Yearly Traffic Safety Analysis

110 CRASHES IN
IOWA, IA
2015

In 2015, Howard County recorded 110 traffic crashes, resulting in 2 fatalities and 60 injuries. The single most prominent contributing factor identified in these incidents was collisions with animals, which accounted for nearly one-third of all crashes. The data reflects a total of one fatal crash during this period.

110

Total Crash Events

2

Persons Killed

60

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Aggregate counts from crash, person, and vehicle records

Vulnerable Road User Casualties

In 2015, motorists were the primary group affected by fatal and injurious crashes in Howard County. A total of 2 motorists were killed and 58 were injured in traffic incidents. Additionally, one pedestrian sustained injuries, while no cyclists were reported as killed or injured during this period.

0

Pedestrians Killed

2

Motorists Killed

0

Other Killed

1

Pedestrians Injured

58

Motorists Injured

1

Other Injured

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Crashes in Howard County occurred most frequently on Wednesdays and Saturdays, with each day recording 23 incidents. The evening commute hour of 5 p.m. was the single busiest hour for crashes, with 12 incidents. While nearly half of all crashes (52) occurred in daylight, a notable number of incidents also happened during hours of darkness on both unlighted (14 crashes) and lighted (7 crashes) roadways.

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Of the 110 total crashes, 69 incidents (62.7%) resulted in no injuries, being classified as property-damage-only. The remaining crashes involved some level of injury, including 20 with minor injuries, 16 with possible injuries, and 4 with serious injuries. One crash was fatal, resulting in two fatalities.

Severity is per crash event (most severe injury). 1 fatal crash events resulted in 2 persons killed.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.9%
Serious Injury4serious injury crashes3.6%
Minor Injury20minor injury crashes18.2%
Possible Injury16possible injury crashes14.5%
No Injury69no injury crashes62.7%

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Most severe injury per crash record

Top Contributing Factors

The most significant contributing factor to crashes was animals, cited in 34 incidents, representing 30.9% of all crashes. Other frequently cited factors included losing control (7 crashes), running off the road to the left (6 crashes), and either running off the road straight or driving too fast for conditions (5 crashes each). These factors provide insight into the primary circumstances leading to collisions in the county.

Officer-Reported Primary Contributing Cause

Animal34 (30.9%)
Lost Control7 (6.4%)
Ran off road - left6 (5.5%)
Ran off road - straight5 (4.5%)
Driving too fast for conditions5 (4.5%)
Other (explain in narrative): Other4 (3.6%)
Ran Stop Sign4 (3.6%)
Swerving/Evasive Action4 (3.6%)
FTYROW: From stop sign4 (3.6%)
Followed too close3 (2.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

A significant portion of crashes occurred in ideal driving conditions, with 52 incidents (47.3%) happening in daylight and 50 (45.5%) in clear weather. Similarly, 38 crashes (34.5%) were on dry road surfaces. However, adverse conditions were also a factor, with 14 crashes on snow-covered roads, 8 in snowy weather, and 5 on icy surfaces.

Weather

Clear50 (62.5%)
Cloudy15 (18.8%)
Snow8 (10.0%)
Rain3 (3.8%)
Freezing rain/drizzle1 (1.3%)
Fog, smoke, smog1 (1.3%)
Blowing Snow1 (1.3%)
Sleet, hail1 (1.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Weather condition at time of crash

Lighting

Daylight52 (65.0%)
Dark - roadway not lighted14 (17.5%)
Dark - roadway lighted7 (8.8%)
Dusk5 (6.3%)
Dawn2 (2.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Lighting condition field

Road Surface

Dry38 (47.5%)
Snow14 (17.5%)
Gravel14 (17.5%)
Wet7 (8.8%)
Ice/frost5 (6.3%)
Slush1 (1.3%)
Sand1 (1.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Road surface condition field

Vehicles & Demographics

The 16-20 age group was the most frequently involved demographic in crashes, accounting for 38 of the 219 total persons involved. Among vehicle makes, Chevrolet (including 'CHEV') was most common with 37 vehicles, followed by Ford with 20, and GMC with 8. These makes represent the most frequently involved vehicles in the year's crashes.

Top Vehicle Makes (156 vehicles)

1
FORD20 (12.8%)
2
CHEVROLET19 (12.2%)
3
CHEV18 (11.5%)
4
GMC8 (5.1%)
5
PONTIAC7 (4.5%)
6
DODGE6 (3.8%)
7
BUICK5 (3.2%)
8
HOND5 (3.2%)
9
DODG4 (2.6%)
10
KIA4 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

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

Sex Distribution (138 persons with recorded sex)

Male86 (62.3%)
Female52 (37.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Person-level records linked to crash events

Major Cause

Collisions with animals were the leading major cause of crashes, accounting for 34 incidents (30.9%). Following this, loss of control was cited as the cause in 7 crashes (6.4%). Running off the road was also a significant factor, with 6 crashes involving running off to the left and 5 running off straight.

Major Cause

1
Animal34 (31.2%)
2
Lost Control7 (6.4%)
3
Ran off road - left6 (5.5%)
4
Ran off road - straight5 (4.6%)
5
Driving too fast for conditions5 (4.6%)
6
Other (explain in narrative): Other4 (3.7%)
7
Ran Stop Sign4 (3.7%)
8
Swerving/Evasive Action4 (3.7%)
9
FTYROW: From stop sign4 (3.7%)

Showing top 9 of 31 reported. 22 additional (36 total) not shown: Followed too close, FTYROW: Making left turn, Improper Backing, Operating vehicle in an reckless, erratic, careless, negligent manner, FTYROW: At uncontrolled intersection, Ran off road - right, FTYROW: Other (explain in narrative), Driver Distraction: Other interior distraction, Driver Distraction: Exterior distraction, FTYROW: From driveway, Crossed centerline (undivided), Driver Distraction: Inattentive/lost in thought, Driver Distraction: Passenger, Failed to keep in proper lane, Failure to signal intentions, FTYROW: From parked position, FTYROW: To pedestrian, Illegally Parked/Unattended, Made improper turn, Other (explain in narrative): Vision obstructed, Passing: Other passing (explain in narrative), Passing: With insufficient distance/inadequate visibility.

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

First Harmful Event

The most common first harmful event was a collision with another vehicle in traffic, which occurred in 43 crashes. The second most frequent event was a collision with an animal, documented in 34 incidents. Non-collision events, primarily overturns or rollovers, were the first harmful event in 11 crashes, while collision with a ditch occurred in 9 cases.

First Harmful Event

1
Collision with: Vehicle in traffic43 (39.1%)
2
Collision with: Animal34 (30.9%)
3
Non-collision events: Overturn/rollover11 (10%)
4
Collision with fixed object: Ditch9 (8.2%)
5
Collision with fixed object: Utility pole/light support2 (1.8%)
6
Collision with: Parked motor vehicle2 (1.8%)
7
Collision with fixed object: Other fixed object (explain in narrative)2 (1.8%)
8
Collision with: Other non-fixed object (explain in narrative)1 (0.9%)
9
Collision with: Re-entering roadway1 (0.9%)

Showing top 9 of 14 reported. 5 additional (5 total) not shown: Non-collision events: Jackknife, Collision with fixed object: Building, Collision with fixed object: Traffic sign support, Collision with fixed object: Tree, Collision with: Non-motorist (see non-motorist section - NOT a unit).

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Roadway Junction / Feature

The majority of crashes, 54 incidents, occurred at non-intersection locations, with 46 of those on straight or curved road segments without special features. Intersections accounted for 26 crashes, with four-way intersections being the most common type, the site of 15 incidents. T-intersections were the location for another 7 crashes.

Roadway Junction / Feature

1
Non-intersection: Non-junction/no special feature46 (57.5%)
2
Intersection: Four-way intersection15 (18.8%)
3
Intersection: T-intersection7 (8.8%)
4
Non-intersection: Driveway access (related, not in)6 (7.5%)
5
Intersection: Other intersection (explain in narrative)4 (5%)
6
Non-intersection: Driveway access (within)1 (1.3%)
7
Non-intersection: Other non-intersection (explain in narrative)1 (1.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Vehicle Type

Passenger cars were the most prevalent vehicle type involved in crashes, with 64 units recorded, followed by light trucks or pickups with 34 units and sport utility vehicles with 25. Commercial vehicles like tractor-trailers were involved in 4 crashes, and motorcycles were involved in 3 incidents. These figures represent the distribution of vehicle types among the 156 total vehicles in crashes.

Vehicle Type

"Other" combines 7 smaller categories (11 records): Maintenance/construction vehicle (2), Tractor/doubles (2), Farm tractor (2), Single-unit truck (>= 3 axles) (2), Truck/trailer (1), Single unit truck (2-axle, 6-tire) (1), Farm equipment (explain in narrative) (1).

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

Traffic Control Device

A large majority of crashes, 82 incidents, occurred on roadways where no traffic controls were present. For crashes where controls were a factor, stop signs were the most common, being present at the scene of 27 incidents. Traffic signals were present for only 4 of the reported crashes.

Traffic Control Device

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

Most Damaged Area

The most common area of vehicle damage was the front, which was the primary impact point in 33 vehicles. Including corner impacts, frontal damage was noted in a total of 54 vehicles. Side impacts were also frequent, with damage to the driver-side or passenger-side areas noted in 33 vehicles, while rear impacts were recorded for 16 vehicles.

Most Damaged Area

"Other" combines 8 smaller categories (35 records): Passenger side - middle (6), Driver side - rear (6), Rear - passenger side corner (5), Passenger side - rear (5), Rear (4), Other (explain in narrative) (4), Top (4), Undercarriage (1).

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

Crashes by City

Within Howard County, the city of Cresco experienced the highest volume of traffic incidents, with 24 crashes recorded in 2015. Far fewer crashes were reported in other municipalities, including 3 in Chester and 2 each in Lime Springs, Protivin, and Riceville. These figures represent crashes occurring within city limits.

Crashes by City

1
CRESCO24 (70.6%)
2
CHESTER3 (8.8%)
3
LIME SPRINGS2 (5.9%)
4
PROTIVIN2 (5.9%)
5
RICEVILLE2 (5.9%)
6
ELMA1 (2.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Paved vs Unpaved Road

Crashes were predominantly on paved roads, with 89 incidents occurring on such surfaces. However, a notable portion, 21 crashes, happened on unpaved roads. This represents 19.1% of all crashes in the county, highlighting the role of the gravel and dirt road network in local traffic safety.

Paved vs Unpaved Road

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Roadway Contributing Factor

In a minority of crashes where a roadway factor was identified, adverse surface conditions were the leading contributor, cited in 16 incidents. This includes conditions such as wet or icy surfaces. An additional 4 crashes were attributed to a slippery, loose, or worn surface, while other factors like obstructions were rarely cited.

Roadway Contributing Factor

1
Surface condition (e.g.wet, icy)16 (69.6%)
2
Slippery, loose or worn surface4 (17.4%)
3
Non-highway work1 (4.3%)
4
Obstruction in roadway1 (4.3%)
5
Shoulders (none, low, soft, high)1 (4.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Driver Condition

Among the subset of drivers for whom a condition other than 'apparently normal' was noted, fatigue and alcohol impairment were the most frequent. Four drivers were identified as being asleep or fatigued, and another four were noted as being under the influence of alcohol. These documented conditions represent a small fraction of the total drivers involved in crashes.

Driver Condition

1
Asleep/fatigued4 (40%)
2
Under the influence of alcohol4 (40%)
3
Medical condition (seizure, reaction)1 (10%)
4
Under the influence of drugs/meds1 (10%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Property Damage

The most common range for officer-estimated property damage was between $1,500 and $7,500, which applied to 79 of the 110 crashes. A smaller number of crashes, 26, resulted in damages estimated between $7,500 and $25,000. Only 3 incidents (2.7%) were reported to have damage exceeding $25,000.

Property Damage

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Manner of Collision

Single-vehicle crashes, categorized as non-collision events, were the most frequent type, accounting for 51 incidents or 46.4% of the total. Among multi-vehicle crashes, broadside collisions were the most common, with 13 incidents (11.8%), followed by rear-end collisions, which occurred in 10 cases (9.1%).

Manner of Collision

"Other" combines 1 smaller categories (4 records): Rear to side (4).

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Pre-Crash Driver Action

The predominant pre-crash action for vehicles involved was moving straight, which was the case for 82 vehicles. Turning left was the action for 12 vehicles immediately before their crash. Backing was the third most common pre-crash maneuver, recorded for 10 vehicles.

Pre-Crash Driver Action

1
Movement essentially straight82 (58.2%)
2
Turning left12 (8.5%)
3
Backing10 (7.1%)
4
Legally Parked6 (4.3%)
5
Slowing/stopping (deceleration)6 (4.3%)
6
Other (explain in narrative)5 (3.5%)
7
Stopped in traffic5 (3.5%)
8
Negotiating a curve4 (2.8%)
9
Overtaking/passing3 (2.1%)

Showing top 9 of 15 reported. 6 additional (8 total) not shown: Entering a parked position, Turning right, Entering traffic lane (merging), Illegally Parked/Unattended, Leaving traffic lane, Making U-turn.

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

Person Type

Of the 219 individuals involved in crashes, the vast majority, 199 people, were drivers. Passengers comprised a smaller group of 18 individuals. The dataset also includes one pedestrian and one other non-motorist among those involved in traffic incidents.

Person Type

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Person Injury Severity

Across all 219 people involved in crashes, 2 sustained fatal injuries. An additional 60 people were injured, with severities ranging from possible (26 persons) and minor (30 persons) to serious (4 persons). This indicates that while most crashes did not result in injury, a significant number of individuals were still affected.

Person Injury Severity

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Occupant Safety Equipment

Based on the available data for safety equipment usage, 32 individuals were recorded as using a shoulder and lap belt. In contrast, 6 individuals were noted as having used no safety restraints at all. The data also shows 3 individuals used only a lap belt.

Occupant Safety Equipment

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Person-level records linked to crash events

Vehicles Per Crash

Single-vehicle crashes were the most common incident type, accounting for 64 of the 110 total crashes (58.2%). The remaining 46 crashes (41.8%) involved two vehicles. No crashes involving three or more vehicles were reported in this dataset.

Vehicles Per Crash

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

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: 2015-01-01 through 2015-12-31
  • Report generated: September 10, 2026

Data Coverage

  • Reporting period: 2015-01-01 through 2015-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 110
  • Total persons involved: 219
  • Total vehicles involved: 156

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: 2015." Published September 10, 2026. Reporting period: 2015-01-01 to 2015-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2015-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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