Yearly Traffic Safety Analysis

184 CRASHES IN
IOWA, IA
2015

In 2015, Greene County recorded 184 total traffic crashes, resulting in 2 fatalities and 75 injuries. These incidents included 2 fatal crashes and 8 crashes where a driver was suspected of being under the influence. A notable finding is that collisions with animals were the single most cited contributing factor, accounting for 18.5% of all crashes.

184

Total Crash Events

2

Persons Killed

75

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 · 2015-01-01 to 2015-12-31 · Aggregate counts from crash, person, and vehicle records

Vulnerable Road User Casualties

In 2015, both traffic fatalities in Greene County were motorists, and an additional 74 motorists were injured. There were no cyclist fatalities or injuries reported during this period. One pedestrian was injured in a crash, but there were no pedestrian fatalities.

0

Pedestrians Killed

2

Motorists Killed

1

Pedestrians Injured

74

Motorists 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 Greene County occurred most frequently on Thursdays and Fridays, with each day accounting for 30 incidents. The afternoon hours were the most common time for crashes, with a peak between 2:00 PM and 5:00 PM, during which 50 crashes occurred. The majority of collisions, 120 out of 184, happened during daylight hours.

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 184 crashes recorded, 29.9% resulted in some level of injury, while 70.1% were classified as having no injuries. There were 2 fatal crashes, which resulted in a total of 2 fatalities. The remaining injury crashes included 6 with serious injuries, 20 with minor injuries, and 27 with possible injuries.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.1%
Serious Injury6serious injury crashes3.3%
Minor Injury20minor injury crashes10.9%
Possible Injury27possible injury crashes14.7%
No Injury129no injury crashes70.1%

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 common contributing factor cited in crashes was an animal, involved in 34 incidents (18.5%). Other leading factors included drivers losing control (17 crashes), driving too fast for conditions (15 crashes), and running off a straight road (13 crashes). Failure to yield right of way at uncontrolled intersections also accounted for 13 crashes.

Officer-Reported Primary Contributing Cause

Animal34 (18.5%)
Lost Control17 (9.2%)
Driving too fast for conditions15 (8.2%)
Ran off road - straight13 (7.1%)
FTYROW: At uncontrolled intersection13 (7.1%)
FTYROW: From stop sign12 (6.5%)
Followed too close7 (3.8%)
Ran Stop Sign7 (3.8%)
Driver Distraction: Exterior distraction6 (3.3%)
Operating vehicle in an reckless, erratic, careless, negligent manner6 (3.3%)

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

Road & Environmental Conditions

The vast majority of crashes occurred in favorable environmental conditions. Clear weather was reported in 112 of the 184 crashes (60.9%), and the road surface was dry in 114 incidents (62.0%). Similarly, 120 crashes, or 65.2% of the total, took place in daylight.

Weather

Clear112 (70.0%)
Cloudy29 (18.1%)
Rain8 (5.0%)
Freezing rain/drizzle4 (2.5%)
Snow4 (2.5%)
Fog, smoke, smog2 (1.3%)
Blowing sand, soil, dirt1 (0.6%)

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

Lighting

Daylight120 (72.3%)
Dark - roadway not lighted31 (18.7%)
Dark - roadway lighted7 (4.2%)
Dawn3 (1.8%)
Dusk3 (1.8%)
Dark - unknown roadway lighting2 (1.2%)

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

Road Surface

Dry114 (68.7%)
Wet16 (9.6%)
Gravel12 (7.2%)
Ice/frost11 (6.6%)
Snow9 (5.4%)
Slush2 (1.2%)
Mud, dirt2 (1.2%)

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

Vehicles & Demographics

Among the 364 people involved in crashes, the 16-20 age group was the most represented with 64 individuals, followed by the 45-54 age group with 56 individuals. Of the 285 vehicles involved, Ford and Chevrolet were the most frequent makes, each appearing 57 times in the crash data.

Top Vehicle Makes (285 vehicles)

1
FORD57 (20%)
2
CHEV57 (20%)
3
CHEVROLET26 (9.1%)
4
GMC11 (3.9%)
5
JEEP11 (3.9%)
6
TOYOTA7 (2.5%)
7
DODG7 (2.5%)
8
HOND7 (2.5%)
9
CHRY6 (2.1%)
10
CHRYSLER6 (2.1%)

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

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

Sex Distribution (261 persons with recorded sex)

Male150 (57.5%)
Female111 (42.5%)

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

Major Cause

The leading major cause of crashes was interaction with an animal, accounting for 34 incidents. Following this, 17 crashes were attributed to a driver losing control, and 15 were due to driving too fast for conditions. Failure to yield at uncontrolled intersections and from stop signs were also significant factors, contributing to 13 and 12 crashes, respectively.

Major Cause

1
Animal34 (19.3%)
2
Lost Control17 (9.7%)
3
Driving too fast for conditions15 (8.5%)
4
Ran off road - straight13 (7.4%)
5
FTYROW: At uncontrolled intersection13 (7.4%)
6
FTYROW: From stop sign12 (6.8%)
7
Followed too close7 (4%)
8
Ran Stop Sign7 (4%)
9
Driver Distraction: Exterior distraction6 (3.4%)

Showing top 9 of 32 reported. 23 additional (52 total) not shown: Operating vehicle in an reckless, erratic, careless, negligent manner, Other (explain in narrative): No improper action, Swerving/Evasive Action, Ran off road - left, Failed to keep in proper lane, Made improper turn, Passing: Other passing (explain in narrative), Ran off road - right, Driver Distraction: Other interior distraction, Other (explain in narrative): Other, Driver Distraction: Other electronic device activity, Driver Distraction: Inattentive/lost in thought, FTYROW: Making left turn, Crossed centerline (undivided), Traveling wrong way or on wrong side of road, Failed to yield to emergency vehicle, FTYROW: From driveway, Driver Distraction: Talking on a hand-held device, Improper Backing, Failure to signal intentions, Driver Distraction: Unrestrained animal, Towing Improperly, Exceeded authorized speed.

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

First Harmful Event

The most frequent first harmful event was a collision with another vehicle in traffic, occurring in 69 crashes. The second most common event was a collision with an animal, which initiated 34 crashes. Run-off-road events were also prevalent, with 19 crashes involving a ditch and 16 involving an overturn or rollover.

First Harmful Event

1
Collision with: Vehicle in traffic69 (37.9%)
2
Collision with: Animal34 (18.7%)
3
Collision with fixed object: Ditch19 (10.4%)
4
Non-collision events: Overturn/rollover16 (8.8%)
5
Collision with: Parked motor vehicle8 (4.4%)
6
Non-collision events: Other non-collision (explain in narrative)7 (3.8%)
7
Collision with: Struck/struck by object/cargo/person from other vehicle6 (3.3%)
8
Collision with fixed object: Utility pole/light support4 (2.2%)
9
Collision with fixed object: Embankment2 (1.1%)

Showing top 9 of 23 reported. 14 additional (17 total) not shown: Collision with fixed object: Other post/pole/support (explain in narrative), Non-collision events: Vehicle went airborne, Other (explain in narrative), Collision with fixed object: Mailbox, Collision with: Re-entering roadway, Collision with fixed object: Guardrail - face, Collision with fixed object: Ground, Miscellaneous events: Eluding law enforcement, Non-collision events: Jackknife, Collision with fixed object: Fire hydrant, Collision with fixed object: Fence, Collision with fixed object: Culvert/pipe opening, 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 data indicates that a majority of crashes occurred away from intersections, with 104 incidents happening on non-junction road segments. Four-way intersections were the most common crash location among junction types, accounting for 39 incidents. T-intersections were the site of another 13 crashes.

Roadway Junction / Feature

1
Non-intersection: Non-junction/no special feature104 (61.5%)
2
Intersection: Four-way intersection39 (23.1%)
3
Intersection: T-intersection13 (7.7%)
4
Non-intersection: Other non-intersection (explain in narrative)4 (2.4%)
5
Intersection: Other intersection (explain in narrative)3 (1.8%)
6
Non-intersection: Driveway access (within)2 (1.2%)
7
Non-intersection: Driveway access (related, not in)2 (1.2%)
8
Non-intersection: Railroad grade crossing1 (0.6%)
9
Non-intersection: Alley1 (0.6%)

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

Vehicle Type

Passenger cars were the most common vehicle type involved in crashes, with 110 units recorded. Sport utility vehicles and four-tire light trucks (pick-ups) were also frequently involved, each with 64 units. Tractor/semi-trailers were involved in 7 crashes, and motorcycles were involved in 4 crashes.

Vehicle Type

"Other" combines 11 smaller categories (14 records): Single-unit truck (>= 3 axles) (3), Cargo/panel van (2), Golf cart (1), School bus (seats > 15) (1), Farm tractor (1), All-terrain vehicle (ATV) (1), Farm equipment (explain in narrative) (1), Truck tractor (bobtail) (1), Truck/trailer (1), Maintenance/construction vehicle (1), Moped (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 vehicles involved in crashes were in areas with no traffic controls present, a situation recorded for 226 vehicles. Where controls were present, stop signs were the most common type, noted for 35 vehicles involved in collisions. A small number of incidents involved vehicles in marked no-passing zones (4).

Traffic Control Device

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

Most Damaged Area

Front-end impacts were the most common area of vehicle damage, with the primary front area being the most damaged point in 84 vehicles. Front-corner impacts were also frequent, with 23 instances on the driver-side corner and 23 on the passenger-side corner. Rear-end damage was noted as the primary impact point for 22 vehicles.

Most Damaged Area

"Other" combines 9 smaller categories (62 records): Passenger side - middle (13), Driver side - rear (10), Passenger side - rear (9), Rear - driver side corner (8), Top (8), Other (explain in narrative) (7), Rear - passenger side corner (3), Undercarriage (3), Non-collision/no damage (1).

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

Crashes by City

Within Greene County, the city of Jefferson experienced the highest number of crashes in 2015, with 70 incidents. Grand Junction followed with 8 crashes, and Scranton had 4 crashes. The remaining incidents were distributed among Churdan (2), Ralston (1), and Rippey (1), or occurred outside of any city's limits.

Crashes by City

1
JEFFERSON70 (81.4%)
2
GRAND JUNCTION8 (9.3%)
3
SCRANTON4 (4.7%)
4
CHURDAN2 (2.3%)
5
RALSTON1 (1.2%)
6
RIPPEY1 (1.2%)

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

Paved vs Unpaved Road

Crashes on paved roads were far more common than on unpaved surfaces. Of the incidents where this was specified, 156 crashes occurred on paved roadways. In contrast, 28 crashes, representing 15.2% of the total, took place on unpaved roads.

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 the minority of crashes where a roadway factor was cited, the most common was the surface condition, such as being wet or icy, which was noted in 15 incidents. A slippery, loose, or worn surface was a factor in 2 crashes. An obstruction in the roadway and ruts or holes each contributed to one crash.

Roadway Contributing Factor

1
Surface condition (e.g.wet, icy)15 (78.9%)
2
Slippery, loose or worn surface2 (10.5%)
3
Obstruction in roadway1 (5.3%)
4
Ruts/holes/bumps1 (5.3%)

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

Driver Condition

Among drivers for whom a condition other than 'apparently normal' was noted, 11 were recorded as being under the influence of alcohol. An emotional state, such as being depressed or angry, was cited for 8 drivers. Being asleep or fatigued was the condition for 3 drivers involved in crashes.

Driver Condition

1
Under the influence of alcohol11 (44%)
2
Emotional (e.g. depressed, angry)8 (32%)
3
Asleep/fatigued3 (12%)
4
Illness/fainted1 (4%)
5
Medical condition (seizure, reaction)1 (4%)
6
Under the influence of drugs/meds1 (4%)

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

Property Damage

The most common estimated property damage cost was in the $1,500 to $7,500 range, which applied to 114 crashes. A significant number of incidents, 56, resulted in damages between $7,500 and $25,000. Nine crashes were estimated to have caused property 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, non-collision events were the most frequent manner of crash, accounting for 86 incidents or 46.7% of the total. Among multi-vehicle crashes, broadside (front to side) collisions were the most common, with 38 occurrences (20.7%). Rear-end collisions followed, making up 10.9% of the total with 20 incidents.

Manner of Collision

"Other" combines 3 smaller categories (5 records): Rear to side (2), Angle, oncoming left turn (2), Rear to rear (1).

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 essentially straight, which was recorded for 196 vehicles. Turning left was the next most common action, noted for 19 vehicles. Sixteen vehicles were legally parked when they were involved in a collision.

Pre-Crash Driver Action

1
Movement essentially straight196 (71.8%)
2
Turning left19 (7%)
3
Legally Parked16 (5.9%)
4
Turning right8 (2.9%)
5
Other (explain in narrative)7 (2.6%)
6
Negotiating a curve7 (2.6%)
7
Backing5 (1.8%)
8
Slowing/stopping (deceleration)5 (1.8%)
9
Overtaking/passing4 (1.5%)

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

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

Person Type

Of the 364 individuals involved in crashes, the vast majority, 347 people, were drivers. Passengers accounted for 16 of the individuals involved. Only one person was identified as a pedestrian.

Person Type

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

Person Injury Severity

Among all persons involved in crashes, 2 sustained fatal injuries and 6 had serious injuries. An additional 27 people received minor injuries, and 42 had possible injuries. The data also shows 2 individuals were confirmed to have no injuries from the incidents they were involved in.

Person Injury Severity

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

Occupant Safety Equipment

For the subset of occupants where safety equipment use was recorded, 54 were using both a shoulder and lap belt. In contrast, 4 occupants were noted as using no safety equipment at all. One person used a DOT-compliant helmet, and one 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

Crashes were almost evenly split between single-vehicle and two-vehicle incidents, with 91 and 87 crashes respectively. Multi-vehicle pile-ups were rare; there were 5 crashes involving three vehicles and one crash involving five vehicles.

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 9, 2026

Data Coverage

  • Reporting period: 2015-01-01 through 2015-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 184
  • Total persons involved: 364
  • Total vehicles involved: 285

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 9, 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

ThatCarHitMe.com · An Injuria.ai Company