Yearly Traffic Safety Analysis

158 CRASHES IN
IOWA, IA
2015

In 2015, Franklin County experienced 158 total traffic crashes, resulting in 3 fatalities and 57 injuries. A significant finding in the data is that collisions with animals were the leading contributing factor, accounting for 38 crashes, or 24.1% of the total.

158

Total Crash Events

3

Persons Killed

57

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (3) 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 total, 3 motorists were killed and 55 were injured in crashes. Additionally, one pedestrian and one cyclist were injured. There were no fatalities recorded for pedestrians or cyclists during this period.

0

Pedestrians Killed

0

Cyclists Killed

3

Motorists Killed

1

Pedestrians Injured

1

Cyclists Injured

55

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 Fridays, with 29 incidents recorded, and Mondays, with 28 incidents. The most common time for crashes was the 7 a.m. hour, which saw 17 crashes, followed by the 4 p.m. hour with 16. The majority of collisions, 93 out of 158, 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 158 crashes, 72.2% (114 crashes) resulted in no injuries. The remaining crashes involved possible injuries (15.2%), minor injuries (10.8%), serious injuries (0.6%), or fatalities (1.3%). There were 2 fatal crashes recorded, which resulted in a total of 3 deaths.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.3%
Serious Injury1serious injury crashes0.6%
Minor Injury17minor injury crashes10.8%
Possible Injury24possible injury crashes15.2%
No Injury114no injury crashes72.2%

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 in the roadway, which was noted in 38 incidents (24.1%). Other leading factors included drivers losing control (17 crashes, 10.8%), running off a straight road (12 crashes, 7.6%), and failing to yield the right-of-way at a stop sign (10 crashes, 6.3%).

Officer-Reported Primary Contributing Cause

Animal38 (24.1%)
Lost Control17 (10.8%)
Ran off road - straight12 (7.6%)
FTYROW: From stop sign10 (6.3%)
FTYROW: At uncontrolled intersection9 (5.7%)
Other (explain in narrative): Other9 (5.7%)
Ran off road - left8 (5.1%)
FTYROW: Making left turn7 (4.4%)
Driving too fast for conditions6 (3.8%)
FTYROW: Other (explain in narrative)4 (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

The majority of crashes occurred in clear weather and on dry roads. Specifically, 75 crashes (47.5%) happened in clear conditions, and 86 crashes (54.4%) were on dry road surfaces. Collisions in daylight accounted for 93 incidents, representing 58.9% of all crashes.

Weather

Clear75 (57.7%)
Cloudy24 (18.5%)
Snow9 (6.9%)
Rain8 (6.2%)
Fog, smoke, smog7 (5.4%)
Freezing rain/drizzle5 (3.8%)
Blowing Snow2 (1.5%)

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

Lighting

Daylight93 (71.0%)
Dark - roadway not lighted22 (16.8%)
Dark - roadway lighted7 (5.3%)
Dawn5 (3.8%)
Dusk4 (3.1%)

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

Road Surface

Dry86 (66.2%)
Wet16 (12.3%)
Snow14 (10.8%)
Ice/frost8 (6.2%)
Gravel4 (3.1%)
Other (explain in narrative)1 (0.8%)
Slush1 (0.8%)

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

Vehicles & Demographics

Among the 307 individuals involved in crashes, the most represented age groups were 26-34 years old (56 people) and 55-64 years old (49 people). The most frequent vehicle makes involved were Chevrolet (50 vehicles), Ford (48 vehicles), and Toyota (13 vehicles).

Top Vehicle Makes (246 vehicles)

1
FORD48 (19.5%)
2
CHEV28 (11.4%)
3
CHEVROLET22 (8.9%)
4
TOYOTA13 (5.3%)
5
DODG12 (4.9%)
6
DODGE10 (4.1%)
7
FREIGHTLINER9 (3.7%)
8
BUIC8 (3.3%)
9
GMC8 (3.3%)
10
NR7 (2.8%)

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

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

Sex Distribution (190 persons with recorded sex)

Male114 (60.0%)
Female76 (40.0%)

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 for crashes was contact with an animal, accounting for 38 incidents. This was followed by drivers losing control (17 crashes), running off a straight road (12 crashes), and failing to yield the right-of-way from a stop sign (10 crashes).

Major Cause

1
Animal38 (24.8%)
2
Lost Control17 (11.1%)
3
Ran off road - straight12 (7.8%)
4
FTYROW: From stop sign10 (6.5%)
5
FTYROW: At uncontrolled intersection9 (5.9%)
6
Other (explain in narrative): Other9 (5.9%)
7
Ran off road - left8 (5.2%)
8
FTYROW: Making left turn7 (4.6%)
9
Driving too fast for conditions6 (3.9%)

Showing top 9 of 29 reported. 20 additional (37 total) not shown: FTYROW: Other (explain in narrative), Other (explain in narrative): No improper action, Followed too close, Ran Stop Sign, Driver Distraction: Adjusting devices (radio, climate), Failed to keep in proper lane, Driver Distraction: Reaching for object(s)/fallen object(s), Improper Backing, Driver Distraction: Other interior distraction, Improper or erratic lane changing, Cargo/equipment loss or shift, Made improper turn, FTYROW: To pedestrian, Driver Distraction: Manual operation of an electronic communication device, Other (explain in narrative): Vision obstructed, Driver Distraction: Passenger, Crossed centerline (undivided), Exceeded authorized speed, Illegally Parked/Unattended, Swerving/Evasive Action.

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 62 crashes. The second most common event was a collision with an animal, accounting for 38 incidents. Collisions with fixed objects like ditches (12) or parked vehicles (13) were also notable.

First Harmful Event

1
Collision with: Vehicle in traffic62 (39.2%)
2
Collision with: Animal38 (24.1%)
3
Collision with: Parked motor vehicle13 (8.2%)
4
Collision with fixed object: Ditch12 (7.6%)
5
Non-collision events: Overturn/rollover10 (6.3%)
6
Other (explain in narrative)3 (1.9%)
7
Collision with fixed object: Bridge/bridge rail parapet3 (1.9%)
8
Non-collision events: Other non-collision (explain in narrative)2 (1.3%)
9
Miscellaneous events: Hit and run2 (1.3%)

Showing top 9 of 21 reported. 12 additional (13 total) not shown: Collision with: Non-motorist (see non-motorist section - NOT a unit), Collision with: Other non-fixed object (explain in narrative), Collision with fixed object: Other fixed object (explain in narrative), Collision with: Struck/struck by object/cargo/person from other vehicle, Collision with fixed object: Guardrail - face, Miscellaneous events: Fire/explosion, Non-collision events: Jackknife, Collision with fixed object: Fence, Collision with fixed object: Traffic sign support, Collision with fixed object: Curb/island/raised median, Collision with fixed object: Tree, Collision with fixed object: Utility pole/light support.

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

Roadway Junction / Feature

A majority of crashes, 75 incidents, occurred at non-intersection locations. Four-way intersections were the most common type of junction for crashes, accounting for 29 incidents, followed by T-intersections with 7 crashes.

Roadway Junction / Feature

1
Non-intersection: Non-junction/no special feature75 (57.3%)
2
Intersection: Four-way intersection29 (22.1%)
3
Intersection: T-intersection7 (5.3%)
4
Non-intersection: Driveway access (related, not in)6 (4.6%)
5
Non-intersection: Other non-intersection (explain in narrative)4 (3.1%)
6
Non-intersection: Driveway access (within)4 (3.1%)
7
Intersection: Other intersection (explain in narrative)2 (1.5%)
8
Non-intersection: Crossover-related1 (0.8%)
9
Intersection: Intersection with ramp1 (0.8%)

Showing top 9 of 11 reported. 2 additional (2 total) not shown: Intersection: Y-intersection, Interchange-related: On-ramp merge area.

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 100 units. Sport utility vehicles and four-tire light trucks were equally represented with 46 vehicles each. Tractor/semi-trailers were involved in 16 crashes.

Vehicle Type

"Other" combines 5 smaller categories (8 records): Other small bus (seats 9-15) (2), Single unit truck (2-axle, 6-tire) (2), Tractor/doubles (2), School bus (seats > 15) (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, 172, were at locations with no traffic controls present. For crashes where traffic controls were a factor, stop signs were the most common, noted in 25 instances, followed by traffic signals in 8 instances.

Traffic Control Device

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 the primary area of damage being the front of the vehicle in 50 cases. This was followed by damage to the front driver-side corner (23 cases) and the middle of the driver's side (22 cases), indicating a prevalence of head-on and angle collisions.

Most Damaged Area

"Other" combines 9 smaller categories (60 records): Rear - driver side corner (11), Driver side - front (10), Top (10), Passenger side - rear (8), Other (explain in narrative) (8), Driver side - rear (5), Rear - passenger side corner (4), Non-collision/no damage (3), Undercarriage (1).

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

Crashes by City

The majority of crashes within municipal limits occurred in Hampton, which recorded 60 incidents. Sheffield followed with 7 crashes, and Alexander had 1 crash.

Crashes by City

1
HAMPTON60 (88.2%)
2
SHEFFIELD7 (10.3%)
3
ALEXANDER1 (1.5%)

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

Paved vs Unpaved Road

Crashes predominantly occurred on paved roadways, which accounted for 143 incidents. Unpaved surfaces, such as gravel roads, were the location for 15 crashes, representing 9.5% of the total.

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 21 crashes. Other factors like debris, ruts, or work zones were each noted in only one crash.

Roadway Contributing Factor

1
Surface condition (e.g.wet, icy)21 (87.5%)
2
Debris1 (4.2%)
3
Ruts/holes/bumps1 (4.2%)
4
Work Zone (roadway-related)1 (4.2%)

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 100 crashes. There were 49 crashes with damage estimated between $7,500 and $25,000, and 5 crashes with 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 such as running off the road were the most frequent manner of collision, accounting for 63 crashes or 39.9% of the total. Rear-end collisions were the second most common type, with 21 incidents (13.3%), followed by broadside collisions with 17 incidents (10.8%).

Manner of Collision

"Other" combines 3 smaller categories (9 records): Rear to side (5), Sideswipe, opposite direction (3), 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 vast majority of vehicles, 142, were moving straight ahead immediately prior to the crash. The next most common pre-crash actions were being legally parked (23 vehicles) and turning left (18 vehicles).

Pre-Crash Driver Action

1
Movement essentially straight142 (64%)
2
Legally Parked23 (10.4%)
3
Turning left18 (8.1%)
4
Other (explain in narrative)9 (4.1%)
5
Backing8 (3.6%)
6
Turning right7 (3.2%)
7
Slowing/stopping (deceleration)7 (3.2%)
8
Changing lanes2 (0.9%)
9
Entering traffic lane (merging)2 (0.9%)

Showing top 9 of 13 reported. 4 additional (4 total) not shown: Stopped in traffic, Negotiating a curve, Illegally Parked/Unattended, 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 307 people involved in crashes, 292 were drivers. The next largest group was passengers, with 13 individuals. The data also includes one pedestrian and one bicyclist.

Person Type

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

Person Injury Severity

Among all 307 people involved in crashes, 3 sustained fatal injuries and 3 had serious injuries. An additional 22 people had minor injuries and 32 had possible injuries, for a total of 57 individuals 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 available data for a subset of occupants, 43 individuals were recorded as using both a shoulder and lap belt. Four individuals were recorded as using no safety equipment. One person was noted 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

The distribution of crashes was nearly even between single-vehicle incidents (77 crashes) and two-vehicle incidents (74 crashes). There were also 7 crashes that involved three 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: 158
  • Total persons involved: 307
  • Total vehicles involved: 246

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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