# Enhancing Next Destination Prediction: A Novel Long Short-Term Memory Neural Network Approach Using Real-World Airline Data

arXiv: https://arxiv.org/abs/2401.12830 · HTML: https://mlsanity.com/paper/2401.12830/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network](https://mlsanity.com/paper/1704.07049/) (2017)
- [Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks](https://mlsanity.com/paper/1404.5772/) (2014)

## Built on by (most cited first, 1 of 1)

- [A Similarity-Based Oversampling Method for Multi-label Imbalanced Text Data](https://mlsanity.com/paper/2411.01013/) (2024)

## Abstract (arXiv)

In the modern transportation industry, accurate prediction of travelers' next destinations brings multiple benefits to companies, such as customer satisfaction and targeted marketing. This study focuses on developing a precise model that captures the sequential patterns and dependencies in travel data, enabling accurate predictions of individual travelers' future destinations. To achieve this, a novel model architecture with a sliding window approach based on Long Short-Term Memory (LSTM) is proposed for destination prediction in the transportation industry. The experimental results highlight satisfactory performance and high scores achieved by the proposed model across different data sizes and performance metrics. This research contributes to advancing destination prediction methods, empowering companies to deliver personalized recommendations and optimize customer experiences in the dynamic travel landscape.