NYC02LA010
2001-10-16 · Northampton, Massachusetts, United States · None · 1 aircraft · Status: Completed
Airport 7B2
Aircraft involved
Probable cause & findings
The pilots' failure to maintain airspeed, which resulted in a loss of control and a hard landing.
Factual narrative
On October 16, 2001, about 2000 eastern daylight time, a Cessna 172L, N811P, was substantially damaged while landing at the Northampton Airport (7B2), Northampton, Massachusetts. The certificated private pilot was not injured. Visual meteorological conditions prevailed and an instrument flight rules (IFR) flight plan had been filed for the flight that departed the Taunton Airport, Taunton, Massachusetts. The personal flight was conducted under 14 CFR Part 91. In a written statement, the pilot said he cancelled his IFR flight plan when he had 7B2 in-sight, and then entered the traffic pattern for runway 14, a 3,506-foot long, 50-foot wide, asphalt runway. While on final approach, the pilot's left foot momentarily became caught under the right rudder pedal. The pilot freed his foot and was about 10 to 15 feet above the runway, when the "airspeed got too slow" and the airplane dropped onto the runway. The airplane sustained substantial damage to the landing gear, wings, and engine. The pilot reported about 145 hours of total flight experience, with about 35 hours in make and model. The pilot further stated he did not experience and mechanical problems with the airplane. The winds reported at an airport about 10 miles south-southeast of 7B2, at 1955, were from 110 degrees at 5 knots. The airplane was on approach for landing. According to the pilot, when the airplane was about 10 to 15 feet above the runway, the "airspeed got too slow" and the airplane dropped onto the runway. The airplane sustained substantial damage to the landing gear, wings, and engine. The pilot said he did not experience and mechanical problems with the airplane. Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_2001_NYC02LA010.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket on
data.ntsb.gov ↗.
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Related research
Matched on aircraft type or causal vocabulary (loss of control). All research papers
- 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.
- Semantic Scholar 2024 · Article (International Conference on Networking and Services) Risk Assessment of Loss of Control In-Flight Trajectories for Urban Air Mobility Safety
In Urban Air Mobility (UAM), maintaining a minimum separation between aircraft is a safety requirement, which becomes challenging amidst congested air traffic and off-nominal conditions, such as Loss …
- 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.