An Enhanced Sustainable Mobility as a Service Based on 5G Network for Human-Centric Mobile Network in Smart City

Senthil, G. A. and Prabha, R. and Roopa, D. and Sridevi, S. (2025) An Enhanced Sustainable Mobility as a Service Based on 5G Network for Human-Centric Mobile Network in Smart City. In: Digital Convergence in Intelligent Mobility Systems. Wiley-Scrivener, pp. 293-214. ISBN 978-1-394-27526-7

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Abstract

Smart cities, propelled by swift urbanization and ecological apprehensions, endeavor to discover inventive approaches to ecological mobility. This study suggests a better viable Mobility as a Service (MaaS) paradigm that utilizes 5G networks to support human-centric wireless networks in urban areas. The system integrates multiple modes of transport alternatives, real-time traffic management, and personalized transportation services to revolutionize urban mobility by utilizing ultra-fast interaction, minimal latency, and widespread device compatibility of 5G. This lays the foundation for an ecologically conscious, human-centered, and resilient future for public transportation, one in which residents of technologically advanced towns have seamless, customized, and eco-friendly transport experiences—using slicing of networks, computing at the edge, and communication via V2X to integrate 5G infrastructure to MaaS technologies and urban smart city ecosystems. The proposed system presents a range of intermodal choices with price changes, immediate reservations, and smooth intermodal transfers determined by customer preferences and updated information. The incorporation of 5G technology facilitates a revolutionary shift in urban transportation by providing fast and low-latency connections, allowing real-time data sharing among users, infrastructure, and vehicles. The research optimizing mobility services, MaaS, raises public transportation systems’ general effectiveness, security, and long-term viability. With 5G connectivity enabling instantaneous interaction between equipment and vehicles, MaaS automatically controls traffic patterns to minimize wait times and reduce traffic. Mobility options that are context-aware and personalized are offered by the MaaS platform, which keeps the demands of the end users in mind. The model uses extensive statistical analysis to learn about user behavior, patterns of traffic, and impacts on the environment. These understandings enable transportation agencies and planning professionals to make well-informed decisions that further optimize the metropolitan mobility environment.

Item Type: Book Section
Subjects: Computer Science Engineering > Artificial Intelligence
Computer Science Engineering > Computer Network
Domains: Computer Science Engineering
Depositing User: Mr IR Admin
Last Modified: 11 May 2026 10:41
URI: https://ir.vistas.ac.in/id/eprint/17612

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