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Algorithms
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Applications of Intelligent Optimization Algorithms in Integrated Transportation Systems
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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 642

Special Issue Editors

Dr. Wenxin Li

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Guest Editor
Institute of Intelligent Transportation, Hubei University of Arts and Science, Xiangyang 41053, China
Interests: intelligent traffic control and safety; intelligent optimization algorithms; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Changxi Ma

E-Mail Website
Guest Editor
School of Traffic and Transportation Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Interests: intelligent transportation systems; traffic control and traffic safety
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Wen

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Guest Editor
Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: railway timetabling and dispatching; railway RAM; railway capacity optimization
Dr. Weike Lu

E-Mail Website
Guest Editor
School of Rail Transportation, Soochow University, Suzhou 215131, China
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
Dr. Qingwei Zhong

E-Mail Website
Guest Editor
College of Air Traffic Management, Civil Aviation Flight University of China, Chengdu 641400, China
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

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 250 words) can be sent to the Editorial Office for assessment.

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. Algorithms 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 1800 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 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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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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19 pages, 8626 KB  
Article
GAD-YOLO: A Sight-Distance Adaptive Detection Algorithm for General Aviation Aircraft Skin Damage
by Tao Wu, Jifei Zhong, Zhanhai Wang, Chen Chen and Zhenghong Xia
Algorithms 2026, 19(1), 61; https://doi.org/10.3390/a19010061 - 10 Jan 2026
Viewed by 154
Abstract
To address the challenges in detecting surface damage on general aviation aircraft skin—such as feature degradation under varying imaging distances, significant target scale variations, and low recognition accuracy—this paper proposes GAD-YOLO, a sight-distance adaptive detection algorithm. First, a P2 small-target detection layer is [...] Read more.
To address the challenges in detecting surface damage on general aviation aircraft skin—such as feature degradation under varying imaging distances, significant target scale variations, and low recognition accuracy—this paper proposes GAD-YOLO, a sight-distance adaptive detection algorithm. First, a P2 small-target detection layer is integrated into the shallow network to enhance the capture of fine damage details. Second, an HMFHead detection head is introduced to mitigate scale variation effects through progressive semantic fusion and edge-aware constraints. Third, an LDown downsampling module is designed to construct a multi-scale feature fusion architecture. This module reduces redundancy via cross-level interaction and a lightweight kernel design, thereby decreasing the number of parameters and computational cost. Additionally, a DySample-based dynamic sampling operator is proposed to preserve local details through proximity-aware sampling while enriching the contextual semantics of distant damage features, effectively improving recognition performance. Experiments on a self-constructed general aviation aircraft skin damage dataset show that GAD-YOLO achieves 87.4% precision, 80.4% recall, 86.6% mAP@0.5, and 59.7% mAP@0.5:0.95. These results outperform the YOLOv11n baseline by 2.0%, 9.4%, 6.7%, and 7.6%, respectively. The proposed method significantly improves detection performance and provides a valuable reference for intelligent inspection and maintenance in general aviation. Full article
(This article belongs to the Special Issue Applications of Intelligent Optimization Algorithms in Integrated Transportation Systems)
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16 pages, 2031 KB  
Article
Cooperative 4D Trajectory Prediction and Conflict Detection in Integrated Airspace
by Xin Ma, Linxin Zheng, Jiajun Zhao and Yuxin Wu
Algorithms 2026, 19(1), 32; https://doi.org/10.3390/a19010032 - 1 Jan 2026
Viewed by 192
Abstract
In order to effectively ensure the flight safety of unmanned aerial vehicles (UAVs) and effectively deal with the risk of integrated airspace operation, this study carried out a series of key technology exploration and verification. In terms of data processing, Density-based spatial clustering [...] Read more.
In order to effectively ensure the flight safety of unmanned aerial vehicles (UAVs) and effectively deal with the risk of integrated airspace operation, this study carried out a series of key technology exploration and verification. In terms of data processing, Density-based spatial clustering of applications with noise (DBSCAN) clustering method is used to preprocess the characteristics of UAV automatic dependent surveillance–broadcast (ADS-B) data, effectively purify the data from the source, eliminate the noise and outliers of track data in spatial dimension and spatial-temporal dimension, significantly improve the data quality and standardize the data characteristics, and lay a reliable and high-quality data foundation for subsequent trajectory analysis and prediction. In terms of trajectory prediction, the convolutional neural networks-bidirectional gated recurrent unit (CNN-BiGRU) trajectory prediction model is innovatively constructed, and the integrated intelligent calculation of ‘prediction-judgment’ is successfully realized. The output of the model can accurately and prospectively judge the conflict situation and conflict degree between any two trajectories, and provide core and direct technical support for trajectory conflict warning. In the aspect of conflict detection, the performance of the model and the effect of conflict detection are fully verified by simulation experiments. By comparing the predicted data of the model with the real track data, it is confirmed that the CNN-BiGRU prediction model has high accuracy and reliability in calculating the distance between aircraft. At the same time, the preset conflict detection method is used for further verification. The results show that there is no conflict risk between the UAV and the manned aircraft in integrated airspace during the full 800 s of terminal area flight. In summary, the trajectory prediction model and conflict detection method proposed in this study provide a key technical guarantee for the construction of an active and accurate integrated airspace security management and control system, and have important application value and reference significance for improving airspace management efficiency and preventing flight conflicts. Full article
(This article belongs to the Special Issue Applications of Intelligent Optimization Algorithms in Integrated Transportation Systems)
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