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Mathematics
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Future Technologies and Models for Integrated Transportation and Intelligent Transportation...
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Future Technologies and Models for Integrated Transportation and Intelligent Transportation Networks

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 2128

Special Issue Editors

Dr. Chenyu Huang

E-Mail Website
Guest Editor
Aviation Institute, University of Nebraska Omaha, Omaha, NE 68182, USA
Interests: data-driven modeling; decision-making; integrating computer vision with drone technology; dynamic integrated system development; aerial inspection; unmanned aircraft system (uas) operations; statistical modeling of transportation operations
Dr. Chuyang Yang

E-Mail Website
Guest Editor
School of Graduate Studies, College of Aviation, Embry-Riddle Aeronautical University Daytona Beach, FL 32114-3900, USA
Interests: traffic control; natural language processing systems; speech recognition; neural network; deep learning; long short-term memory network; air transportation; civil aviation; transport
Prof. Dr. Trung Thanh Nguyen

E-Mail Website
Guest Editor
School of Engineering, Liverpool John Moores University, Liverpool L33AF, UK
Interests: operational research in transportation; risk assessment; safety analysis; transport/logistics

Special Issue Information

Dear Colleagues,

This Special Issue, Future Technologies and Models for Integrated Transportation and Intelligent Transportation Networks, invites cutting-edge research on advanced technologies and theoretical models that reshape and enhance modern transportation systems. Advanced technologies such as data fusion, edge computing, multisensory systems, and machine learning have significantly transformed transportation, while digital twins and Industry 4.0 applications facilitate mathematical models that improve efficiency, safety, accessibility, and sustainability across transportation modes, including road, railway, marine, aviation, and multimodal networks. We encourage submissions that explore the integration of these technologies with mathematical theory, optimization algorithms, and machine learning to solve complex transportation challenges. This Special Issue also emphasizes the importance of understanding theoretical developments to support the practical implementation of integrated, intelligent transportation systems.

Original research articles and reviews are welcome in this Special Issue. Research areas may include (but are not limited to) the following:

  1. Mathematical modelling of digital twins;
  2. Optimization techniques in multimodal transportation networks;
  3. Algorithm design in intelligent transportation systems;
  4. Predictive modelling and machine learning;
  5. Edge computing;
  6. Network theory and graph algorithms;
  7. Optimization models for sustainable transportation;
  8. Game theory applications;
  9. Mathematical applications to transportation safety and accessibility;
  10. Predictive maintenance for transportation infrastructure.

We look forward to receiving your contributions.

Dr. Chenyu Huang
Dr. Chuyang Yang
Prof. Dr. Trung Thanh Nguyen
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. Mathematics 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 2600 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 transportation systems
  • multimodal transportation
  • transportation network
  • algorithms
  • mathematical methods
  • optimization
  • data fusion
  • machine learning
  • edge computing
  • digital twins

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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22 pages, 2108 KiB  
Article
Deep Reinforcement Learning for Real-Time Airport Emergency Evacuation Using Asynchronous Advantage Actor–Critic (A3C) Algorithm
by Yujing Zhou, Yupeng Yang, Bill Deng Pan, Yongxin Liu, Sirish Namilae, Houbing Herbert Song and Dahai Liu
Mathematics 2025, 13(14), 2269; https://doi.org/10.3390/math13142269 - 15 Jul 2025
Viewed by 458
Abstract
Emergencies can occur unexpectedly and require immediate action, especially in aviation, where time pressure and uncertainty are high. This study focused on improving emergency evacuation in airport and aircraft scenarios using real-time decision-making support. A system based on the Asynchronous Advantage Actor–Critic (A3C) [...] Read more.
Emergencies can occur unexpectedly and require immediate action, especially in aviation, where time pressure and uncertainty are high. This study focused on improving emergency evacuation in airport and aircraft scenarios using real-time decision-making support. A system based on the Asynchronous Advantage Actor–Critic (A3C) algorithm, an advanced deep reinforcement learning method, was developed to generate faster and more efficient evacuation routes compared to traditional models. The A3C model was tested in various scenarios, including different environmental conditions and numbers of agents, and its performance was compared with the Deep Q-Network (DQN) algorithm. The results showed that A3C achieved evacuations 43.86% faster on average and converged in fewer episodes (100 vs. 250 for DQN). In dynamic environments with moving threats, A3C also outperformed DQN in maintaining agent safety and adapting routes in real time. As the number of agents increased, A3C maintained high levels of efficiency and robustness. These findings demonstrate A3C’s strong potential to enhance evacuation planning through improved speed, adaptability, and scalability. The study concludes by highlighting the practical benefits of applying such models in real-world emergency response systems, including significantly faster evacuation times, real-time adaptability to evolving threats, and enhanced scalability for managing large crowds in high-density environments including airport terminals. The A3C-based model offers a cost-effective alternative to full-scale evacuation drills by enabling virtual scenario testing, supports proactive safety planning through predictive modeling, and contributes to the development of intelligent decision-support tools that improve coordination and reduce response time during emergencies. Full article
(This article belongs to the Special Issue Future Technologies and Models for Integrated Transportation and Intelligent Transportation Networks)
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17 pages, 1850 KiB  
Article
SGD-TripleQNet: An Integrated Deep Reinforcement Learning Model for Vehicle Lane-Change Decision
by Yang Liu, Tianxing Yang, Liwei Tian and Jianbiao Pei
Mathematics 2025, 13(2), 235; https://doi.org/10.3390/math13020235 - 11 Jan 2025
Cited by 4 | Viewed by 1370
Abstract
With the advancement of autonomous driving technology, vehicle lane-change decision (LCD) has become a critical issue for improving driving safety and efficiency. Traditional deep reinforcement learning (DRL) methods face challenges such as slow convergence, unstable decisions, and low accuracy when dealing with complex [...] Read more.
With the advancement of autonomous driving technology, vehicle lane-change decision (LCD) has become a critical issue for improving driving safety and efficiency. Traditional deep reinforcement learning (DRL) methods face challenges such as slow convergence, unstable decisions, and low accuracy when dealing with complex traffic environments. To address these issues, this paper proposes a novel integrated deep reinforcement learning model called “SGD-TripleQNet” for autonomous vehicle lane-change decision-making. This method integrates three types of deep Q-learning networks (DQN, DDQN, and Dueling DDQN) and uses the Stochastic Gradient Descent (SGD) optimization algorithm to dynamically adjust the network weights. This dynamic weight adjustment process fine-tunes the weights based on gradient information to minimize the target loss function. The approach effectively addresses key challenges in autonomous driving lane-change decisions, including slow convergence, low accuracy, and unstable decision-making. The experiment shows that the proposed method, SGD-TripleQNet, has significant advantages over single models: In terms of convergence speed, it is approximately 25% faster than DQN, DDQN, and Dueling DDQN, achieving stability within 150 epochs; in terms of decision stability, the Q-value fluctuations are reduced by about 40% in the later stages of training; in terms of final performance, the average reward exceeds that of DQN (by 6.85%), DDQN (by 6.86%), and Dueling DDQN (by 6.57%), confirming the effectiveness of the proposed method. It also provides a theoretical foundation and practical guidance for the design and optimization of future autonomous driving systems. Full article
(This article belongs to the Special Issue Future Technologies and Models for Integrated Transportation and Intelligent Transportation Networks)
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