A multi-modal geospatial–temporal LSTM based deep learning framework for predictive modeling of urban mobility patterns

A multi-modal geospatial–temporal LSTM based deep learning framework for predictive modeling of urban mobility patterns

Abstract Urban mobility prediction is crucial for optimizing resource allocation, managing transportation systems, and planning urban development. We propose a novel framework, GeoTemporal LSTM (GT-LSTM), designed to address the intricate spatiotemporal dynamics of urban environments. GT-LSTM integr...

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Bibliographic Details
Main Authors: Sangeetha S.K.B, Sandeep Kumar Mathivanan, Hariharan Rajadurai, Jaehyuk Cho, Sathishkumar Veerappampalayam Easwaramoorthy
Format: Article
Language:English
Published: Nature Portfolio 2024-12-01
Series:Scientific Reports
Subjects:
Geospatial–temporal
Multi-modal
Mobility prediction
Transporation
Urban Transportation
Online Access:https://doi.org/10.1038/s41598-024-74237-3
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https://doi.org/10.1038/s41598-024-74237-3

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