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Smart city article details

Title	Intelligent Transportation Systems: Machine Learning Approaches For Urban Mobility In Smart Cities
ID_Doc	32662
Authors	Chen G.; Zhang J.W.
Year	2024
Published	Sustainable Cities and Society, 107
DOI	http://dx.doi.org/10.1016/j.scs.2024.105369
Abstract	Urban mobility in smart cities presents a complex challenge, demanding innovative solutions to address the ever-growing demands of transportation systems. This paper introduces a comprehensive approach that integrates machine learning techniques into the optimization of urban transportation. The proposed framework employs a multilayer objective function and incorporates constraints, considering factors such as interaction cost between transportation modes, energy consumption, and environmental impact. Leveraging a modified Teaching–Learning Based Optimization (TLBO) algorithm and a hybrid Artificial Neural Network–Recurrent Neural Network (ANN–RNN) technique, the model aims to enhance system adaptability and efficiency. In contrast to existing research, our work emphasizes a holistic optimization strategy that balances both the efficiency and sustainability of urban transportation. The outcomes of this research contribute to the advancement of Intelligent Transportation Systems, offering a nuanced understanding of system dynamics and providing a foundation for resilient and adaptive transportation networks in the evolving landscape of smart cities. © 2024 Elsevier Ltd
Author Keywords	Intelligent transportation systems; Machine learning; Optimization; Smart cities; Sustainable transportation; Urban mobility

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