A Deep Learning Approach for Short-Term Airport Traffic Flow Prediction

Zhen, Yan; Yang, Hongyu; Li, Fan; Lin, Yi

doi:10.3390/aerospace9010011

Cited by 18 publications

(12 citation statements)

References 30 publications

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“…LSTM has four gates, each for a different task. LSTM has a feedback mechanism and produces good results for classification tasks [ 53 ]. LSTM is used for an embedding layer with dimensions of 5000 and 100.…”

Section: Methodsmentioning

confidence: 99%

A Performance Comparison of Classification Algorithms for Rose Plants

Malik

Aslam

Nasr

et al. 2022

Computational Intelligence and Neuroscience

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One of the key roles of Botanists is to be able to recognize flowers. This role has become highly challenging given that the number of discovered flower types are nearing half a million. To support Botanists, Information Technology offers promising solutions. Specifically, machine learning techniques are intrinsically appealing due to being precise enough as required. To this aim, two observations on flower leaves are relevant and leverage flower identification: one, flower plants exhibit unique features in their leaves thus allow distinction of their co-located flowers; two, leaves have a much longer life than flowers thus preserve identity properties longer. This paper proposes the use of machine learning-based identification of rose types by leveraging the features from their leaves. For this purpose, the performance of Naive Bayes, Generalized Linear Model, Multilayer Perceptron, Decision Tree, Random Forest, Gradient Boosted Trees, and Support Vector Machine has been analyzed. This study optimizes the RF model by investigating and tuning its various parameters such as the number of trees, the depth of trees, and splitting criteria. The best results are achieved with gain ratio because it takes more distinct values to avoid the problems associated with Information Gain. Optimizing the number of trees and the depth of trees of RF yield better accuracy than other models. Extensive experiments are performed to analyze the results of ensemble algorithms by using the voting method for each instance. Results suggest that the performance of ensemble classifiers is superior to that of individual models.

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Section: Methodsmentioning

confidence: 99%

A Performance Comparison of Classification Algorithms for Rose Plants

Malik

Aslam

Nasr

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…As such, the models associated with this approach are built on data-driven techniques such as data analytics and visualization [43][44][45][46][47], as well as machine learning [48][49][50][51][52]. The machine learning-based techniques are especially useful for trajectory prediction [53][54][55]. In particular, unsupervised clustering schemes are typically employed to establish the approximate layout of approach paths from different arrival directions [56][57][58].…”

Section: A Existing Approaches To Arrival Traffic Modelingmentioning

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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“…The main innovation of the method is that the uncertainty of future aircraft locations is explicitly taken into account when assessing complexity. In order to effectively predict the dynamic capacity of the terminal area, Yang et al [34,35] proposed a dynamic capacity prediction model under the influence of dangerous weather.…”

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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A Deep Learning Approach for Short-Term Airport Traffic Flow Prediction

Cited by 18 publications

References 30 publications

A Performance Comparison of Classification Algorithms for Rose Plants

A Performance Comparison of Classification Algorithms for Rose Plants

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

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

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