OnlineAirTrajClus: An Online Aircraft Trajectory Clustering for Tarmac Situation Awareness

Shao, Wei; Salim, Flora D.; Chan, Jeffrey; Qin, A. K.; Ma, Jiaman; Feest, Bradley

doi:10.1109/percom.2019.8767400

Cited by 9 publications

(7 citation statements)

References 36 publications

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“…Therefore, a proper pre-processing procedure is required. To this end, we apply the methods mentioned in [13] to make the datasets ready for the next stage. More specific details will be discussed in the following section 5.1.…”

Section: Methodsmentioning

confidence: 99%

“…In this context, Internet of Thing (IoT) -related techniques such as sensor networks and big data analytics have the potential to provide a scalable solution. Increasing works are exploring sensor networks in the air traffic area [11,12,13]. They either use the sensor data to estimate the traffic at the airport or monitor meteorological parameters.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map

Shao,

Prabowo,

Zhao

et al. 2021

Preprint

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To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor data, if modelled correctly, can be used to generate a situational awareness map. Existing techniques apply traditional supervised learning methods onto historical data, contextual features and route information among different airports to predict flight delay are inaccurate and only predict arrival delay but not departure delay, which is essential to airlines. In this paper, we propose a vision-based solution to achieve a high forecasting accuracy, applicable to the airport. Our solution leverages a snapshot of the airport situational awareness map, which contains various trajectories of aircraft and contextual features such as weather and airline schedules. We propose an endto-end deep learning architecture, TrajCNN, which captures both the spatial and temporal information from the situational awareness map. Additionally, we reveal that the situational awareness map of the airport has a vital impact on estimating flight departure delay. Our proposed framework obtained a good result (around 18 minutes error) for predicting flight departure delay at Los Angeles International Airport.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map

Shao,

Prabowo,

Zhao

et al. 2021

Preprint

Self Cite

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show abstract

“…In the first stage, we collect historical data, weather condition data, and tarmac aircraft and vehicles GPS data from different data sources. Since the collected data is noisy, incomplete and redundant, we apply the methods used in [14] to clean and pre-process the data.…”

Section: Methodsmentioning

confidence: 99%

Flight Delay Prediction using Airport Situational Awareness Map

Shao

Prabowo

Zhao

et al. 2019

Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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The prediction of flight delays plays a significantly important role for airlines and travellers because flight delays cause not only tremendous economic loss but also potential security risks. In this work, we aim to integrate multiple data sources to predict the departure delay of a scheduled flight. Different from previous work, we are the first group, to our best knowledge, to take advantage of airport situational awareness map, which is defined as airport traffic complexity (ATC), and combine the proposed ATC factors with weather conditions and flight information. Features engineering methods and most state-of-the-art machine learning algorithms are applied to a large real-world data sources. We reveal a couple of factors at the airport which has a significant impact on flight departure delay time. The prediction results show that the proposed factors are the main reasons behind the flight delays. Using our proposed framework, an improvement in accuracy for flight departure delay prediction is obtained.

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“…Specifically, various data mining (e.g., segmentation 35 , clustering 36,37 ) and modelling techniques [38][39][40] could be applied to build prediction models for measuring occupants' mental state with the sensor-based physiological and behaviour recordings in buildings. This could be further used for various applications, such as monitoring signs of frustration and disengagement 20,41 , proving better seating arrangements 42 , improving teaching strategies by measuring the emotional climate in classrooms 43 , ventilating the classrooms timely to prevent excessive carbon dioxide from affecting students' concentration [44][45][46] , providing a thermally comfort environments for both students and staffs 47 , etc.…”

Section: Usage Notesmentioning

confidence: 99%

Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables

Gao¹,

Marschall²,

Burry³

et al. 2021

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We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia. The data capture contained two elements: First, a 5-month longitudinal field study In-Gauge using two outdoor weather stations, as well as indoor weather stations in 17 classrooms and temperature sensors on the vents of occupant-controlled room air-conditioners; these were collated into individual datasets for each classroom at a 5-minute logging frequency, including additional data on occupant presence. The dataset was used to derive predictive models of how occupants operate room air-conditioning units. Second, we tracked 23 students and 6 teachers in a 4-week cross-sectional study En-Gage, using wearable sensors to log physiological data, as well as daily surveys to query the occupants' thermal comfort, learning engagement, emotions and seating behaviours. This is the first publicly available dataset studying the daily behaviours and engagement of high school students using heterogeneous methods. The combined data could be used to analyse the relationships between indoor climates and mental states of school students.

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OnlineAirTrajClus: An Online Aircraft Trajectory Clustering for Tarmac Situation Awareness

Cited by 9 publications

References 36 publications

Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map

Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map

Flight Delay Prediction using Airport Situational Awareness Map

Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables

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