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Applied Sciences
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Application of New Technology and New Ideas in Intelligent Transportation...
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Application of New Technology and New Ideas in Intelligent Transportation System

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

Deadline for manuscript submissions: 20 May 2024 | Viewed by 2712

Special Issue Editors

Dr. Weitiao Wu

E-Mail Website
Guest Editor
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
Interests: public transport; logistics; traffic flow
Special Issues, Collections and Topics in MDPI journals
Dr. Shidong Liang

E-Mail Website
Guest Editor
Business School, University of Shanghai for Science & Technology, Shanghai 200093, China
Interests: urban transit planning and operation; traffic control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The intelligent transportation system effectively integrates advanced information, communication, sensor, electronic control, and computer technology into transportation, service control, and vehicle manufacturing. This results in harmony and close cooperation between people, vehicles, and roads, thus improving transportation efficiency, easing traffic jams, reducing traffic accidents, and decreasing energy consumption and exhaust emissions. With the advancement of a new round of scientific and technological revolution and industrial transformation, some new technologies and ideas are emerging and being applied to intelligent transportation systems. To better grasp the current development trend of domestic and overseas intelligent transportation systems, promote the foundation research and technological innovation of intelligent transportation systems, and deepen the academic exchanges of intelligent transportation systems, we are organizing a Special Issue titled "Application of new technology and new ideas in intelligent transportation system".

Dr. Weitiao Wu
Dr. Shidong Liang
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

  • connected automatic vehicles
  • safe driving assistance
  • vehicle control system
  • advanced public transportation system
  • rail transit system
  • the key technologies of intelligent transportation
  • operating vehicle management system
  • traffic big data analysis
  • information acquisition system

Published Papers (4 papers)

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Research

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17 pages, 10699 KiB  
Article
Improved Long-Term Forecasting of Passenger Flow at Rail Transit Stations Based on an Artificial Neural Network
by Zitao Du, Wenbo Yang, Yuna Yin, Xinwei Ma and Jiacheng Gong
Appl. Sci. 2024, 14(7), 3100; https://doi.org/10.3390/app14073100 - 07 Apr 2024
Viewed by 460
Abstract
When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration. To overcome this [...] Read more.
When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration. To overcome this problem, we propose several long-term factors affecting the passenger flow of rail transit in this paper. We also create a visual analysis of these factors using ArcGIS and construct a long-term passenger flow prediction model for rail transit based on a class neural network using an SPSS Modeler. After optimizing relevant parameters, the prediction accuracy reaches 94.6%. We compare the results with other models and find that the neural network model has a good performance in predicting long-term rail transit passenger flow. Finally, the factors affecting passenger flow are ranked in terms of importance. It is found that among these factors, bicycles available for connection have the biggest influence on the passenger flow of rail stations. Full article
(This article belongs to the Special Issue Application of New Technology and New Ideas in Intelligent Transportation System)
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16 pages, 3674 KiB  
Article
A New Indicator for Measuring Efficiency in Urban Freight Transportation: Defining and Implementing the OEEM (Overall Equipment Effectiveness for Mobility)
by Adrián Les, Paula Morella, María Pilar Lambán, Jesús Royo and Juan Carlos Sánchez
Appl. Sci. 2024, 14(2), 779; https://doi.org/10.3390/app14020779 - 16 Jan 2024
Viewed by 708
Abstract
Urban freight transportation is the activity that has the greatest impact on urban areas in terms of sustainability and livability, and it is, therefore, necessary to reduce its impact. Currently, there is a lack of methodologies to validate the methods proposed by companies [...] Read more.
Urban freight transportation is the activity that has the greatest impact on urban areas in terms of sustainability and livability, and it is, therefore, necessary to reduce its impact. Currently, there is a lack of methodologies to validate the methods proposed by companies to reduce their impacts. The proposed methodology presents the implementation of a KPI (Key Performance Indicator) based on the triple bottom line approach: economic, social and environmental, since a company with good results on the “triple bottom line” will experience an increase in its economic profitability and its environmental commitment while reducing the impacts that generate negative perceptions of it. This KPI is the OEEM (Overall Equipment Effectiveness for Mobility), a redesign of the well-known OEE (Overall Equipment Effectiveness), but adapted to the needs of urban freight transportation since this indicator provides a quick overview of the efficiency or performance of the activity according to five components: quality of deliveries, vehicle utilization, availability of the vehicle–driver tandem and efficiency (result of traffic and efficiency of delivery stops). The methodology developed will be implemented in a case study where the KPI will be calculated on the basis of real-time data and visualized on a control panel; thanks to this KPI, the company will be able to validate whether the measures taken have a positive or negative impact. Full article
(This article belongs to the Special Issue Application of New Technology and New Ideas in Intelligent Transportation System)
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22 pages, 2585 KiB  
Article
An Access Control Framework for Multilayer Rail Transit Systems Based on Trust and Sensitivity Attributes
by Xin Geng, Yinghong Wen, Zhisong Mo and Yu Liu
Appl. Sci. 2023, 13(23), 12904; https://doi.org/10.3390/app132312904 - 01 Dec 2023
Cited by 1 | Viewed by 669
Abstract
The construction of multilayer rail transit systems is a necessary way to realize “modern metropolitan areas on rail”, improve resource sharing, and increase travel services, where data integration is of utmost importance. To break data silos and realize data flow between different rail [...] Read more.
The construction of multilayer rail transit systems is a necessary way to realize “modern metropolitan areas on rail”, improve resource sharing, and increase travel services, where data integration is of utmost importance. To break data silos and realize data flow between different rail systems, a fine-grained access control framework is proposed in this paper. Through categorical and hierarchical schemes, a universal security scale is established for cross-domain data resources. Based on this, a trust and sensitivity attribute-based access control (TSABAC) model is put forward to describe the characteristics of the access control process. Furthermore, the method of policy integration is discussed, as well as the solution to the policy incompatibility problem, due to cross-domain interaction. As shown in practical application and simulation analysis, this framework can meet the requirements of security and granularity. This research is of great significance for promoting the high-quality development of urban agglomerations and metropolitan areas, and improving the quality and efficiency of rail transit. Full article
(This article belongs to the Special Issue Application of New Technology and New Ideas in Intelligent Transportation System)
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Review

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17 pages, 2573 KiB  
Review
Remote Sensing and Machine Learning for Safer Railways: A Review
by Wesam Helmi, Raj Bridgelall and Taraneh Askarzadeh
Appl. Sci. 2024, 14(9), 3573; https://doi.org/10.3390/app14093573 - 24 Apr 2024
Viewed by 395
Abstract
Regular railway inspections are crucial for maintaining their safety and efficiency. However, traditional inspection methods are complex and expensive. Consequently, there has been a significant shift toward combining remote sensing (RS) and machine learning (ML) techniques to enhance the efficiency and accuracy of [...] Read more.
Regular railway inspections are crucial for maintaining their safety and efficiency. However, traditional inspection methods are complex and expensive. Consequently, there has been a significant shift toward combining remote sensing (RS) and machine learning (ML) techniques to enhance the efficiency and accuracy of railway defect monitoring while reducing costs. The advantages of RS-ML techniques include their ability to automate and refine inspection processes and address challenges such as image quality and methodological limitations. However, the integration of RS and ML in railway monitoring is an emerging field, with diverse methodologies and outcomes that the research has not yet synthesized. To fill this gap, this study conducted a systematic literature review (SLR) to consolidate the existing research on RS-ML applications in railway inspection. The SLR meticulously compiled and analyzed relevant studies, evaluating the evolution of research trends, methodological approaches, and the geographic distribution of contributions. The findings showed a notable increase in relevant research activity over the last five years, highlighting the growing interest in this realm. The key methodological patterns emphasize the predominance of approaches based on convolutional neural networks, a variant of artificial neural networks, in achieving high levels of precision. These findings serve as a foundational resource for academics, researchers, and practitioners in the fields of computer science, engineering, and transportation to help guide future research directions and foster the development of more efficient, accurate, and cost-effective railway inspection methods. Full article
(This article belongs to the Special Issue Application of New Technology and New Ideas in Intelligent Transportation System)
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