Enhancing Next Destination Prediction: A Novel Long Short-Term Memory Neural Network Approach Using Real-World Airline Data
This work addresses the need for accurate destination prediction in the transportation industry to enhance customer satisfaction and targeted marketing, but it appears incremental as it builds on existing LSTM methods.
The study tackled the problem of predicting travelers' next destinations by developing a novel LSTM neural network model with a sliding window approach, achieving satisfactory performance and high scores across different data sizes and metrics.
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.