Safety

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...

Connecting Surrogate Safety Measures to Crash Probablity via Causal Probabilistic Time Series Prediction

Lu, Jiajian
Grembek, Offer
Mark Hansen
2022

Surrogate safety measures can provide fast and pro-active safety analysis and give insights on the pre-crash process and crash failure mechanism by studying near misses. However, validating surrogate safety measures by connecting them to crashes is still an open question. This paper proposed a method to connect surrogate safety measures to crash probability using probabilistic time series prediction. The method used sequences of speed, acceleration and time-to-collision to estimate the probability density functions of those variables with transformer masked autoregressive flow (transformer...

Robust Management of Airport Security Queues Considering Passenger Non-compliance with Chance-Constrained Optimization

Cao, Shangqing
Kasliwal, Aparimit
Zheng, Huangyi
Reihanifar, Masoud
Robuste, Francesc
Mark Hansen
2025

The long waiting time at airport security has become an emergent issue as demand for air travel continues to grow. Not only does queuing at security cause passengers to miss their flights, but also reduce the amount of time passengers spend at the airport post-security, potentially leading to less revenue for the airport operator. One of the key issues to address to reduce waiting time is the management of arrival priority. As passengers on later flights can arrive before passengers on earlier flights, the security system does not always process passengers in the order of the degree of...

Excess Delay from GDP: Measurement and Causal Analysis

Liu, Ke
Mark Hansen
2025

Ground Delay Programs (GDPs) have been widely used to resolve excessive demand-capacity imbalances at arrival airports by shifting foreseen airborne delay to pre-departure ground delay. While offering clear safety and efficiency benefits, GDPs may also create additional delay because of imperfect execution and uncertainty in predicting arrival airport capacity. This paper presents a methodology for measuring excess delay resulting from individual GDPs and investigates factors that influence excess delay using regularized regression models. We measured excess delay for 1210 GDPs from 33 U.S...