Computing in Future Transportation Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 874

Special Issue Editors

Prof. Dr. Hao Wu
E-Mail Website
Guest Editor
Key Laboratory of Railway Industry of Broadband Mobile Information Communications, Beijing Jiaotong University, Beijing 100044, China
Interests: broadband mobile communication system and dedicated mobile communication; communication engineering; artificial intelligence
Prof. Dr. Hongke Zhang
E-Mail Website
Guest Editor
National Engineering Laboratory for Next Generation Internet Interconnection Devices, Beijing Jiaotong University, Beijing 100044, China
Interests: future internet; routing; 5G network

Special Issue Information

Dear Colleagues,

With the accelerated penetration of the new generation of information technologies such as 5G/6G, artificial intelligence, big data, and the Internet of Things in the field of transportation, the constantly moving vehicles have gradually become data centers and computing equipment on the wheels, which poses new challenges to the computing infrastructures, architecture, methods and computing capability in the future transportation system. Therefore, this special issue is aimed at introducing new ideas and experimental results of intelligent, sustainable and green computing architecture from design, service and theory to its practical application in the field of future transportation.

Potential topics related to computing in future transportation system include,but are not limited to, intelligent computing, data-intensive applications, large-scale computational science, artificial intelligence, machine learning and deep learning, new parallel and distributed computing, green computing, sustainable computing, and massive data processing from roadside infrastructure and vehicles. Computer architecture, digital twins and resource management technology, which are necessary to achieve high service performance of future transportation systems, are also topics of interest.

This Special Issue will publish high-quality, original research papers, in the overlapping fields of:

  • Intelligent computing;
  • Cloud/edge/fog computing;
  • Green computing;
  • Quantum computing;
  • AI, machine learning and deep learning;
  • Traffic big data processing, application, system and algorithm;
  • Intelligent vehicles;
  • Intelligent transport systems;
  • Traffic management and control;
  • Digital twins enabling transportation system;
  • Internet of Things;
  • Resource management and optimization;
  • Security, privacy and safety systems.

Prof. Dr. Hao Wu
Prof. Dr. Hongke Zhang
Guest Editors

Manuscript Submission Information

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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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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.


  • computing in future transportation
  • green computing, sustainable computing
  • intelligent vehicles
  • internet of vehicles
  • V2X communication
  • collaborative awareness and information fusion
  • big data of transportation
  • edge intelligent computing
  • architecture modeling and performance evaluation
  • AI, machine learning and deep learning

Published Papers (1 paper)

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19 pages, 2823 KiB  
An Energy-Efficient Optimization Method for High-Speed Rail Communication Systems Assisted by Intelligent Reflecting Surfaces (IRS)
Appl. Sci. 2023, 13(16), 9401; - 18 Aug 2023
Viewed by 558
This paper proposes an intelligent reflecting surface (IRS)-assisted energy efficiency optimization algorithm to address the problem of energy efficiency (EE) degradation in high-speed rail communication systems caused by line-of-sight link blockages between base stations and trains. The joint optimization of base station beamforming [...] Read more.
This paper proposes an intelligent reflecting surface (IRS)-assisted energy efficiency optimization algorithm to address the problem of energy efficiency (EE) degradation in high-speed rail communication systems caused by line-of-sight link blockages between base stations and trains. The joint optimization of base station beamforming and IRS phase shifts is formulated as a variable-coupled energy efficiency maximization problem, subject to the base station’s transmission power and the IRS unit’s modulus constraints. This is known to be an NP-hard problem, making it challenging to obtain the global optimal solution. To tackle the issue of optimization variable coupling, an alternating optimization is employed to decompose the original problem into two sub-problems: base station beamforming and IRS phase-shift optimization. The Dinkelbach method is utilized to convert the fractional objective function into a difference form; then, the successive convex approximation (SCA) algorithm is applied to transform non-convex constraints into convex ones, which are solved using CVX. The Riemann conjugate gradient (RCG) algorithm can effectively solve the difficult unit module constraint. Finally, an alternating iterative strategy is employed to converge to a suboptimal solution. Our simulation results demonstrate that the proposed algorithm significantly enhances system efficiency with low computational complexity. Specifically, when the number of IRS reflecting elements is 64, the system’s EE is improved by approximately 12.41%, 35.26%, and 37.96% compared to the semi-definite relaxation algorithm, the random phase shift approach, and no IRS scheme, respectively. Full article
(This article belongs to the Special Issue Computing in Future Transportation Systems)
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