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Applied Sciences
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Advanced, Smart, and Sustainable Transportation
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Advanced, Smart, and Sustainable Transportation

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4725

Special Issue Editors

Prof. Dr. Shaopeng Zhong

E-Mail Website
Guest Editor
Department of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China
Interests: sustainable urban planning and transportation planning; land use and transportation integration; urban transportation network analysis; transportation network reliability; road congestion pricing; logic-driven transport big data analysis; data mining techniques for traffic monitoring data
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kai Liu

E-Mail Website
Guest Editor
School of Economics and Management, Dalian University of Technology, Dalian 116024, China
Interests: intelligent transportation system (ITS); transportation in geography information system (T-GIS); transport geography; advanced traveler information system (ATIS); urban and regional planning and transport planning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhijia Tan

E-Mail Website
Guest Editor
School of Management, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, China
Interests: modeling and optimization of transportation systems; freight big data and intelligent decision-making theory; green and intelligent transportation theory
Dr. Hongmei Zhou

E-Mail Website
Guest Editor
Department of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China
Interests: traffic planning and management; traffic safety; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 8th International Conference on Traffic Engineering and Transportation Systems will be held in Dalian, China, on September 20–22, 2024. ICTETS 2024 invites the submission of substantial, original, and unpublished research papers regarding transportation systems, transportation planning, traffic control and information technology, controlling methods for logistics and supply chains, etc.

In recent years, urban structures and logistics systems have undergone significant changes, posing new challenges and opportunities for traffic engineering and transportation systems. The methods to address these evolving issues are continuously enhanced through iterative upgrades, particularly in the areas of transportation systems, supply chain control measures, and the integration of new multi-mode transportation systems. Concurrently, research efforts have been focused on advancing the theory, application, safety, and integration of these innovations in transportation engineering.

Our proposed Special Issue aims to further explore innovations in traffic engineering and logistics management. This Special Issue will provide an opportunity for researchers to expand on the topics presented at ICTETS 2024 and delve deeper into the advancements shaping the transportation industry. By aligning with the themes of the conference, this Special Issue aims to facilitate knowledge sharing and collaboration among experts in the field, ultimately contributing to the development of a more efficient and sustainable transportation and logistics system.

Prof. Dr. Shaopeng Zhong
Prof. Dr. Kai Liu
Prof. Dr. Zhijia Tan
Dr. Hongmei Zhou
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. 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.

Keywords

  • theory and applications of traffic engineering
  • transportation systems and logistics
  • traffic control and information technology
  • transportation safety
  • transportation planning
  • rail and transit systems
  • intelligent transportation systems
  • big data analysis in transportation
  • emerging transportation technologies
  • sustainable transportation

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Published Papers (6 papers)

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Research

21 pages, 9483 KiB  
Article
SISGAN: A Generative Adversarial Network Pedestrian Trajectory Prediction Model Combining Interaction Information and Scene Information
by Wanqing Dou and Lili Lu
Appl. Sci. 2024, 14(20), 9537; https://doi.org/10.3390/app14209537 - 18 Oct 2024
Viewed by 670
Abstract
Accurate pedestrian trajectory prediction is crucial in many fields. This requires the full use and learning of pedestrians’ social interactions, movements, and environmental information. In view of the current research on pedestrian trajectory prediction, wherein most of the pedestrian interaction information is explored [...] Read more.
Accurate pedestrian trajectory prediction is crucial in many fields. This requires the full use and learning of pedestrians’ social interactions, movements, and environmental information. In view of the current research on pedestrian trajectory prediction, wherein most of the pedestrian interaction information is explored from the level of overall interaction, this paper proposes the SISGAN model, which designs a social interaction module from the perspective of the target pedestrian, and takes four kinds of interaction information as the influencing factors of pedestrian interaction, so as to describe the influence mechanism of pedestrian–pedestrian interaction. In addition, in terms of environmental information, the index density of pedestrian historical trajectory in space is taken into account in the extraction of environmental information, which increases the potential correlation between environmental information and pedestrians. Finally, we integrate social interaction information and environmental information and make the final trajectory prediction based on GAN. Experiments on ETH and UCY datasets demonstrate the effectiveness of the SISGAN model proposed in this paper. Full article
(This article belongs to the Special Issue Advanced, Smart, and Sustainable Transportation)
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14 pages, 3224 KiB  
Article
Optimizing Rural Highway Maintenance Scheme with Mathematical Programming
by Fei Shan, Hui Li, Zhongren Wang, Ming Jin and Dawei Chen
Appl. Sci. 2024, 14(18), 8253; https://doi.org/10.3390/app14188253 - 13 Sep 2024
Viewed by 505
Abstract
Maintaining rural highways is crucial in ensuring the reliability and efficiency of transportation infrastructure in modern rural areas. Rural highways often suffer heavy traffic from logistics and regular transportation users. The efficient management of these roads is essential to avoid issues like traffic [...] Read more.
Maintaining rural highways is crucial in ensuring the reliability and efficiency of transportation infrastructure in modern rural areas. Rural highways often suffer heavy traffic from logistics and regular transportation users. The efficient management of these roads is essential to avoid issues like traffic bottlenecks, fuel consumption, and environmental problems. Traditional maintenance approaches focus on cost reduction, which can lead to adverse effects such as network congestion and environmental damage. To address these challenges, this study proposes a bi-level mathematical programming model aiming at optimizing rural highway maintenance. This model balances maintenance costs, network congestion, system fuel consumption, and environmental impacts. By transforming the bi-level model into a single-level mixed-integer linear programming model, the study enhances the computational feasibility, enabling practical implementation using commercial solvers. The model’s effectiveness is validated through numerical examples, providing insights for the development of optimal maintenance schedules that minimize externality costs while adhering to financial constraints and operational guidelines, providing a valuable addition to the road engineer’s toolbox. Full article
(This article belongs to the Special Issue Advanced, Smart, and Sustainable Transportation)
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18 pages, 5660 KiB  
Article
Optimal Deployment of Container Weighing Equipment: Models and Properties
by Zhaojing Yang, Min Xu, Xuecheng Tian, Yong Jin and Shuaian Wang
Appl. Sci. 2024, 14(17), 7798; https://doi.org/10.3390/app14177798 - 3 Sep 2024
Viewed by 509
Abstract
Container weighing is crucial to the safety of the shipping system and has garnered significant attention in the maritime industry. This research develops a container weighing optimization model and validates several propositions derived from this model. Then, a case study is conducted on [...] Read more.
Container weighing is crucial to the safety of the shipping system and has garnered significant attention in the maritime industry. This research develops a container weighing optimization model and validates several propositions derived from this model. Then, a case study is conducted on ports along the Yangtze River, and the sensitivity analysis of the model is provided. We report the following findings. First, the model can be solved efficiently for large-scale optimization problems. Second, as the number of weighing machines increases, the container weighing mode changes—from selectively weighing containers at their origin ports, then weighing containers at their transshipment ports or destination ports, to all of the containers weighed at their origin ports. Third, in order to improve the safety benefits of weighing containers, port authorities can increase the weighing capacity of weighing machines. The research provides theoretical guidance for shipping system managers to design container weighing plans that enhance maritime safety. Full article
(This article belongs to the Special Issue Advanced, Smart, and Sustainable Transportation)
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16 pages, 23913 KiB  
Article
Mileage-Aware for Vehicle Maintenance Demand Prediction
by Fanghua Chen, Deguang Shang, Gang Zhou, Ke Ye and Fujie Ren
Appl. Sci. 2024, 14(16), 7341; https://doi.org/10.3390/app14167341 - 20 Aug 2024
Viewed by 746
Abstract
It is of paramount importance to accurately predict the maintenance demands of vehicles in order to guarantee their sustainable use. Nevertheless, the current methodologies merely predict a partial aspect of a vehicle’s maintenance demands, rather than the comprehensive maintenance demands. Moreover, the process [...] Read more.
It is of paramount importance to accurately predict the maintenance demands of vehicles in order to guarantee their sustainable use. Nevertheless, the current methodologies merely predict a partial aspect of a vehicle’s maintenance demands, rather than the comprehensive maintenance demands. Moreover, the process of predicting vehicle maintenance demands must give due consideration to the influence of mileage on such demands. In light of the aforementioned considerations, we put forth a vehicle overall maintenance demand prediction method that incorporates vehicle mileage awareness. In order to address the discrepancy between the vector space of mileage and that of the project, we put forth a mileage representation method for the maintenance demand prediction task. To capture the significant impact of key mileage and projects on future demand, we propose a learning module for key temporal information using a fusion of Long Short-Term Memory (LSTM) networks and attention mechanism. Moreover, to integrate maintenance mileage and projects, we propose a fusion method based on a gated unit. The experimental results obtained from real datasets demonstrate that the proposed model exhibits a superior performance compared to existing methods. Full article
(This article belongs to the Special Issue Advanced, Smart, and Sustainable Transportation)
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7 pages, 781 KiB  
Communication
Proposal of Innovative Methods for Computer Vision Techniques in Maritime Sector
by Bo Jiang, Xuan Wu, Xuecheng Tian, Yong Jin and Shuaian Wang
Appl. Sci. 2024, 14(16), 7126; https://doi.org/10.3390/app14167126 - 14 Aug 2024
Viewed by 881
Abstract
Computer vision (CV) techniques have been widely studied and applied in the shipping industry and maritime research. The existing literature has primarily focused on enhancing image recognition accuracy and precision for water surface targets by refining CV models themselves. This paper introduces innovative [...] Read more.
Computer vision (CV) techniques have been widely studied and applied in the shipping industry and maritime research. The existing literature has primarily focused on enhancing image recognition accuracy and precision for water surface targets by refining CV models themselves. This paper introduces innovative methods to further improve the accuracy of detection and recognition using CV models, including using ensemble learning and integrating shipping domain knowledge. Additionally, we present a novel application of CV techniques in the maritime domain, expanding the research perspective beyond the traditional focus on the accurate detection and recognition of water surface targets. Specifically, a novel solution integrating a CV model and the transfer learning method is proposed in this paper to address the challenge of relatively low-speed and high-charge internet services on ocean-going vessels, aiming to improve the online video viewing experience while conserving network resources. This paper is of importance for advancing further research and application of CV techniques in the shipping industry. Full article
(This article belongs to the Special Issue Advanced, Smart, and Sustainable Transportation)
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30 pages, 9332 KiB  
Article
Research on Multi-Mode Braking Energy Recovery Control Strategy for Battery Electric Vehicles
by Boju Liu, Gang Li and Shuang Wang
Appl. Sci. 2024, 14(15), 6505; https://doi.org/10.3390/app14156505 - 25 Jul 2024
Viewed by 721
Abstract
To further improve the braking energy recovery efficiency of battery electric vehicles and increase the range of the cars, this paper proposes a multi-mode switching braking energy recovery control strategy based on fuzzy control. The control strategy is divided into three modes: single-pedal [...] Read more.
To further improve the braking energy recovery efficiency of battery electric vehicles and increase the range of the cars, this paper proposes a multi-mode switching braking energy recovery control strategy based on fuzzy control. The control strategy is divided into three modes: single-pedal energy recovery, coasting energy recovery, and conventional braking energy recovery. It takes the accelerator pedal and brake pedal opening as the switching conditions. It calculates the front and rear wheel braking ratio allocation coefficients and the motor braking ratio through fuzzy control to recover braking energy. The genetic algorithm (GA) is used to update the optimized affiliation function to optimize the motor braking allocation ratio through fuzzy control, and joint simulation is carried out based on the NEDC (New European Driving Cycle) and CLTC-P (China Light-duty Vehicle Test Cycle for Passenger vehicles) cycle conditions. The results show that the multi-mode braking energy recovery control strategy proposed in this paper improves the energy recovery rate and range contribution rate by 4% and 9.6%, respectively, and increases the range by 22.5 km under NEDC cycle conditions. It also improves the energy recovery rate and range contribution rate by 8.7% and 5.5%, respectively, and increases the range by 13 km under CLTC-P cycle conditions, which can effectively improve the energy recovery efficiency of the vehicle and increase the range of battery electric vehicles. Full article
(This article belongs to the Special Issue Advanced, Smart, and Sustainable Transportation)
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