Summary of Predicting the Skies: a Novel Model For Flight-level Passenger Traffic Forecasting, by Sina Ehsani et al.
Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting
by Sina Ehsani, Elina Sergeeva, Wendy Murdy, Benjamin Fox
First submitted to arxiv on: 7 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel approach to predicting flight-level passenger traffic leverages a multimodal deep learning model that integrates Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). The model ingests historical traffic data, fare closure information, and seasonality attributes specific to each flight. By exploiting temporal patterns and spatial relationships within the data, the approach yields substantial accuracy improvements compared to traditional models, with an approximate 33% improvement in Mean Squared Error (MSE) over benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to predict how many people will be on a plane at any given time. This helps airlines make decisions about things like ticket prices and flight routes. The researchers developed a special kind of computer model that combines two types of AI: recurrent neural networks (RNNs) and convolutional neural networks (CNNs). They used this model to analyze data from American Airlines, including information about past flights and how many people were on them. By using this data, the model can make more accurate predictions about future flight traffic. |
Keywords
* Artificial intelligence * Cnn * Deep learning * Mse * Rnn
