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

arXiv: https://arxiv.org/abs/2105.08969 · HTML: https://mlsanity.com/paper/2105.08969/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 42 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction](https://mlsanity.com/paper/1803.01254/) (2018)
- [Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction](https://mlsanity.com/paper/1701.04245/) (2017)
- [COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference](https://mlsanity.com/paper/1909.11048/) (2019)
- [Flight Delay Prediction using Airport Situational Awareness Map](https://mlsanity.com/paper/1911.01605/) (2019)

## Built on by (most cited first, 5 of 5)

- [Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)](https://mlsanity.com/paper/2305.05237/) (2023)
- [REFINE: Random RangE FInder for Network Embedding](https://mlsanity.com/paper/2108.10703/) (2021)
- [Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting](https://mlsanity.com/paper/2302.09956/) (2023)
- [Continually learning out-of-distribution spatiotemporal data for robust energy forecasting](https://mlsanity.com/paper/2306.06385/) (2023)
- [Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning](https://mlsanity.com/paper/2309.04296/) (2023)

## Abstract (arXiv)

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 end-to-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.