ThatCarHitMe.com
An Injuria.ai Company
CRASH INTELLIGENCE REPORT · IOWA, IA · 2015
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/iowa/statewide/2015-annual-report
Yearly Traffic Safety Analysis
119 CRASHES IN
IOWA, IA
2015
In 2015, Lucas County recorded 119 traffic crashes, resulting in 56 injuries and zero fatalities. A significant portion of these incidents were single-vehicle events, with the most common contributing factor being collisions with animals. These animal-related crashes accounted for 55 of the total 119 incidents, representing 46.2% of all crashes in the county for the year.
119
Total Crash Events
0
Persons Killed
56
Persons Injured
0
Fatal Crash Events
Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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
All 56 injuries recorded in Lucas County in 2015 were sustained by motorists. There were no fatalities among any group. Furthermore, no crashes during this period resulted in injuries or fatalities to pedestrians or cyclists.
0
Motorists Killed
56
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 Lucas County occurred most frequently on Tuesdays and Thursdays, with each day recording 20 incidents. The evening commute period was a distinct peak, with the single hour from 5 p.m. to 6 p.m. accounting for 15 crashes. A notable monthly spike occurred in November, which saw 28 crashes, significantly more than any other month.
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 119 total crashes, 59.7% (71 crashes) resulted in no injuries, while the remaining 40.3% (48 crashes) involved at least one injury. These injury crashes included 6 with serious injuries, 18 with minor injuries, and 24 with possible injuries. There were no fatal crashes recorded in Lucas County during this period, and consequently, zero fatalities.
Outcome by Severity (Crash Events)
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 dominant contributing factor to crashes was animals in the roadway, cited in 55 incidents, or 46.2% of the total. Following distantly were factors such as 'Driving too fast for conditions' and 'Lost Control,' each contributing to 8 crashes (6.7% each). Failure to yield from a stop sign was also a notable factor, listed in 6 crashes (5.0%).
Officer-Reported Primary Contributing Cause
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 63.9% (76 crashes) happening in clear weather and 60.5% (72 crashes) on dry road surfaces. Regarding lighting, 43.7% of crashes (52 incidents) took place during daylight hours. However, a notable 32.8% of crashes (39 incidents) occurred after dark on unlighted roadways.
Weather
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Weather condition at time of crash
Lighting
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Lighting condition field
Road Surface
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Road surface condition field
Vehicles & Demographics
Among persons involved in crashes with a known age, the 45-54 age group was the most represented, with 41 individuals. The most common vehicle makes involved in collisions were Chevrolet (38 vehicles), Ford (25 vehicles), and Dodge (13 vehicles). These figures represent the frequency of involvement in crashes and not market share.
Top Vehicle Makes (162 vehicles)
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records
4 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (155 persons with recorded sex)
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Person-level records linked to crash events
Major Cause
Collisions with animals were the leading major cause of crashes, accounting for 55 incidents. Other primary causes included driving too fast for conditions and losing control, each cited in 8 crashes. Failure to yield the right-of-way from a stop sign was the cause of 6 crashes.
Major Cause
Showing top 9 of 25 reported. 16 additional (22 total) not shown: Passing: Other passing (explain in narrative), Exceeded authorized speed, Followed too close, Ran Stop Sign, Failed to keep in proper lane, Other (explain in narrative): Other, Ran Traffic Signal, Driver Distraction: Manual operation of an electronic communication device, Failure to signal intentions, FTYROW: At uncontrolled intersection, Driver Distraction: Inattentive/lost in thought, FTYROW: From yield sign, Improper Backing, Crossed centerline (undivided), Other (explain in narrative): Vision obstructed, Cargo/equipment loss or shift.
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records
First Harmful Event
The most common first harmful event was a collision with an animal, which initiated 54 crashes. The second most frequent event was a collision with another vehicle in traffic, occurring in 31 incidents. Non-collision events, such as an overturn or rollover, were the first harmful event in 7 crashes.
First Harmful Event
Showing top 9 of 14 reported. 5 additional (5 total) not shown: Collision with fixed object: Fence, Non-collision events: Other non-collision (explain in narrative), Collision with fixed object: Culvert/pipe opening, Non-collision events: Vehicle went airborne, Collision with fixed object: Bridge/bridge rail parapet.
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records
Roadway Junction / Feature
The majority of crashes, 73 incidents, did not occur at or near an intersection or junction. Four-way intersections were the most common junction type for crashes, accounting for 19 incidents. Driveways and interchange ramps were each associated with a small number of crashes.
Roadway Junction / Feature
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 75 units recorded. Light trucks and pickups were the second most frequent with 35 vehicles, followed by sport utility vehicles (SUVs) with 26. There were also 5 tractor-trailers and 3 motorcycles involved in crashes during this period.
Vehicle Type
"Other" combines 5 smaller categories (7 records): Single unit truck (2-axle, 6-tire) (2), Single-unit truck (>= 3 axles) (2), Truck/trailer (1), Truck tractor (bobtail) (1), Farm equipment (explain in narrative) (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, totaling 120 incidents, occurred on road segments where no traffic controls were present. Stop signs were the most common form of traffic control noted at crash locations, present in 13 cases. Work zone signage was a factor in 4 crashes.
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 impact, recorded as the most damaged area for 40 vehicles. Frontal impacts also included 18 incidents damaging the driver-side front and 15 damaging the front passenger-side corner. Rear-end collisions were indicated by 10 vehicles having the rear as the most damaged area.
Most Damaged Area
"Other" combines 9 smaller categories (32 records): Other (explain in narrative) (7), Driver side - rear (6), Driver side - middle (6), Top (5), Passenger side - rear (3), Non-collision/no damage (2), Rear - passenger side corner (1), Undercarriage (1), Rear - driver side corner (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 municipal limits occurred in Chariton, which recorded 33 incidents. The town of Lucas had the next highest number with 8 crashes. Other municipalities, including Derby and Russell, each recorded fewer than 5 crashes.
Crashes by City
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 surfaces, which accounted for 102 incidents. However, a notable 17 crashes, representing 14.3% of the total, took place on unpaved gravel or dirt roads within the county.
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, adverse surface conditions such as wet or icy pavement were the most common, contributing to 10 incidents. Other factors like an obstruction in the roadway or a work zone were each noted in only one crash.
Roadway Contributing Factor
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records
Property Damage
The most frequent estimated property damage cost was in the $1,500 to $7,500 range, which applied to 81 crashes. Nine crashes resulted in severe property damage estimated at over $25,000. Another 20 crashes fell into the $7,500 to $25,000 damage category.
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 dominant manner of collision, accounting for 72 crashes or 60.5% of the total. Among multi-vehicle crashes, broadside (front-to-side) collisions were the most common type with 14 incidents, followed by rear-end collisions with 10 incidents.
Manner of Collision
"Other" combines 1 smaller categories (2 records): Other (explain in narrative) (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 drivers were moving straight ahead prior to their crash, with this action recorded for 112 vehicles. Turning left was the next most common pre-crash action, noted for 10 vehicles. Five vehicles were stopped in traffic just before being involved in a collision.
Pre-Crash Driver Action
Showing top 9 of 12 reported. 3 additional (3 total) not shown: Negotiating a curve, Making U-turn, Other (explain in narrative).
Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records
Person Type
Of the 219 individuals involved in crashes, the overwhelming majority, 212 people, were drivers. The remaining 7 individuals were passengers in vehicles. No pedestrians or cyclists were recorded as being involved in any crashes 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
A total of 56 individuals sustained injuries in crashes. Of these, 6 people suffered serious injuries, 22 had minor injuries, and 28 were recorded with possible injuries. These figures represent the total count of injured persons across all 119 crashes.
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, 42 occupants were recorded as using a shoulder and lap belt. In contrast, 3 individuals were noted as having used no safety equipment at the time of their crash.
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, accounting for 78 of the 119 total incidents (65.5%). Crashes involving two vehicles occurred 40 times. Additionally, there was one incident reported that involved four 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: 119
- Total persons involved: 219
- Total vehicles involved: 162
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
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Iowa Crash Data · ArcGIS
Period: 2015-01-01 – 2015-12-31
Generated: September 9, 2026 · All rights reserved