Multi-Airport Delay Prediction with Transformers

by   Liya Wang, et al.

Airport performance prediction with a reasonable look-ahead time is a challenging task and has been attempted by various prior research. Traffic, demand, weather, and traffic management actions are all critical inputs to any prediction model. In this paper, a novel approach based on Temporal Fusion Transformer (TFT) was proposed to predict departure and arrival delays simultaneously for multiple airports at once. This approach can capture complex temporal dynamics of the inputs known at the time of prediction and then forecast selected delay metrics up to four hours into the future. When dealing with weather inputs, a self-supervised learning (SSL) model was developed to encode high-dimensional weather data into a much lower-dimensional representation to make the training of TFT more efficiently and effectively. The initial results show that the TFT-based delay prediction model achieves satisfactory performance measured by smaller prediction errors on a testing dataset. In addition, the interpretability analysis of the model outputs identifies the important input factors for delay prediction. The proposed approach is expected to help air traffic managers or decision makers gain insights about traffic management actions on delay mitigation and once operationalized, provide enough lead time to plan for predicted performance degradation.



There are no comments yet.


page 3

page 5

page 7

page 9

page 15


Inferring untrained complex dynamics of delay systems using an adapted echo state network

Caused by finite signal propagation velocities, many complex systems fea...

Towards Safer Transportation: a self-supervised learning approach for traffic video deraining

Video monitoring of traffic is useful for traffic management and control...

Multi-step-ahead Prediction from Short-term Data by Delay-embedding-based Forecast Machine

Making accurate multi-step-ahead prediction for a complex system is a ch...

Spatio-Temporal Data Mining for Aviation Delay Prediction

To accommodate the unprecedented increase of commercial airlines over th...

TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction

Critical incident stages identification and reasonable prediction of tra...

A self-organizing system for urban traffic control based on predictive interval microscopic model

This paper introduces a self-organizing traffic signal system for an urb...

Effects of winter climate on high speed passenger trains in Botnia-Atlantica region

Harsh winter climate can cause various problems for both public and priv...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.