Yearly Traffic Safety Analysis

233 CRASHES IN
IOWA, IA
2015

In 2015, Crawford County recorded 233 total traffic crashes, resulting in 4 fatalities and 99 injuries. These incidents involved 390 vehicles and 494 individuals. The most frequently cited contributing factor in these collisions was the presence of an animal on the roadway, which was noted in 31 separate crashes.

233

Total Crash Events

4

Persons Killed

99

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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

Motorists comprised the entirety of fatalities, with 4 killed, and accounted for the vast majority of injuries, with 97 injured. In addition to motorists, 2 pedestrians were injured in crashes during this period. No cyclists were reported as killed or injured in 2015.

0

Pedestrians Killed

4

Motorists Killed

2

Pedestrians Injured

97

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

Crash frequency in Crawford County peaked on Fridays, which saw 47 incidents, while the single busiest hour was 4 p.m., with 19 crashes. A significant majority of collisions, 153 out of 233, occurred during daylight hours. Crashes during periods of darkness, dusk, or dawn collectively accounted for 53 incidents.

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 233 crashes, 153 (65.7%) resulted in no injuries, being classified as property-damage-only events. The remaining incidents involved some level of injury, including 47 with possible injuries, 18 with minor injuries, and 11 with serious injuries. Four crashes were classified as fatal, leading to a total of 4 fatalities.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.7%
Serious Injury11serious injury crashes4.7%
Minor Injury18minor injury crashes7.7%
Possible Injury47possible injury crashes20.2%
No Injury153no injury crashes65.7%

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 leading contributing factor identified in crashes was the presence of an animal, cited in 31 incidents (13.3%). Following this, losing control of the vehicle was a factor in 23 crashes (9.9%), and following too closely was noted in 21 crashes (9.0%). Other significant factors included running a stop sign (13 crashes) and failure to yield right-of-way from a stop sign (11 crashes).

Officer-Reported Primary Contributing Cause

Animal31 (13.3%)
Lost Control23 (9.9%)
Followed too close21 (9%)
Ran Stop Sign13 (5.6%)
FTYROW: From stop sign11 (4.7%)
Ran off road - left11 (4.7%)
Other (explain in narrative): Other11 (4.7%)
FTYROW: Making left turn9 (3.9%)
Ran off road - straight9 (3.9%)
Operating vehicle in an reckless, erratic, careless, negligent manner6 (2.6%)

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 majority of crashes occurred in favorable conditions, with 153 (65.7%) happening in daylight, 136 (58.4%) in clear weather, and 134 (57.5%) on dry road surfaces. Adverse weather was a factor in a smaller subset of crashes, including 17 in rain and 15 in snow. Similarly, 27 crashes occurred on wet roads and 37 on surfaces affected by snow, ice, or slush.

Weather

Clear136 (66.0%)
Cloudy29 (14.1%)
Rain17 (8.3%)
Snow15 (7.3%)
Freezing rain/drizzle5 (2.4%)
Blowing Snow3 (1.5%)
Sleet, hail1 (0.5%)

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

Lighting

Daylight153 (73.6%)
Dark - roadway not lighted22 (10.6%)
Dark - roadway lighted21 (10.1%)
Dusk7 (3.4%)
Dawn3 (1.4%)
Dark - unknown roadway lighting2 (1.0%)

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

Road Surface

Dry134 (64.7%)
Wet27 (13.0%)
Snow17 (8.2%)
Ice/frost14 (6.8%)
Gravel8 (3.9%)
Slush6 (2.9%)
Sand1 (0.5%)

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

Vehicles & Demographics

Among the 494 people involved in crashes, the 16-20 age group was the most represented with 85 individuals, followed by the 35-44 age group with 75 individuals. Analysis of the 390 vehicles involved shows that Chevrolet was the most frequent make with 88 vehicles, followed by Ford with 66 and Dodge with 42 vehicles.

Top Vehicle Makes (390 vehicles)

1
FORD66 (16.9%)
2
CHEV59 (15.1%)
3
CHEVROLET29 (7.4%)
4
DODG26 (6.7%)
5
GMC23 (5.9%)
6
PONT16 (4.1%)
7
DODGE16 (4.1%)
8
TOYT13 (3.3%)
9
JEEP12 (3.1%)
10
BUIC9 (2.3%)

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

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

Sex Distribution (343 persons with recorded sex)

Male213 (62.1%)
Female130 (37.9%)

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

Major Cause

The most common major cause attributed to crashes was the presence of an animal, accounting for 31 incidents. The second leading cause was a driver losing control of their vehicle, cited in 23 crashes. Following too closely was the third most frequent cause, contributing to 21 crashes.

Major Cause

1
Animal31 (14.6%)
2
Lost Control23 (10.8%)
3
Followed too close21 (9.9%)
4
Ran Stop Sign13 (6.1%)
5
FTYROW: From stop sign11 (5.2%)
6
Ran off road - left11 (5.2%)
7
Other (explain in narrative): Other11 (5.2%)
8
FTYROW: Making left turn9 (4.2%)
9
Ran off road - straight9 (4.2%)

Showing top 9 of 37 reported. 28 additional (73 total) not shown: Operating vehicle in an reckless, erratic, careless, negligent manner, FTYROW: From driveway, Improper Backing, Driving too fast for conditions, Exceeded authorized speed, Passing: Other passing (explain in narrative), FTYROW: From parked position, FTYROW: Other (explain in narrative), Driver Distraction: Exterior distraction, Driver Distraction: Passenger, Driver Distraction: Reaching for object(s)/fallen object(s), Passing: Where prohibited by signs/markings, Driver Distraction: Other interior distraction, FTYROW: At uncontrolled intersection, Driver Distraction: Inattentive/lost in thought, Ran Traffic Signal, Operator inexperience, Other (explain in narrative): No improper action, Aggressive driving/road rage, Made improper turn, Driver Distraction: Adjusting devices (radio, climate), Illegally Parked/Unattended, FTYROW: From yield sign, Failed to keep in proper lane, Other (explain in narrative): Vision obstructed, Driver Distraction: Manual operation of an electronic communication device, Swerving/Evasive Action, FTYROW: To pedestrian.

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, which occurred in 132 crashes. Collisions involving animals were the second most common event, with 30 incidents. Single-vehicle events were also notable, including 15 overturns or rollovers and 11 crashes where the first harmful event was striking a ditch.

First Harmful Event

1
Collision with: Vehicle in traffic132 (57.1%)
2
Collision with: Animal30 (13%)
3
Non-collision events: Overturn/rollover15 (6.5%)
4
Collision with: Parked motor vehicle12 (5.2%)
5
Collision with fixed object: Ditch11 (4.8%)
6
Collision with fixed object: Utility pole/light support4 (1.7%)
7
Non-collision events: Other non-collision (explain in narrative)4 (1.7%)
8
Other (explain in narrative)3 (1.3%)
9
Collision with: Re-entering roadway3 (1.3%)

Showing top 9 of 18 reported. 9 additional (17 total) not shown: Collision with fixed object: Tree, Collision with fixed object: Embankment, Collision with: Other non-fixed object (explain in narrative), Collision with fixed object: Curb/island/raised median, Collision with fixed object: Mailbox, Collision with: Non-motorist (see non-motorist section - NOT a unit), Non-collision events: Non-contact vehicle (phantom), Miscellaneous events: Eluding law enforcement, Collision with fixed object: Other post/pole/support (explain in narrative).

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

Roadway Junction / Feature

Crashes were more likely to occur away from intersections, with 113 incidents happening on non-junction road segments. Four-way intersections were the most common junction type for crashes, accounting for 57 incidents. T-intersections were the site of 19 crashes.

Roadway Junction / Feature

1
Non-intersection: Non-junction/no special feature113 (54.3%)
2
Intersection: Four-way intersection57 (27.4%)
3
Intersection: T-intersection19 (9.1%)
4
Non-intersection: Driveway access (related, not in)9 (4.3%)
5
Non-intersection: Alley3 (1.4%)
6
Intersection: Y-intersection3 (1.4%)
7
Intersection: Other intersection (explain in narrative)2 (1%)
8
Non-intersection: Driveway access (within)1 (0.5%)
9
Non-intersection: Other non-intersection (explain in narrative)1 (0.5%)

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 126 units recorded. Light trucks and pickups were the second most frequent with 104 vehicles, followed by sport utility vehicles (SUVs) with 86. Commercial tractor-trailers were involved in 20 crashes, and motorcycles were involved in 7.

Vehicle Type

"Other" combines 7 smaller categories (12 records): Single unit truck (2-axle, 6-tire) (3), Farm tractor (2), Moped (2), Single-unit truck (>= 3 axles) (2), Cargo/panel van (1), School bus (seats > 15) (1), All-terrain vehicle (ATV) (1).

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

Traffic Control Device

The majority of vehicles involved in crashes were in areas with no traffic controls present, a situation recorded for 253 vehicles. For crashes where traffic controls were present, stop signs were the most common, noted for 56 vehicles. Traffic signals were present for 34 of the vehicles involved in collisions.

Traffic Control Device

"Other" combines 3 smaller categories (4 records): Yield signs (2), School zone signs (1), Warning sign (1).

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

Most Damaged Area

Frontal impacts were the most common type of vehicle damage, with the primary point of impact being the front for 76 vehicles. Rear impacts, indicative of rear-end collisions, were the second most common, with 36 vehicles sustaining the most damage to the rear. Damage to the driver's side was also significant, recorded as the most damaged area on 78 vehicles across front, middle, and rear sections.

Most Damaged Area

"Other" combines 9 smaller categories (100 records): Driver side - rear (19), Top (19), Passenger side - middle (18), Passenger side - front (16), Passenger side - rear (10), Rear - passenger side corner (9), Other (explain in narrative) (6), Undercarriage (2), 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

The highest concentration of crashes within Crawford County occurred in Denison, which accounted for 121 of the reported incidents. Considerably fewer crashes were reported in other municipalities, with Dow City recording 8 crashes and Schleswig recording 7. Several other towns, including Manilla, Vail, and Charter Oak, reported 2 or fewer crashes.

Crashes by City

1
DENISON121 (85.2%)
2
DOW CITY8 (5.6%)
3
SCHLESWIG7 (4.9%)
4
MANILLA2 (1.4%)
5
VAIL1 (0.7%)
6
CHARTER OAK1 (0.7%)
7
KIRON1 (0.7%)
8
ASPINWALL1 (0.7%)

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

Paved vs Unpaved Road

The vast majority of crashes, 212 out of 233, occurred on paved roadways. Crashes on unpaved surfaces, such as gravel or dirt roads, accounted for 21 incidents, representing 9.0% of the total for the year.

Paved vs Unpaved Road

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

Roadway Contributing Factor

Among roadway-related contributing factors, adverse surface conditions such as wet or icy roads were the most cited, contributing to 34 crashes. A slippery, loose, or worn surface was noted as a factor in 4 additional crashes. Other factors like debris or work zones were each cited in only one crash.

Roadway Contributing Factor

1
Surface condition (e.g.wet, icy)34 (82.9%)
2
Slippery, loose or worn surface4 (9.8%)
3
Debris1 (2.4%)
4
Ruts/holes/bumps1 (2.4%)
5
Work Zone (roadway-related)1 (2.4%)

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

Driver Condition

In a small subset of crashes, driver condition was noted as a factor. Six drivers were recorded as being under the influence of alcohol, and 3 drivers were noted as being asleep or fatigued. One crash was attributed to a driver's illness or fainting.

Driver Condition

1
Under the influence of alcohol6 (60%)
2
Asleep/fatigued3 (30%)
3
Illness/fainted1 (10%)

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 140 crashes. The next most frequent cost bracket was $7,500 to $25,000, accounting for 80 crashes. A smaller number of incidents resulted in higher damage, with 6 crashes estimated to have costs 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 collision, accounting for 74 crashes or 31.8% of the total. Among multi-vehicle crashes, rear-end collisions were the most common type with 45 incidents (19.3%), followed closely by broadside collisions with 41 incidents (17.6%).

Manner of Collision

"Other" combines 3 smaller categories (13 records): Other (explain in narrative) (5), Rear to rear (4), Sideswipe, opposite direction (4).

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

Pre-Crash Driver Action

The most common action drivers were taking immediately before a crash was moving straight ahead, which was the case for 205 vehicles. Turning left was the next most frequent pre-crash action, recorded for 44 vehicles. A notable 28 vehicles were legally parked when they were involved in a collision.

Pre-Crash Driver Action

1
Movement essentially straight205 (55.3%)
2
Turning left44 (11.9%)
3
Legally Parked28 (7.5%)
4
Backing21 (5.7%)
5
Slowing/stopping (deceleration)17 (4.6%)
6
Turning right13 (3.5%)
7
Stopped in traffic12 (3.2%)
8
Overtaking/passing8 (2.2%)
9
Other (explain in narrative)8 (2.2%)

Showing top 9 of 16 reported. 7 additional (15 total) not shown: Negotiating a curve, Entering a parked position, Leaving a parked position, Illegally Parked/Unattended, Entering traffic lane (merging), Making U-turn, Leaving traffic lane.

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

Person Type

Of the 494 individuals involved in crashes, the vast majority, 473, were drivers. Passengers accounted for 19 of the individuals, and 2 were pedestrians. No cyclists were recorded as being involved in crashes.

Person Type

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

Person Injury Severity

Among the 494 people involved in crashes, 4 individuals sustained fatal injuries. A total of 99 people were injured, with severities ranging from possible (61 people) and minor (26 people) to serious (12 people). The majority of individuals involved in crashes were not injured.

Person Injury Severity

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

Occupant Safety Equipment

Among the occupants for whom safety equipment use was recorded, 68 were noted as using a shoulder and lap belt. Twelve individuals were recorded as using no safety equipment at the time of the crash. Other equipment, such as child safety seats or helmets, was used in a small number of cases.

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

Two-vehicle collisions were the most common crash configuration, accounting for 138 incidents. Single-vehicle crashes were also frequent, with 86 incidents recorded. Multi-vehicle pile-ups involving three or more vehicles were less common, with 8 three-vehicle crashes and 1 four-vehicle crash reported.

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: 233
  • Total persons involved: 494
  • Total vehicles involved: 390

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

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