Applications of Intelligent Optimization Algorithms in Integrated Transportation Systems
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: 30 November 2026 | Viewed by 54
Special Issue Editors
Interests: intelligent traffic control and safety; intelligent optimization algorithms; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent transportation systems; traffic control and traffic safety
Special Issues, Collections and Topics in MDPI journals
Interests: railway timetabling and dispatching; railway RAM; railway capacity optimization
Interests: connected and automated vehicles; end-to-end autonomous driving; scenario generation; traffic digital twins; V2X interaction
Special Issues, Collections and Topics in MDPI journals
Interests: optimization modeling applied in railroad operation and scheduling; mathematical modeling and optimization; air traffic management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Modern integrated transportation systems are characterized by their unprecedented scale, complexity, and dynamic nature. Effectively managing these systems to enhance their efficiency, sustainability, and resilience presents significant challenges that traditional operational research methods often struggle to address. The emergence of intelligent optimization algorithms (IOAs), including metaheuristics, machine learning-driven optimizers, and hybrid approaches, offers a powerful and innovative toolkit to tackle these complex, large-scale, and non-linear problems. This Special Issue aims to compile high-quality original research and commentary articles focusing on the theoretical innovations and practical applications of various intelligent optimization algorithms in various modes of transportation such as highways, railways, aviation, and maritime transportation. The contributions we seek should not only demonstrate algorithmic innovation, but also provide practical and feasible solutions to real-world problems.
Dr. Wenxin Li
Prof. Dr. Changxi Ma
Dr. Chao Wen
Dr. Weike Lu
Dr. Qingwei Zhong
Guest Editors
Manuscript Submission Information
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Keywords
- intelligent prediction of multimodal freight volume
- multi modal transportation vehicle operation path planning
- public transportation and multimodal system optimization
- vehicle operation scheduling and intelligent control
- autonomous vehicles and advanced driver-assistance systems (ADAS)
- intelligent network vehicle trajectory prediction
- UAV swarm scheduling and control
- aircraft swarm management in high-density flight environments
- UAV path planning and conflict detection
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