Numerical Optimization in Integrated Multi-Dimensional Transportation Systems

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 December 2025 | Viewed by 429

Special Issue Editors


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Guest Editor
1. School of Aeronautics, Northwestern Polytechnical University, Xi’an 710071, China
2. National Key Laboratory of Aircraft Configuration Design, Xi’an 710071, China
Interests: traffic flow modeling; CAV; UGV; air–ground cooperative control

E-Mail Website
Guest Editor
1. School of Aeronautics, Northwestern Polytechnical University, Xi’an 710071, China
2. National Key Laboratory of Aircraft Configuration Design, Xi’an 710071, China
Interests: UAV swarm control; intelligent flight control; navigation and guidance; aircraft perception and decision making
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Special Issue Information

Dear Colleagues,

This Special Issue focuses on the latest advancements in intelligent transportation systems, highlighting transformative developments in connected automated vehicles, unmanned aerial systems, and integrated transportation networks. These innovations are reshaping the future of transportation, offering new opportunities and challenges for optimization and integration across multi-dimensional systems.

Numerical optimization has achieved significant progress in enhancing the efficiency, sustainability, and adaptability of transportation systems. By integrating advanced optimization techniques with emerging technologies such as artificial intelligence (AI) and the Internet of Things (IoT), real-time data can be effectively utilized to improve decision making, energy efficiency, emission reduction, and coordination between ground and air transportation. These advancements are driving the creation of smarter, more cohesive transportation ecosystems.

This Special Issue explores critical topics such as traffic flow modeling, energy and emission optimization, unmanned system coordination, and multi-modal transportation planning. It aims to showcase cutting-edge research and foster interdisciplinary collaboration to address the complexities of modern transportation systems.

We invite contributions that advance the theory, methodology, and applications of numerical optimization in this rapidly evolving field. Join us in exploring innovative solutions to shape the future of integrated multi-dimensional transportation systems.

Dr. Changyin Dong
Prof. Dr. Ni Li
Guest Editors

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Keywords

  • integrated multi-dimensional transportation
  • traffic flow modeling and control
  • energy and emission optimization in transportation
  • unmanned aerial vehicles (UAVs)
  • connected automated vehicles (CAVs)
  • unmanned ground vehicles (UGVs)
  • air–ground cooperative systems
  • UAV and UGV collaborative transport
  • cooperative optimization and control
  • intelligent flight control and optimization
  • navigation and guidance technologies
  • vehicle perception and decision making
  • swarm intelligent control
  • multi-modal transportation planning
  • behavioral science in transportation systems
  • travel behavior analysis
  • integrated transportation networks
  • low-altitude economy and advanced/urban air mobility
  • multi-source big data fusion and analysis in transportation

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Published Papers (1 paper)

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Research

21 pages, 3051 KiB  
Article
Novel Gaussian-Decrement-Based Particle Swarm Optimization with Time-Varying Parameters for Economic Dispatch in Renewable-Integrated Microgrids
by Yuan Wang, Wangjia Lu, Wenjun Du and Changyin Dong
Mathematics 2025, 13(15), 2440; https://doi.org/10.3390/math13152440 - 29 Jul 2025
Viewed by 233
Abstract
Background: To address the uncertainties of renewable energy power generation, the disorderly charging characteristics of electric vehicles, and the high electricity cost of the power grid in expressway service areas, a method of economic dispatch optimization based on the improved particle swarm optimization [...] Read more.
Background: To address the uncertainties of renewable energy power generation, the disorderly charging characteristics of electric vehicles, and the high electricity cost of the power grid in expressway service areas, a method of economic dispatch optimization based on the improved particle swarm optimization algorithm is proposed in this study. Methods: Mathematical models of photovoltaic power generation, energy storage systems, and electric vehicles were established, thereby constructing the microgrid system model of the power load in the expressway service area. Taking the economic cost of electricity consumption in the service area as the objective function and simultaneously meeting constraints such as power balance, power grid interactions, and energy storage systems, a microgrid economy dispatch model is constructed. An improved particle swarm optimization algorithm with time-varying parameters of the inertia weight and learning factor was designed to solve the optimal dispatching strategy. The inertia weight was improved by adopting the Gaussian decreasing method, and the asymmetric dynamic learning factor was adjusted simultaneously. Findings: Field case studies demonstrate that, compared to other algorithms, the improved Particle Swarm Optimization algorithm effectively reduces the operational costs of microgrid systems while exhibiting accelerated convergence speed and enhanced robustness. Value: This study provides a theoretical mathematical reference for the economic dispatch optimization of microgrids in renewable-integrated transportation systems. Full article
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