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: 30 September 2024 | Viewed by 779

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
Special Issues, Collections and Topics in MDPI journals

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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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers

This special issue is now open for submission, see below for planned papers.

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A Novel MIMO-based UAVs as a Base-station for Next Generation Emergency Transportation
Authors: Waleed Shahjehan 1; Abid Ullah 1; Syed Waqar Shah 1; Mohammad Aljaidi 2; Ali Safaa Sadiq 3*; Omprakash Kaiwartya 3
Affiliation: 1 Department of Electrical Engineering, Engineering University Peshawar, 814 Khyber Pakhtunkhwa, Pakistan; waleedshahjehan@gmail.com , abidullah@uetpeshawar.edu.pk , aqar.shah@uetpeshawar.edu.pk; 2 Computer Science Department, Faculty of Information Technology, Zarqa University, Zarqa 13110 Jordan, mjaidi@zu.edu.jo; 3 Department of Computer Science, Nottingham Trent University; Clifton Lane, NG11 8NS, Nottingham, UK
Abstract: Unmanned aerial vehicles (UAVs), have become very adaptable platforms for a wide range of uses, especially in emergency situations where timely and dependable communication is critical. This work introduces a new method of improving communication between unmanned aerial vehicles (UAVs) and a terrestrial base-station during emergency scenarios. The method makes use of a Multiple-Input Multiple-Output (MIMO) communication system built on the Radar Water-filling Model. Conventional communication design can be degraded in an emergency circumstance, causing the channels of communication ineffective. The proposed approach makes use of UAVs that have radar capabilities and MIMO-capable communication technologies to overcome this difficulty. A tried-and-true method for processing radar signals, the Radar Water-filling Model, is modified to effectively distribute resource allocation according to the urgency of the challenge. Commingling the UAVs concepts, MIMO techniques and Radar principles may create a reliable communication link in any natural hazard in which conventional base station fails to provide mobile users connectivity. This novel architecture has huge ability to take rapid response in disaster handling and can coordinate with mobile users to enhance end customer safety. The experimental findings for MIMO Radar Water-filling Model shows significant performance improvement while evaluating with conventional techniques.

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