Intelligent Transport Systems (ITSs) Meet Generative Artificial Intelligence

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 3470

Special Issue Editors


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Guest Editor
Department of Electrical and Electronic Information Engineering, Toyohashi University of Technology, Toyohashi 441-8580, Japan
Interests: computer networks; distributed systems; machine learning
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Guest Editor
School of Computer Science, University of Auckland, Auckland 1142, New Zealand
Interests: computer networks; network science; social computing; performance evaluation; Internet measurements; data science; cyber security

Special Issue Information

Dear Colleagues,

The evolution of Intelligent Transport Systems (ITS) represents an amalgamation of cutting-edge technologies integrating data analytics, advanced connectivity, and automation to fundamentally redefine the landscape of transportation networks. This transformation spans a diverse array of applications, including adaptive traffic control mechanisms, real-time predictive maintenance protocols, and the integration of interconnected vehicles. The overarching goal of ITS is to optimize traffic flow dynamics, mitigate congestion bottlenecks, and foster the development of sustainable mobility solutions. By leveraging intricate data-driven insights and innovative automation, ITSs endeavour to enhance transportation efficiency, reinforce safety measures, and build an ecosystem that adapts dynamically to evolving travel demands.

Concurrently, the frontier development in generative artificial intelligence (AI) encapsulates a realm where algorithms and models exhibit the unprecedented capability to learn intricate patterns from data and autonomously generate novel content, designs, or solutions. This paradigm shift is manifested through sophisticated techniques such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning architectures. Generative AI stands poised at the precipice of transportation innovation, offering unparalleled prospects for intelligent design, simulation, optimization, and decision making within the transportation domain. The integration of generative AI with ITSs heralds a groundbreaking convergence, unveiling new horizons for unprecedented advancements in transportation technology. This juncture not only augments the prospects of intelligent transportation solutions but also paves the way for the exploration of uncharted territories within the realm of mobility.

The fusion of ITS with generative AI presents an open canvas for researchers and practitioners to embark on pioneering investigations and contribute groundbreaking insights to the transportation domain. Novel research endeavours are poised to explore and harness the potential of this convergence to unravel innovative methodologies for addressing complex challenges within transportation networks, which is creating intelligent infrastructures and vehicles offering enhanced, intuitive, safe, and personalized in-car experiences. Researchers are invited to dive into unexplored realms, devising AI-driven solutions that transcend conventional paradigms. This Special Issue invites submissions that showcase the synergistic interplay between ITSs and generative AI, fostering groundbreaking advancements that redefine the future of transportation systems. Contributions that delineate novel approaches, address challenges, and unveil transformative insights into this converging frontier are eagerly anticipated.

Possible Topics: We invite researchers and practitioners to submit original research, reviews, and perspectives on, but not limited to, the following topics:

  • Distributed architectures and federated learning approaches to train generative AI models;
  • Generative models for traffic predication and optimization;
  • Generative AI-enabled predictive maintenance for transportation infrastructure;
  • Generative AI-enabled autonomous vehicles and their integration into ITS frameworks;
  • Generative AI applications in designing adaptive transportation systems;
  • Ethical considerations in deploying generative AI-driven transportation solutions;
  • Legal implication and liability in generative AI-enabled transport;
  • Security and privacy challenges in interconnected and generative AI-enabled ITS environments;
  • Human–vehicle interactions through generative AI;
  • Creative content generation for in-vehicle experiences;
  • Innovative applications of generative AI in augmenting public transportation, e.g., bus, tram, train, ferry experiences;
  • Generative AI-driven solutions for multimodal transportation and seamless integration;
  • The sustainability, e.g., carbon footprint studies on generative AI-enabled intelligent transport systems;
  • Curriculum development for AI in transportation to train future professionals in the integration of generative AI within Intelligent Transport Systems.

Dr. William Liu
Dr. Xun Shao
Dr. Aniket Mahanti
Guest Editors

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Keywords

  • intelligent transport system
  • generative artificial intelligence
  • large language models
  • distributed language models
  • vehicular Internet of Things
  • edge computing
  • cloud computing

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Published Papers (2 papers)

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14 pages, 1786 KiB  
Article
AI Services-Oriented Dynamic Computing Resource Scheduling Algorithm Based on Distributed Data Parallelism in Edge Computing Network of Smart Grid
by Jing Zou, Peizhe Xin, Chang Wang, Heli Zhang, Lei Wei and Ying Wang
Future Internet 2024, 16(9), 312; https://doi.org/10.3390/fi16090312 - 28 Aug 2024
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Abstract
Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize [...] Read more.
Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization. Full article
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40 pages, 4416 KiB  
Review
A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
by Waleed Shahjehan, Rajkumar Singh Rathore, Syed Waqar Shah, Mohammad Aljaidi, Ali Safaa Sadiq and Omprakash Kaiwartya
Future Internet 2024, 16(9), 337; https://doi.org/10.3390/fi16090337 - 14 Sep 2024
Abstract
As the world braces for an era of ubiquitous and seamless connectivity, hybrid beamforming stands out as a beacon guiding the evolutionary path of wireless communication technologies. Several hybrid beamforming technologies are explored for millimeter-wave multiple-input multi-output (MIMO) communication. The aim is to [...] Read more.
As the world braces for an era of ubiquitous and seamless connectivity, hybrid beamforming stands out as a beacon guiding the evolutionary path of wireless communication technologies. Several hybrid beamforming technologies are explored for millimeter-wave multiple-input multi-output (MIMO) communication. The aim is to provide a roadmap for hybrid beamforming that enhances wireless fidelity. In this systematic review, a detailed literature review of algorithms/techniques used in hybrid beamforming along with performance metrics, characteristics, limitations, as well as performance evaluations are provided to enable communication compatible with modern Wireless Intelligent Transport Systems (WITSs). Further, an in-depth analysis of the mmWave hybrid beamforming landscape is provided based on user, link, band, scattering, structure, duplex, carrier, network, applications, codebook, and reflecting intelligent surfaces to optimize system design and performance across diversified user scenarios. Furthermore, the current research trends for hybrid beamforming are provided to enable the development of advanced wireless communication systems with optimized performance and efficiency. Finally, challenges, solutions, and future research directions are provided so that this systematic review can serve as a touchstone for academics and industry professionals alike. The systematic review aims to equip researchers with a deep understanding of the current state of the art and thereby enable the development of next-generation communication in WITSs that are not only adept at coping with contemporary demands but are also future-proofed to assimilate upcoming trends and innovations. Full article
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