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Title	Investigation Of Hybrid Models And Architectures For Real-Time Adaptive Passenger Flow Prediction
ID_Doc	33476
Authors	Begisbayev D.; Sakhipov A.; Seiitbek R.; Mansurova A.; Mansurova A.
Year	2024
Published	Proceedings - 2024 7th International Conference on Data Science and Information Technology, DSIT 2024
DOI	http://dx.doi.org/10.1109/DSIT61374.2024.10880981
Abstract	This study presents a theoretical foundation for a hybrid machine-learning model designed for real-time adaptive passenger flow prediction in public transportation systems. By integrating both historical and real-time data, the model aims to enhance demand forecasting accuracy and support optimized resource allocation. The proposed model combines Long Short-Term Memory (LSTM) networks for time-series dependencies with Graph Convolutional Networks (GCNs) to capture spatial relationships, enabling dynamic adaptation to changing patterns in passenger flow. Although this study is theoretical, anticipated practical applications include improved scheduling efficiency and responsiveness in transit systems, contributing to sustainable resource management. Key challenges, such as computational complexity and scalability for large datasets, are discussed alongside potential gains in prediction accuracy over traditional models, positioning this approach as a promising step forward in adaptive transit systems. © 2024 IEEE.
Author Keywords	data-driven optimization; hybrid machine learning; neural networks; passenger flow prediction; public transit systems; Real-time adaptive systems; smart cities; time-series analysis

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