A Map-Matching Algorithm for Ground Movement Trajectory Representation using A-SMGCS Data

2022 Winter Simulation Conference (WSC)

et al. 2022

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Conflicts between taxiing aircraft are resolved by making the aircraft with lower priority wait, slow down, or change their path. Prevalent priority assignment is based on rules such as First Come First Serve. However, this is not viable as priority assignment done by an air-traffic controller (ATC) based on multiple factors. Thus, a machine learning approach is proposed to mimic an ATC's priority assignment. Firstly, the potential conflict scenarios between two aircraft from historical data, which are resolved, are detected and extracted. Then a Random Forest model is developed to learn ATC's behaviors. The model mimics ATC's behavior with an accuracy of 89% and can thus be an effective approach for priority assignment in path-planning and conflict resolution. Further analysis indicates that features such as unimpeded time difference, distance to destination and start, and speed are major considerations that affect the ATC's decisions.

Section: Methodsmentioning

confidence: 99%

Modelling Aircraft Priority Assignment by Air Traffic Controllers During Taxiing Conflicts Using Machine Learning

Duggal

Tran

2022 Winter Simulation Conference (WSC)

et al. 2022

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“…Thus, the first pre-processing step is to apply a map-matching algorithm to standardize the trajectory data and remove noise and outliers. The applied map-matching algorithm (adopted from author's previous work [47]) includes two steps -spatial matching and temporal map-matching. In spatial matching, the tracking positions of the aircraft are matched with the airport-airside network to obtain a taxi-route that is the graph representation of aircraft movement as a sequence of taxi segments.…”

Section: Data Pre-processingmentioning

confidence: 99%

A Generative Adversarial Imitation Learning Approach for Realistic Aircraft Taxi-Speed Modeling

IEEE Trans. Intell. Transport. Syst.

Tran

Alam

et al. 2022

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Classical approaches for modelling aircraft taxispeed assume constant speed or use a turning rate function to approximate taxi-timings for taxiing aircraft. However, those approaches cannot predict spatio-temporal component of aircrafttaxi trajectory due to a lack of consideration of the complexity and stochasticity of airport-airside movements and interactions. This research adopts the Generative Adversarial Imitation Learning (GAIL) algorithm for aircraft taxi-speed modelling, while considering multiple operational factors including surrounding traffic on the ground and target take-off time. The proposed model can learn and reproduce the ground movement patterns in a real-world dataset under different circumstances. In addition, the characteristics of the taxi-speed model are also analyzed, especially focusing on handling conflict scenarios with surrounding traffic. Finally, the travel-time of the aircraft from starting to target positions are compared with baseline models and actual taxiing data. The proposed model outperforms all the baseline models with a significant margin. In terms of spatial completion (SC), it achieves up to 97.1% for arrivals and 88.3% for departures. The results also show significantly high performance for temporal completion. The model achieves a stable performance with low Root Mean Square Error (RMSE) (16.8 seconds for arrivals, 32.4 seconds for departures) and Mean Absolute Percentage Error (MAPE) (4.4% for arrivals and 7.6% for departures). Our model's errors are 72% lower for arrivals and 48% lower for departures when compared to other baseline models.

“…Each trajectory represents aircraft movements in spatial and temporal domains. It is, however, observed that the raw data contains a lot of noise (irregularity in positional coordinates over time) and missing information which is pre-processed, map matched and cleaned [7], [39], [40]. Post cleaning, 60% of the data remains which is used for simulation purposes (refer TABLE I).…”

Section: B Extracting Aircraft Taxi Routes Using A-smgcs Datamentioning

confidence: 99%

“…The airside network is represented by graph G(N, E) [39] where edges E represent taxiways (and runways) that are connected at nodes N . The graph G(N, E) in this study is constructed from public OpenStreeet map data [41] following the methodology outlined in [39], [40]. The resulting Changi Airport graph contains 518 nodes and 1686 edges (refer Fig.…”

Section: A Airside Networkmentioning

confidence: 99%

A Deep Reinforcement Learning Approach for Airport Departure Metering Under Spatial–Temporal Airside Interactions

Ali

IEEE Trans. Intell. Transport. Syst.

Alam

et al. 2022

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