Trajectory pattern identification and classification for real-time air traffic applications in Area Navigation terminal airspace

Deng, Chuhao; Choi, Hong-Cheol; Park, Hyunsang; Hwang, Inseok

doi:10.1016/j.trc.2022.103765

Cited by 14 publications

(3 citation statements)

References 26 publications

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“…Williams et al [1,2,7] used the ATMAP algorithm to quantify the weather data of METAR messages. They studied the change law of key features such as demand and actual flow caused by different weather [8][9][10]. In terms of air traffic-related classification forecasting, single variable forecasting of air traffic that reflects the traffic situation has solved part of the problem of forecasting the operational features of airports.…”

Section: Related Workmentioning

confidence: 99%

Terminal Traffic Situation Prediction Model under the Influence of Weather Based on Deep Learning Approaches

et al. 2022

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In order to quantify the degree of influence of weather on traffic situations in real time, this paper proposes a terminal traffic situation prediction model under the influence of weather (TSPM-W) based on deep learning approaches. First, a feature set for predicting traffic situations is constructed based on data such as weather, traffic demand, delay conditions, and flow control strategies. When constructing weather data, a terminal area weather quantification method (TAWQM) is proposed to quantify various weather feature values. When constructing the traffic situation label, fuzzy C-means clustering (FCM) is used to perform cluster analysis on the traffic situation, and the traffic situation is marked as bad, average, or good. Accordingly, the multi-source data is fused as the input vector, based on the combined prediction model of convolutional neural network (CNN) and gated recurrent unit (GRU), TSPM-W is constructed. Finally, based on the historical operation data of the Guangzhou Baiyun International Airport terminal area, the proposed data set is used to predict the traffic situation time series at intervals of 1 h, 3 h, and 6 h. The comparative experimental results show that the proposed time series prediction model has higher prediction accuracy than other existing prediction methods. The proposed dataset is able to more accurately predict the traffic situation in the terminal area.

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Section: Related Workmentioning

confidence: 99%

Terminal Traffic Situation Prediction Model under the Influence of Weather Based on Deep Learning Approaches

et al. 2022

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“…Yet, for trajectories with clearly specific classes, the results lack intuitiveness and require manual empirical judgement. In recent years, deep learning methods including long short-term memory (LSTM) [25] and bi-directional LSTM networks [26] have gained significant attention in trajectory clustering. These methods encode trajectories and transform them into feature vectors.…”

Section: Introductionmentioning

confidence: 99%

A New Trajectory Clustering Method for Mining Multiple Periodic Patterns from Complex Oceanic Trajectories

Du,

Chen,

et al. 2024

Remote Sensing

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Oceanic trajectories frequently exhibit multiple periodic patterns across various time intervals, e.g., tidal variations, mesoscale eddies, and El Niño events correspond to diurnal, seasonal, and interannual fluctuations in environmental factors. To explore hidden spatiotemporal multiple periodic behaviors in noisy ocean data, we propose a novel trajectory clustering method, namely DTID-STFC. It first identifies dense time intervals (DTIs) in which trajectories occur frequently. Subsequently, within each DTI, it utilizes spectral embedding to project trajectories onto a latent subspace and proposes three-way fuzzy clustering to obtain results. We evaluate the proposed method on simulated datasets and compare it with traditional and state-of-the-art trajectory clustering approaches. Experimental results indicate that it outperforms other methods across all five metrics. Moreover, when applying the DTID-STFC method to the analysis of mesoscale cyclonic eddies in the South China Sea and vessel data, it demonstrates more discernible results than traditional methods, and it aligns well with physical oceanographic processes. This proposed method offers valuable insights into identifying periodic behaviors from complex and noisy spatiotemporal oceanic trajectory data.

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“…Moreover, the proposed benefits of RNAV include smoother trajectories, reduction in fuel consumption, potential reduction in ATC workload [31][32][33], and the satisfaction of PBN requirements [34]. Waypoints play an important role in the facilitation of smooth trajectories and flexibility in transit in RNAV.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Terminal Area Navigation and Conventional Standard Arrival Routes with Cellular Automata

Ogedengbe

Wong

Liem

2023

AIAA AVIATION 2023 Forum

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Aircraft arrival at airports is limited by the physical configuration and layout of the terminal maneuvering area (TMA), which might result in a bottleneck. In a bottleneck situation, navigation is made difficult because arriving aircraft will need special considerations to manage congestion and guarantee safety. In addition, the operating cost of airline operators increases with higher fuel consumption encountered in the extra airborne time. Likewise, passengers incur more travel time with the additional arrival delays. Traditional arrival adopts conventional standard arrival route (STAR), with trajectories that are rigidly coordinated by air traffic controller (ATC). However, a more flexible approach to general navigation has also been introduced, namely the area navigation (RNAV), which makes provision for flexibility in aircraft path selection. This flexibility is enabled by abstract navigational elements, namely waypoints that are unlike conventional navigational aids (NAVAIDs). We look at a modeling framework that juxtaposes the key elements of the traditional STAR (procedure-based) navigation and RNAV (performance-based) navigation. We adopt the cellular automata (CA) framework that has the capability to model the dynamic interaction between aircraft, inherent environmental uncertainties, and arrival route as defined by waypoints. To this end, we develop a modeling environment that enables a fair comparison between these two approaches under similar conditions to determine their respective viability. The effect of the number of waypoints in the TMA under different traffic conditions is illustrated using the relationship between TMA occupancy, traffic flow, and the landing rate. Results show that RNAV navigation exhibits more sensitivity to waypoint distribution within the TMA in terms of throughput, compared to STAR navigation. STAR navigation, on the other hand, demonstrates inherent latency that allows for higher capacities, albeit at the risk of initiating abrupt jamming when a high number of waypoints are active. Consequently, we are able to establish guidelines that justify the adoption RNAV over STAR and vice versa. Furthermore, we also identify performance disparities between them, and where each approach is desirable.

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Trajectory pattern identification and classification for real-time air traffic applications in Area Navigation terminal airspace

Cited by 14 publications

References 26 publications

Terminal Traffic Situation Prediction Model under the Influence of Weather Based on Deep Learning Approaches

Terminal Traffic Situation Prediction Model under the Influence of Weather Based on Deep Learning Approaches

A New Trajectory Clustering Method for Mining Multiple Periodic Patterns from Complex Oceanic Trajectories

A Comparative Analysis of Terminal Area Navigation and Conventional Standard Arrival Routes with Cellular Automata

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