadway lighted17 (6.1%)
↓-5.6%prior 18
Dawn7 (2.5%)
→0.0%prior 7
Dusk7 (2.5%)
↓-41.7%prior 12
Dark - unknown roadway lighting4 (1.4%)
↓-33.3%prior 6

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

Road Surface

Dry168 (59.8%)
↓-13.8%prior 195
Snow40 (14.2%)
↑42.9%prior 28
Wet30 (10.7%)
↓-28.6%prior 42
Gravel26 (9.3%)
↑23.8%prior 21
Ice/frost9 (3.2%)
↓-43.8%prior 16
Slush5 (1.8%)
Mud, dirt2 (0.7%)
Other (explain in narrative)1 (0.4%)

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford and Chevrolet vehicles being the most common in both 2016 and 2017, though both saw a slight decrease in involvement counts. The age distribution of people involved in crashes showed a notable shift; involvement for the 35-44 age group increased from 76 to 109 individuals, while the 26-34 and 45-54 age groups, which were the top two in 2016, both saw a decrease in their numbers.

Top Vehicle Makes (562 vehicles)

1
FORD94 (16.7%)
↓-6.0%prior 100
2
CHEVROLET57 (10.1%)
↓-21.9%prior 73
3
CHEV50 (8.9%)
↑11.1%prior 45
4
TOYOTA28 (5%)
↑12.0%prior 25
5
FREIGHTLINER25 (4.4%)
↑38.9%prior 18
6
GMC19 (3.4%)
↑35.7%prior 14
7
DODGE15 (2.7%)
↑7.1%prior 14
8
JEEP15 (2.7%)
↑7.1%prior 14
9
HOND15 (2.7%)
↑50.0%prior 10
10
NISSAN15 (2.7%)
↑7.1%prior 14

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

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

Sex Distribution (423 persons with recorded sex)

Male256 (60.5%)
↓-6.9%prior 275
Female167 (39.5%)
↑6.4%prior 157

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: 385
  • Total persons involved: 644
  • Total vehicles involved: 562

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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