Enhancing next destination prediction: An application of LSTM using real-world airline data

Date

2023-6-22

Author

Salihoğlu, Salih

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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 is proposed for destination prediction in the transportation industry. The experimental results highlight the satisfactory performance and high scores achieved by the LSTM 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.

Subject Keywords

Next destination prediction, LSTM, Deep learning

URI

https://hdl.handle.net/11511/104456

Collections

Graduate School of Natural and Applied Sciences, Thesis