  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.
