Yearly Traffic Safety Analysis

128 CRASHES IN
IOWA, IA
2015

In 2015, Monroe County recorded 128 traffic crashes, resulting in 2 fatalities and 36 injuries. The single most prominent statistical finding from the data is the high incidence of collisions involving animals, which was cited as the primary contributing factor in 46 crashes, accounting for 35.9% of all incidents in the county.

128

Total Crash Events

2

Persons Killed

36

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, crashes in Monroe County resulted in 2 motorists killed and 35 motorists injured. No cyclists were killed or injured. One pedestrian was injured, but there were no pedestrian fatalities. The data clearly indicates that vehicle occupants constituted the entirety of fatalities and the vast majority of injuries.

0

Pedestrians Killed

2

Motorists Killed

1

Pedestrians Injured

35

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 occurrences in Monroe County were most frequent on Mondays, which saw 25 incidents, closely followed by Thursdays with 24. The most common time for crashes was the 9 p.m. hour, with 11 incidents recorded. While more crashes happened during daylight hours (52 incidents), a significant number also occurred in dark conditions, including 28 on unlit roadways and 8 on lit 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

The vast majority of crashes, 100 out of 128, resulted in no injuries, accounting for 78.1% of all incidents. Injury-sustaining crashes included 4 with serious injuries, 4 with minor injuries, and 18 with possible injuries. Two separate crashes were fatal, resulting in a total of 2 fatalities for the year.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.6%
Serious Injury4serious injury crashes3.1%
Minor Injury4minor injury crashes3.1%
Possible Injury18possible injury crashes14.1%
No Injury100no injury crashes78.1%

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 leading contributing factor to crashes was overwhelmingly animals, cited in 46 incidents, or 35.9% of the total. The next most common factor was losing control of the vehicle, which accounted for 12 crashes (9.4%). Following this, several factors were each responsible for 7 crashes (5.5% each), including following too closely, running a stop sign, running off a straight road, and failure to yield the right-of-way from a stop sign.

Officer-Reported Primary Contributing Cause

Animal46 (35.9%)
Lost Control12 (9.4%)
Followed too close7 (5.5%)
Ran Stop Sign7 (5.5%)
Ran off road - straight7 (5.5%)
FTYROW: From stop sign7 (5.5%)
Operating vehicle in an reckless, erratic, careless, negligent manner6 (4.7%)
FTYROW: From driveway4 (3.1%)
Other (explain in narrative): Other3 (2.3%)
Made improper turn2 (1.6%)

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

Road & Environmental Conditions

A majority of crashes occurred in favorable conditions, with 70 incidents happening in clear weather and 63 on dry road surfaces. Daylight was the most frequent lighting condition, present for 52 crashes. However, adverse conditions were also a factor, with 11 crashes on wet roads, 6 during rain, and a combined 7 on roads with snow or ice. Crashes in low light were also notable, with 38 incidents occurring in dark conditions.

Weather

Clear70 (76.9%)
Cloudy14 (15.4%)
Rain6 (6.6%)
Snow1 (1.1%)

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

Lighting

Daylight52 (57.1%)
Dark - roadway not lighted28 (30.8%)
Dark - roadway lighted8 (8.8%)
Dark - unknown roadway lighting2 (2.2%)
Dawn1 (1.1%)

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

Road Surface

Dry63 (69.2%)
Wet11 (12.1%)
Gravel8 (8.8%)
Snow4 (4.4%)
Ice/frost3 (3.3%)
Other (explain in narrative)2 (2.2%)

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

Vehicles & Demographics

The most frequently involved vehicle makes in crashes were Chevrolet (47 vehicles), Ford (32), and Dodge (15), combining abbreviated and full names from the data. Analysis of persons involved shows the 35-44 age group was the most represented, with 37 individuals. Other highly represented groups include those aged 45-54 (33 people), 16-20 (31 people), and both the 21-25 and 26-34 age brackets (30 people each).

Top Vehicle Makes (184 vehicles)

1
FORD32 (17.4%)
2
CHEV26 (14.1%)
3
CHEVROLET21 (11.4%)
4
DODG8 (4.3%)
5
DODGE7 (3.8%)
6
GMC7 (3.8%)
7
PONT7 (3.8%)
8
BUIC6 (3.3%)
9
JEEP5 (2.7%)
10
TOYOTA5 (2.7%)

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

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

Sex Distribution (176 persons with recorded sex)

Male104 (59.1%)
Female72 (40.9%)

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 frequently cited major cause for crashes was interaction with an animal, accounting for 46 of 128 incidents (35.9%). Losing control of the vehicle was the second leading cause, with 12 incidents (9.4%). Other significant causes, each contributing to 7 crashes (5.5%), were following too closely, running a stop sign, and failing to yield the right-of-way from a stop sign.

Major Cause

1
Animal46 (37.1%)
2
Lost Control12 (9.7%)
3
Followed too close7 (5.6%)
4
Ran Stop Sign7 (5.6%)
5
Ran off road - straight7 (5.6%)
6
FTYROW: From stop sign7 (5.6%)
7
Operating vehicle in an reckless, erratic, careless, negligent manner6 (4.8%)
8
FTYROW: From driveway4 (3.2%)
9
Other (explain in narrative): Other3 (2.4%)

Showing top 9 of 28 reported. 19 additional (25 total) not shown: Made improper turn, Driving too fast for conditions, Driver Distraction: Inattentive/lost in thought, Other (explain in narrative): Vision obstructed, Ran off road - left, Swerving/Evasive Action, Failed to keep in proper lane, Operator inexperience, Other (explain in narrative): No improper action, Driver Distraction: Talking on a hands free device, Ran Traffic Signal, Passing: On wrong side, Passing: Where prohibited by signs/markings, Driver Distraction: Other interior distraction, Driver Distraction: Reaching for object(s)/fallen object(s), Failed to yield to emergency vehicle, FTYROW: Other (explain in narrative), Disregarded RR Signal, Driver Distraction: Passenger.

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

First Harmful Event

The initial event in crashes was almost evenly split between a collision with another vehicle in traffic (47 incidents) and a collision with an animal (46 incidents). These two events together initiated 72.7% of all crashes. Collisions with fixed objects were less common, with the most frequent being running into a ditch, which occurred in 10 crashes.

First Harmful Event

1
Collision with: Vehicle in traffic47 (37%)
2
Collision with: Animal46 (36.2%)
3
Collision with fixed object: Ditch10 (7.9%)
4
Non-collision events: Overturn/rollover7 (5.5%)
5
Collision with fixed object: Utility pole/light support4 (3.1%)
6
Non-collision events: Non-contact vehicle (phantom)2 (1.6%)
7
Collision with: Parked motor vehicle2 (1.6%)
8
Collision with: Railway vehicle/train1 (0.8%)
9
Collision with: Re-entering roadway1 (0.8%)

Showing top 9 of 16 reported. 7 additional (7 total) not shown: Collision with: Struck/struck by object/cargo/person from other vehicle, Collision with fixed object: Curb/island/raised median, Miscellaneous events: Eluding law enforcement, Collision with fixed object: Tree, Collision with fixed object: Bridge/bridge rail parapet, Collision with: Non-motorist (see non-motorist section - NOT a unit), Collision with: Other non-fixed object (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 on non-junction road segments than at intersections. The data shows 48 crashes happened on straight or curved sections of road with no special feature. In contrast, 31 crashes occurred at intersections, with four-way intersections being the most common type, accounting for 21 of these incidents.

Roadway Junction / Feature

1
Non-intersection: Non-junction/no special feature48 (53.3%)
2
Intersection: Four-way intersection21 (23.3%)
3
Intersection: T-intersection8 (8.9%)
4
Non-intersection: Driveway access (related, not in)5 (5.6%)
5
Non-intersection: Driveway access (within)3 (3.3%)
6
Non-intersection: Railroad grade crossing3 (3.3%)
7
Intersection: Y-intersection1 (1.1%)
8
Intersection: Other intersection (explain in narrative)1 (1.1%)

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 71 units, followed by four-tire light trucks or pickups (47) and sport utility vehicles (33). These three categories comprised 82.1% of the 184 vehicles involved in incidents. Notably, 9 tractor-trailers and 4 motorcycles were also involved in crashes during this period.

Vehicle Type

"Other" combines 6 smaller categories (7 records): Single-unit truck (>= 3 axles) (2), Train (1), Golf cart (1), Motor home/recreational vehicle (1), Other light truck (<=10000 lbs) (1), Cargo/panel van (1).

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

Traffic Control Device

A significant majority of crashes, 109 incidents, occurred in areas where no traffic controls were present. For crashes that did happen at controlled locations, stop signs were the most common traffic control device, noted in 22 incidents. Crashes at locations with traffic signals (6 incidents) or railway crossing devices (7 incidents) were less frequent.

Traffic Control Device

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

Most Damaged Area

The front of the vehicle was the most common area of initial impact, recorded as the most damaged area for 38 vehicles. Rear impacts were noted in 16 cases, suggesting a notable share of rear-end type collisions. Impacts to the front-driver side corner (14 vehicles) and various points along the driver and passenger sides were also frequently reported.

Most Damaged Area

"Other" combines 8 smaller categories (34 records): Driver side - middle (6), Passenger side - middle (6), Passenger side - rear (6), Rear - driver side corner (6), Driver side - rear (5), Rear - passenger side corner (2), Other (explain in narrative) (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

Within Monroe County, the city of Albia recorded the highest number of crashes with 48 incidents. The town of Lovilia reported 7 crashes. A large number of crashes in the county occurred outside of any specific city's limits and are therefore not included in this municipal breakdown.

Crashes by City

1
ALBIA48 (87.3%)
2
LOVILIA7 (12.7%)

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

Paved vs Unpaved Road

Of the crashes where the road surface type was specified, 111 occurred on paved roads. A smaller but significant number, 16 crashes, took place on unpaved surfaces such as gravel or dirt roads. This represents 12.6% of the 127 crashes with available surface data, highlighting the role of the county's secondary road network in traffic incidents.

Paved vs Unpaved Road

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

Driver Condition

While most drivers were recorded as 'apparently normal,' the data specifies conditions for a subset of individuals. Among these, driving under the influence of alcohol was the most cited condition, noted for 5 drivers. Additionally, 2 drivers were identified as being asleep or fatigued at the time of their crash.

Driver Condition

1
Under the influence of alcohol5 (50%)
2
Asleep/fatigued2 (20%)
3
Hearing impaired/deaf1 (10%)
4
Illness/fainted1 (10%)
5
Medical condition (seizure, reaction)1 (10%)

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

Property Damage

The estimated property damage for the majority of crashes fell into the '$1,500 - $7,500' range, which applied to 106 of the 128 incidents (82.8%). More severe damage was less common, with 16 crashes estimated between $7,500 and $25,000, and only 3 crashes exceeding $25,000 in property damage.

Property Damage

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

Manner of Collision

Single-vehicle incidents were the most common manner of collision, accounting for 58 of the 128 total crashes (45.3%). Among multi-vehicle crashes, broadside collisions were the most frequent type, with 26 incidents (20.3%), followed by rear-end collisions, which occurred 15 times (11.7%).

Manner of Collision

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 simply moving straight ahead, which was the case in 110 instances. The next most frequent pre-crash action was turning left, recorded for 11 vehicles. Backing and being stopped in traffic were also noted, with 7 and 6 instances respectively.

Pre-Crash Driver Action

1
Movement essentially straight110 (69.2%)
2
Turning left11 (6.9%)
3
Backing7 (4.4%)
4
Stopped in traffic6 (3.8%)
5
Turning right5 (3.1%)
6
Slowing/stopping (deceleration)5 (3.1%)
7
Legally Parked5 (3.1%)
8
Making U-turn2 (1.3%)
9
Leaving a parked position2 (1.3%)

Showing top 9 of 14 reported. 5 additional (6 total) not shown: Other (explain in narrative), Negotiating a curve, Entering traffic lane (merging), Overtaking/passing, Entering a parked position.

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

Person Type

Of the 223 people involved in crashes, the overwhelming majority were drivers, accounting for 212 individuals (95.1%). Passengers made up a small fraction of the total, with 10 individuals (4.5%). Only one pedestrian was involved in a crash during this period.

Person Type

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

Person Injury Severity

Out of 223 individuals involved in crashes, 38 people were either injured or killed. This includes 2 fatalities and 36 individuals who sustained injuries ranging from possible to serious. The remaining 185 people involved in these incidents were not reported as injured.

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 limited available data for safety equipment usage, 22 occupants were reported as using a shoulder and lap belt. Of the 26 individuals for whom restraint use was documented, 3 were reported as using no safety equipment. One person was recorded as wearing a DOT-compliant helmet.

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 type of incident, accounting for 76 of the 128 total crashes (59.4%). Two-vehicle collisions were the next most frequent, with 49 incidents (38.3%). Multi-vehicle pile-ups involving three or more vehicles were rare, with only three such crashes recorded.

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: 128
  • Total persons involved: 223
  • Total vehicles involved: 184

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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