Yearly Traffic Safety Analysis

433 CRASHES IN
IOWA, IA
2015

In 2015, Cedar County recorded 433 traffic crashes, resulting in 5 fatalities and 100 injuries. These incidents included 4 fatal crashes. The single most common contributing factor cited in these collisions was an animal on the roadway, which was a factor in 101 crashes, representing 23.3% of all incidents.

433

Total Crash Events

5

Persons Killed

100

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (5) 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

Motor vehicle occupants comprised the vast majority of those killed or injured, with 5 motorists killed and 94 injured. There were no fatalities involving pedestrians or bicyclists in 2015. However, 3 pedestrians and 3 bicyclists sustained injuries in traffic collisions.

0

Pedestrians Killed

0

Cyclists Killed

5

Motorists Killed

3

Pedestrians Injured

3

Cyclists Injured

94

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 occurred most frequently on Tuesdays, which saw a total of 75 incidents. The most common time for a crash was during the afternoon commute, with the peak hour being 4 p.m., when 33 crashes occurred. While more crashes happened during daylight hours (205), a significant number also took place in darkness (110).

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, 347 out of 433 (80.1%), resulted in no injuries. There were 82 crashes that involved some level of injury, including 7 with serious injuries, 30 with minor injuries, and 45 with possible injuries. A total of 4 crashes were classified as fatal, which resulted in 5 total fatalities.

Severity is per crash event (most severe injury). 4 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.9%
Serious Injury7serious injury crashes1.6%
Minor Injury30minor injury crashes6.9%
Possible Injury45possible injury crashes10.4%
No Injury347no injury crashes80.1%

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

Collisions with animals were the leading contributing factor, cited in 101 crashes (23.3%). Running off a straight road was the second most common factor, accounting for 63 crashes (14.5%). Other frequently cited factors included losing control (39 crashes), driving too fast for conditions (33 crashes), and following too closely (23 crashes).

Officer-Reported Primary Contributing Cause

Animal101 (23.3%)
Ran off road - straight63 (14.5%)
Lost Control39 (9%)
Driving too fast for conditions33 (7.6%)
Followed too close23 (5.3%)
FTYROW: From stop sign14 (3.2%)
Ran off road - left14 (3.2%)
Swerving/Evasive Action13 (3%)
Driver Distraction: Other interior distraction13 (3%)
Driver Distraction: Inattentive/lost in thought11 (2.5%)

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 plurality of crashes occurred in favorable conditions, with 200 incidents happening in clear weather and 194 on dry road surfaces. Most crashes, 205 in total, also took place during daylight hours. However, adverse conditions were also a factor, with 42 crashes occurring in snow, 33 on icy roads, and 110 in dark or unlit conditions.

Weather

Clear200 (60.4%)
Cloudy43 (13.0%)
Snow42 (12.7%)
Rain17 (5.1%)
Blowing Snow12 (3.6%)
Fog, smoke, smog7 (2.1%)
Freezing rain/drizzle6 (1.8%)
Sleet, hail3 (0.9%)
Severe Winds1 (0.3%)

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

Lighting

Daylight205 (62.1%)
Dark - roadway not lighted88 (26.7%)
Dark - roadway lighted17 (5.2%)
Dusk8 (2.4%)
Dawn7 (2.1%)
Dark - unknown roadway lighting5 (1.5%)

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

Road Surface

Dry194 (58.8%)
Snow45 (13.6%)
Ice/frost33 (10.0%)
Gravel31 (9.4%)
Wet23 (7.0%)
Slush4 (1.2%)

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

Vehicles & Demographics

The analysis of persons involved in crashes shows that younger individuals are most represented, with 107 people in the 21-25 age group and 105 in the 16-20 age group. Among vehicle makes, Ford was the most frequent, with 122 vehicles involved in collisions. This was followed by Chevrolet, which was listed as both 'Chevrolet' (65 vehicles) and 'CHEV' (49 vehicles).

Top Vehicle Makes (611 vehicles)

1
FORD122 (20%)
2
CHEVROLET65 (10.6%)
3
CHEV49 (8%)
4
FREIGHTLINER27 (4.4%)
5
TOYOTA24 (3.9%)
6
DODG20 (3.3%)
7
DODGE19 (3.1%)
8
GMC18 (2.9%)
9
HONDA16 (2.6%)
10
NR12 (2%)

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

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

Sex Distribution (517 persons with recorded sex)

Male325 (62.9%)
Female192 (37.1%)

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 identified in crashes was an animal, accounting for 101 incidents. Running off a straight road was the second-most cited cause with 63 crashes, followed by losing control (39 crashes). Driving too fast for conditions (33 crashes) and following too closely (23 crashes) were also significant contributing causes.

Major Cause

1
Animal101 (24.5%)
2
Ran off road - straight63 (15.3%)
3
Lost Control39 (9.5%)
4
Driving too fast for conditions33 (8%)
5
Followed too close23 (5.6%)
6
FTYROW: From stop sign14 (3.4%)
7
Ran off road - left14 (3.4%)
8
Swerving/Evasive Action13 (3.2%)
9
Driver Distraction: Other interior distraction13 (3.2%)

Showing top 9 of 41 reported. 32 additional (99 total) not shown: Driver Distraction: Inattentive/lost in thought, Ran Stop Sign, Other (explain in narrative): Other, Improper or erratic lane changing, Operating vehicle in an reckless, erratic, careless, negligent manner, Other (explain in narrative): No improper action, FTYROW: From yield sign, FTYROW: At uncontrolled intersection, FTYROW: Other (explain in narrative), Driver Distraction: Exterior distraction, Passing: Other passing (explain in narrative), Ran off road - right, FTYROW: To pedestrian, Improper Backing, FTYROW: From driveway, Traveling wrong way or on wrong side of road, Made improper turn, Driver Distraction: Reaching for object(s)/fallen object(s), Crossed centerline (undivided), Failed to keep in proper lane, Driver Distraction: Passenger, Passing: Where prohibited by signs/markings, Cargo/equipment loss or shift, Equipment failure, Exceeded authorized speed, Failure to signal intentions, FTYROW: Making left turn, Driver Distraction: Adjusting devices (radio, climate), Other (explain in narrative): Vision obstructed, Passing: With insufficient distance/inadequate visibility, Ran Traffic Signal, Driver Distraction: Other electronic device activity.

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 124 crashes. The second-most common event was a collision with an animal, recorded in 100 incidents. Run-off-road events were also prevalent, including 53 collisions with cable barriers and 31 overturns or rollovers.

First Harmful Event

1
Collision with: Vehicle in traffic124 (29%)
2
Collision with: Animal100 (23.4%)
3
Collision with fixed object: Cable barrier53 (12.4%)
4
Non-collision events: Overturn/rollover31 (7.3%)
5
Collision with fixed object: Ditch31 (7.3%)
6
Collision with: Parked motor vehicle19 (4.4%)
7
Non-collision events: Non-contact vehicle (phantom)8 (1.9%)
8
Miscellaneous events: Hit and run6 (1.4%)
9
Collision with: Non-motorist (see non-motorist section - NOT a unit)6 (1.4%)

Showing top 9 of 29 reported. 20 additional (49 total) not shown: Collision with: Other non-fixed object (explain in narrative), Collision with fixed object: Guardrail - face, Collision with fixed object: Utility pole/light support, Collision with: Re-entering roadway, Other (explain in narrative), Collision with: Struck/struck by object/cargo/person from other vehicle, Collision with: Thrown or falling object, Non-collision events: Other non-collision (explain in narrative), Collision with fixed object: Fence, Non-collision events: Jackknife, Collision with fixed object: Building, Collision with fixed object: Bridge/bridge rail parapet, Collision with fixed object: Guardrail - end, Collision with fixed object: Mailbox, Collision with fixed object: Fire hydrant, Collision with: Railway vehicle/train, Collision with fixed object: Embankment, Collision with fixed object: Culvert/pipe opening, Non-collision events: Fell/jumped from vehicle, Collision with fixed object: Traffic sign support.

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

Roadway Junction / Feature

The majority of crashes, 241 in total, occurred on non-intersection road segments. Collisions at intersections were less frequent, with 50 incidents at four-way intersections and 10 at T-intersections. Overall, crashes at driveway access points, interchange ramps, and other junction types were comparatively rare.

Roadway Junction / Feature

1
Non-intersection: Non-junction/no special feature241 (72.4%)
2
Intersection: Four-way intersection50 (15%)
3
Intersection: T-intersection10 (3%)
4
Non-intersection: Driveway access (related, not in)7 (2.1%)
5
Non-intersection: Driveway access (within)6 (1.8%)
6
Intersection: Other intersection (explain in narrative)4 (1.2%)
7
Non-intersection: Other non-intersection (explain in narrative)4 (1.2%)
8
Interchange-related: On-ramp merge area4 (1.2%)
9
Interchange-related: Off-ramp, diverge area3 (0.9%)

Showing top 9 of 13 reported. 4 additional (4 total) not shown: Intersection: L-intersection, Intersection: Intersection with ramp, Non-intersection: Railroad grade crossing, Interchange-related: On-ramp.

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, accounting for 273 vehicles. Light trucks and pickups were the second-most common with 117 vehicles, followed by 86 sport utility vehicles. Notably, 59 tractor/semi-trailers were involved in collisions, representing a significant portion of the vehicle fleet.

Vehicle Type

"Other" combines 12 smaller categories (23 records): Single unit truck (2-axle, 6-tire) (6), Tractor/doubles (3), Truck/trailer (3), Motorcycle (2), Maintenance/construction vehicle (2), Train (1), Motor home/recreational vehicle (1), Farm equipment (explain in narrative) (1), Passenger van (seats 9-15) (1), School bus (seats > 15) (1), Snowmobile (1), Farm tractor (1).

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

Traffic Control Device

The vast majority of vehicles involved in crashes, 432 in total, were at locations with no traffic controls present. For crashes where traffic controls were a factor, stop signs were the most common, present for 44 vehicles. All other forms of control, such as yield signs (6) and traffic signals (3), were far less frequent.

Traffic Control Device

"Other" combines 3 smaller categories (5 records): Traffic signals (3), Railway crossing device (1), Flashing traffic control signal (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, with 125 vehicles sustaining primary damage to the front. Rear impacts were the next most frequent, recorded on 48 vehicles, which corresponds to the number of rear-end collisions. Damage to the front corners (85 vehicles combined) and sides was also frequently reported.

Most Damaged Area

"Other" combines 10 smaller categories (138 records): Driver side - middle (24), Top (21), Passenger side - front (19), Passenger side - rear (18), Other (explain in narrative) (15), Rear - passenger side corner (14), Driver side - rear (13), Undercarriage (8), Non-collision/no damage (4), Cargo loss (2).

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

Impairment (Alcohol / Drugs)

Driver impairment was suspected or confirmed in 19 crashes, representing 4.4% of all collisions. Of these, alcohol was the sole factor in 15 incidents. An additional 3 crashes involved a combination of alcohol and drugs, while 1 crash was attributed to drugs alone.

Crashes by City

Within Cedar County, the city of Tipton recorded the highest number of crashes with 54. The towns of West Branch and Durant followed, each with 12 crashes. Other municipalities, including Lowden (7 crashes) and Stanwood (5 crashes), reported fewer incidents.

Crashes by City

1
TIPTON54 (54%)
2
WEST BRANCH12 (12%)
3
DURANT12 (12%)
4
LOWDEN7 (7%)
5
STANWOOD5 (5%)
6
MECHANICSVILLE4 (4%)
7
CLARENCE3 (3%)
8
BENNETT2 (2%)
9
WILTON1 (1%)

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, 382 incidents, occurred on paved roadways. A notable minority of 48 crashes, representing approximately 11% of the total, took place on unpaved surfaces like gravel or dirt roads. This highlights 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

Roadway Contributing Factor

Adverse surface conditions, such as wet or icy pavement, were the most commonly cited roadway factor, contributing to 70 crashes. Other roadway-related factors were much less common but included debris on the road (6 crashes) and traffic backups from prior incidents (5 crashes). For most crashes, no specific roadway factor was identified.

Roadway Contributing Factor

1
Surface condition (e.g.wet, icy)70 (78.7%)
2
Debris6 (6.7%)
3
Traffic backup, prior crash5 (5.6%)
4
Work Zone (roadway-related)3 (3.4%)
5
Ruts/holes/bumps2 (2.2%)
6
Shoulders (none, low, soft, high)1 (1.1%)
7
Traffic backup, prior non-recurring incident1 (1.1%)
8
Traffic backup, regular congestion1 (1.1%)

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

Driver Condition

Among drivers where a specific condition was noted, being asleep or fatigued was a factor for 11 drivers, equal to the 11 drivers noted as being under the influence of alcohol. Less common conditions included illness or fainting (4 drivers) and being under the influence of drugs or medication (3 drivers). These figures do not include the majority of drivers who were listed as 'apparently normal'.

Driver Condition

1
Asleep/fatigued11 (33.3%)
2
Under the influence of alcohol11 (33.3%)
3
Illness/fainted4 (12.1%)
4
Under the influence of drugs/meds3 (9.1%)
5
Emotional (e.g. depressed, angry)2 (6.1%)
6
Medical condition (seizure, reaction)2 (6.1%)

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 was assigned to 300 crashes. A significant number of incidents resulted in higher costs, with 100 crashes causing between $7,500 and $25,000 in damage. Severe damage, estimated at over $25,000, was recorded for 25 crashes.

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, often involving a vehicle running off the road, were the predominant manner of collision, accounting for 229 incidents (52.9%). Among crashes involving multiple vehicles, rear-end collisions were the most common type with 58 occurrences. This was followed by same-direction sideswipes (39) and broadside collisions (35).

Manner of Collision

"Other" combines 2 smaller categories (8 records): Sideswipe, opposite direction (6), Angle, oncoming left turn (2).

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

Pre-Crash Driver Action

The vast majority of vehicles involved in collisions, 380 in total, were moving straight ahead prior to the event. Other common pre-crash actions included slowing or stopping, which was the case for 23 vehicles, and turning left, for 22 vehicles. An additional 34 vehicles were struck while legally parked.

Pre-Crash Driver Action

1
Movement essentially straight380 (70.8%)
2
Legally Parked34 (6.3%)
3
Slowing/stopping (deceleration)23 (4.3%)
4
Turning left22 (4.1%)
5
Turning right16 (3%)
6
Backing10 (1.9%)
7
Changing lanes10 (1.9%)
8
Overtaking/passing9 (1.7%)
9
Stopped in traffic9 (1.7%)

Showing top 9 of 15 reported. 6 additional (24 total) not shown: Other (explain in narrative), Negotiating a curve, Entering traffic lane (merging), Leaving traffic lane, Illegally Parked/Unattended, Accelerating in road.

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

Person Type

Of the 720 individuals involved in traffic crashes, 691 were drivers of motor vehicles. Passengers accounted for a much smaller group of 23 people. The remaining individuals were vulnerable road users, including 3 pedestrians and 3 bicyclists.

Person Type

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

Person Injury Severity

Across all persons involved in crashes, 5 suffered fatal injuries and 100 sustained non-fatal injuries. Among the injured, 10 were classified as serious, 35 as minor, and 55 as possible injuries. These figures highlight the human toll of the 433 crashes recorded.

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 a subset of crash reports where equipment use was documented, 71 occupants were recorded as using a shoulder and lap belt. In contrast, 8 occupants were noted as having used no safety equipment. This data is not comprehensive for all occupants but indicates patterns where information was available.

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 264 crashes, or 61% of the total. Two-vehicle collisions were the next most frequent, with 162 occurrences. Crashes involving three or more vehicles were uncommon, with only 7 such events recorded in total.

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: 433
  • Total persons involved: 720
  • Total vehicles involved: 611

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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