Safety

Analysis of De-peaking Strategies Implemented by American Airlines: Causes and Effects

Zhang, Yu
Menendez, Monica
Mark Hansen
2003

The airline industry in the United States has been one of the hardest-hit (economic victims of the terrorist attacks on September 11, 2001. In an effort to reduce operating cost to better adapt to the decreasing demand, American Airlines (AA) has introduced de-peaking strategies at two of its major hubs: Chicago O’Hare and Dallas/Fort Worth International Airports. In this paper, American Airlines’ market performance before de-peaking implementation is investigated. The results suggest that the differences between the market performance of the whole company and those services going through...

Safety of Efficiency: Link Between Operational Performance and Operational Errors in the National Airspace System

Mark Hansen
Zhang, Yu
2004
Safety and efficiency are two of the most important goals of the national airspace system (NAS). However, these goals are typically viewed as competing or independent, rather than complementary. The complementary aspect of safety and efficiency is illustrated from the point of view of human factors and ergonomics. Statistical methods were used to analyze safety and efficiency performance measurements, operational errors, and daily flight time index (DFTI). Factors related to weather and complexity were found to be more likely on days with poor operational performance as measured by DFTI. The...

Challenges of Measuring Performance for FAA's Safety Oversight System

Mark Hansen
McAndrews, Carolyn
2005
FAA regulates the safety of the aviation industry through the safety oversight system, which is a system of rulemaking, standard-setting, certification, accident investigation, rule enforcement, and surveillance activities. Federal programs, including those of FAA, use performance indicators to measure the achievement of program goals. As part of a broader program of developing risk management methodologies, FAA is researching performance indicators that can be used to measure the performance of the safety oversight system. One of its goals is to create performance indicators that can...

Evaluation of the Performance of Ground Delay Programs

Liu, Yi
Mark Hansen
2014

Ground delay programs (GDPs) are frequently used to keep U.S. air transportation safe and efficient. Most research on GDPs has focused on optimal design and implementation, with little attention given to retrospective performance evaluation. This research fills that gap by identifying GDP performance criteria, developing associated performance metrics, and evaluating GDP performance metrics across airports and over time. GDP performance criteria are established, and associated performance metrics are specified for five performance goals: capacity utilization, efficiency, predictability,...

Multimodal Impact Analysis of an Airside Catastrophic Event: A Case Study of the Asiana Crash

Marzuoli, Aude
Boidot, Emmanuel
Feron, Eric
van Erp, Paul B. C.
Ucko, Alexis
Alexandre Bayen
Mark Hansen
2016

Transportation networks constitute a critical infrastructure enabling the transfers of passengers and goods, with a significant impact on the economy at different scales. Transportation modes, whether air, road, or rail, are intrinsically coupled through passenger transfers and are interdependent. The frequent occurrence of perturbations on one or several modes disrupts passengers' entire journeys, directly and through ripple effects. This paper provides a case report of the Asiana crash in San Francisco International Airport (SFO) on July 6, 2013, and its repercussions on the multimodal...

Real-Time Prediction of Runway Occupancy Buffers

Dai, Lu
Mark Hansen
2020

To improve runway safety and efficiency, real-time prediction of the time separation between successive flights using the same runway would be valuable. In this paper, we develop methods for such predictions, focusing on the time difference between when the prior aircraft exits the runway and the next arriving aircraft crosses the runway threshold, a metric we term runway occupancy buffer. We use two modeling frameworks: a two-stage modeling framework that predicts runway occupancy buffer through prediction of leading aircraft's runway occupancy time and trailing aircraft's required time...

Obstacle Clustering and Path Optimization for Drone Routing

Li, Ang
Mark Hansen
2020

To enable safe and efficient Unmanned Aircraft Systems (UAS) operations at low altitudes, it is necessary to conduct airspace management and operations for UAS traffic. This study focuses on deterministic clustering-based drone routing, with specific emphasis on the trade-off between horizontal and vertical travel costs. The routing problem is simplified to a 2D problem that we solve at several altitude candidates. Altitude candidates were generated based on clustered static obstacles in low urban airspace. Fast-Marching algorithm is performed to generate the shortest path at each altitude...

Sequential Prediction of Go-Around Occurrence

Dai, Lu
Liu, Yulin
Mark Hansen
2022

A go-around is an aborted landing event of an aircraft that is on final approach. Go-arounds are costly and detrimental to safety. Building upon our previous work in go-around detection and analysis of feature contributions, we investigate different learning models and prediction regimes for making sequential predictions of go-around probabilities based on realized trajectory data and environment factors as the aircraft proceeds on its approach. This paper develops and compares the performance of different learning algorithms and prediction strategies for the sequential go-around...

Learning the Representation of Surrogate Safety Measures to Identify Traffic Conflict

Lu, Jiajian
Grembek, Offer
Mark Hansen
2022

Traffic conflict can be identified by the presence of evasive actions or the amount of temporal (spatial) proximity measures like time-to-collision (TTC). However, it is not enough to use only one kind of measures in some scenarios and it is hard to set a threshold for those measures. This paper proposed a method to identify traffic conflict by learning the representation of TTC and driver maneuver profiles with deep unsupervised learning and clustering the representations into traffic conflict and non-conflict clusters. We first trained a transformer encoder to encode sequences of...

Learning the Representation of Surrogate Safety Measures to Identify Traffic Conflict

Lu, Jiajian
Grembek, Offer
Mark Hansen
2022

Traffic conflict can be identified by the presence of evasive actions or the amount of temporal (spatial) proximity measures like time-to-collision (TTC). However, it is not enough to use only one kind of measures in some scenarios and it is hard to set a threshold for those measures. This paper proposed a method to identify traffic conflict by learning the representation of TTC and driver maneuver profiles with deep unsupervised learning and clustering the representations into traffic conflict and non-conflict clusters. We first trained a transformer encoder to encode sequences of...