NTSB CAROL · Event
Event CEN14LA430
Aircraft involved
Probable cause & findings
Failure of the tow bar shear pin, which resulted in the tug operator’s loss of control of the airplane during pushback operations. Contributing to the accident was the tug operator’s inability to communicate to the flight crew to apply the airplane’s brakes.
Factual narrative
On August 9, 2014, about 0735 central daylight time (CDT), a Bombardier CL600 airplane, N943LR, collided with a tug during pushback from the gate at San Antonio International Airport (SAT) San Antonio, Texas. The airplane sustained substantial damage to the fuselage structure and internal damage to nose landing gear. The airplane was registered to and operated by Mesa Airlines Inc. as US Airways flight 2763. The four flight crewmembers and passengers on-board were not injured. Visual meteorological conditions prevailed at the time of the accident and the flight operated on a instrument flight rules flight plan. The scheduled, domestic passenger flight was operated under the provisions of 14 Code of Federal Regulations Part 121. The flight was destined for Phoenix Sky Harbor International Airport (PHX), Phoenix, Arizona. The first officer reported the tug driver did not have an operable headset and the pushback was initiated using hand signals. The airplane was positioned on the ramp at a 90-degree angle to the gate. The tug driver stated after turning the airplane onto the taxiway he "reversed back," pulling the airplane forward. The tug stopped perpendicular to the left nose of the airplane, but the airplane continued to roll forward while still attached to the tow bar. The airplane rolled into the tug impacting the left side of the fuselage. Examination revealed the tow bar shear pin had failed, but the investigation could not determine if the shear pin failed prior to or during the pushback process. In accordance with company policies and procedures, a postaccident drug test of the tug driver was administered about 9 hours after the accident, which was positive for marijuana. According to 49 CFR Part 40 Section 40.87, the initial test cut off is 50 ng/ml, but a positive marijuana test can be reported if the confirmatory test identifies 15 ng/ml or more of marijuana metabolite (tetrahydrocannabinol carboxylic acid, or THC-COOH) in urine. According to the NTSB Medical Officer, about 30% of THC is eventually excreted in urine, primarily as THC-COOH. However, its presence in urine only indicates prior THC exposure. After smoking marijuana, it can take as long as four hours for THC-COOH to appear in the urine at concentrations above the initial reporting cut off of 50 ng/ml. Positive urine test results generally indicate use within hours to a few days; however, the detection window can be significantly longer following chronic, heavy use. During pushback from the gate, the tug positioned the airplane on the taxiway. Before disconnecting, the tug reversed and the airplane rolled forward while still attached to the tug. As the airplane rolled past the tug, the tug impacted the left side of the fuselage. The tow bar pin was found sheared, but it could not be determined if the pin failed before or during the tow operation. Further, the operator did not have an immediate means of communicating with the flight crew the need to apply the brakes while the tug was still attached. The tug operator's postaccident urine test was positive for marijuana, which indicated prior use. However, it could not be determined whether the tug operator was impaired by the effects of marijuana at the time of the event. Source: NTSB Aviation Accident Database Retrieved: 2026-02-12
NTSB Findings
Hierarchical cause / factor breakdown from the FAA bulk avdata database. Each finding tagged C (Cause) or F (Factor).
- C Personnel issues-Task performance-Use of equip/info-Use of equip/system-Ground crew - C
- F Personnel issues-Task performance-Communication (personnel)-Lack of communication-Ground crew - F
Verbatim from NTSB's published report. Source file
NTSB_2014_CEN14LA430.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket on
data.ntsb.gov ↗.
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Related research
What the literature says.
Academic papers and agency reports matching this event's aircraft type or causal vocabulary (loss of control). Sourced from NASA NTRS, NTSB Safety Studies, FAA CAMI, AOPA Air Safety Institute, Embry-Riddle Scholarly Commons, arXiv, and the Semantic Scholar academic graph.
- Embry-Riddle Scholarly Commons 2025 · Journal article (JAAER)
A Scoping Review of Aviation Loss of Control Inflight Research
Loss of control – inflight (LOC-I) contributes to aircraft accidents at unacceptably high rates. Significant industry efforts and research have aimed to improve LOC-I prevention, detection, and recove…
- SKYbrary (Eurocontrol) 2024 · SKYbrary article
Loss of Control In-Flight (LOC-I) — SKYbrary Knowledge Base
SKYbrary comprehensive knowledge-base entry on Loss of Control In-Flight — definitions, contributing factors, accident case studies (Air France 447, Colgan 3407), and prevention strategies.
- NTSB Aircraft Accident Reports 2022 · Accident report
Loss of Control on Takeoff in Icing Conditions — Citation 560XL
Cessna Citation 560XL fatal takeoff icing accident, March 2018. Investigation of a Citation 560XL loss-of-control takeoff accident in icing conditions.
- Semantic Scholar 2021 · Article (Aviation)
ANALYSIS OF GENERAL AVIATION FIXED-WING AIRCRAFT ACCIDENTS INVOLVING INFLIGHT LOSS OF CONTROL USING A STATE-BASED APPROACH
Inflight loss of control (LOC-I) is a significant cause of General Aviation (GA) fixed-wing aircraft accidents. The United States National Transportation Safety Board’s database provides a rich source…
- NASA NTRS 2021 · Presentation
Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft.
- NASA NTRS 2021 · Conference Paper
Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft.
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