A Methodology for Shuttle Scheduling in Airports That Ensures Mitigating Arriving Passenger Congestion Under Uncertain Demand

Akıncılar, Aykan

doi:10.1109/mits.2021.3049359

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…The economic losses caused by flight delays [1] also can be effectively reduced with an accurate prediction of ETA [2], [3]. Moreover, the accurate prediction of arrival time at the waypoint has a major impact on trajectory sequencing improvement and alleviation of traffic congestion [4], [5]. Additionally, the operational efficiency of airports and airlines can benefit from a more accurate ETA prediction [6].…”

Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Neural Network Model for Estimated-Time-of-Arrival Prediction of Flights in a Terminal Maneuvering Area

Chen

et al. 2023

IEEE Intell. Transport. Syst. Mag.

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Affected by the nondeterministic nature of flight trajectories, external environment, and airport operational conditions, the prediction of the Estimated Time of Arrival (ETA) is one of the most challenging tasks for air traffic control in terminal manoeuvring area (TMA). Previous studies lack adequate utilization of the spatial and temporal behaviors embedded in the continuous trajectories. We propose a novel spatial-temporal neural network model for estimating time of arrival (STNN-ETA), which consists of three components: (1) trajectory pattern recognition which classifies historical trajectories into several patterns/clusters; (2) trajectory prediction that predicts a target flight's subsequent positions based on trajectory pattern matching; and (3) the arrival time prediction in which nonlinear function and recurrent units are adopted to capture the spatial-temporal features for the prediction purpose. In the proposed model, we also utilize a spatial attention mechanism and a temporal attention mechanism to focus on important features from radar echo maps and trajectory series respectively, and suppress unnecessary ones for ETA prediction. To validate the effectiveness of the proposed method, we apply it to predict the ETA of flights within the Beijing TMA. Extensive experiments show that STNN-ETA outperforms the stateof-the-art models in terms of the mean absolute error (MAE).

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Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Neural Network Model for Estimated-Time-of-Arrival Prediction of Flights in a Terminal Maneuvering Area

Chen

et al. 2023

IEEE Intell. Transport. Syst. Mag.

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Customized scheduling for shared bus with deadlines

Jin

et al. 2022

Softw Pract Exp

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Public transportation system is one of the most effective ways to conserve energy and reduce carbon emissions. However, the traditional public transportation system does not provide customized service and cannot guarantee the arrival time to destination. To address these issues, we formulate the minimum shared bus scheduling problem to minimize the number of shared buses such that all orders can be completed under constraints of deadlines and capacity of shared bus. We propose the approximation algorithms, S‐MBSA for the shared bus with strong endurance and E‐MBSA for the large‐scale order scenario, to solve the minimum shared bus scheduling problem. We further formulate the constrained maximum revenue shared bus scheduling problem to maximize the revenue under the limited number of shared buses, and propose an approximation algorithm, CMRBSA, to find the shared bus route schedules. Through the extensive simulations, we demonstrate the significant superiority of S‐MBSA and E‐MBSA in terms of number of shared buses. Furthermore, CMRBSA outperforms the benchmark algorithms significantly in terms of revenue.

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Finding Similar Historical Scenarios for Better Understanding Aircraft Taxi Time: A Deep Metric Learning Approach

Zhang

et al. 2023

IEEE Intell. Transport. Syst. Mag.

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A Methodology for Shuttle Scheduling in Airports That Ensures Mitigating Arriving Passenger Congestion Under Uncertain Demand

Cited by 3 publications

References 19 publications

A Spatiotemporal Neural Network Model for Estimated-Time-of-Arrival Prediction of Flights in a Terminal Maneuvering Area

A Spatiotemporal Neural Network Model for Estimated-Time-of-Arrival Prediction of Flights in a Terminal Maneuvering Area

Customized scheduling for shared bus with deadlines

Finding Similar Historical Scenarios for Better Understanding Aircraft Taxi Time: A Deep Metric Learning Approach

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