Special Issue "Advanced Application of Sustainable Transportation: Intelligent and Autonomous Traffic Monitoring, Control and Management Systems for Smart Cities"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: 30 June 2022.

Special Issue Editors

Dr. Salama A. Mostafa
E-Mail Website
Chief Guest Editor
Center of Intelligent and Autonomous Systems (CIAS), Department of Software Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja 86400, Malaysia
Interests: ambient intelligence; edge computing; and IoT for smart building; machine learning and reinforcement learning for optimizing decisions; autonomous agents for monitoring and surveillance system; adjustable autonomy for transportation control
Dr. Mazin Abed Mohammed
E-Mail Website
Guest Editor
Information systems Department, College of Computer Science and Information Technology, University of Anbar, Ramadi, Anbar 31001, Iraq
Interests: artificial intelligence; computational intelligence for real-world applications; biomedical computing
Prof. Dr. Seifedine Kadry
E-Mail Website
Guest Editor
Department of Applied Data Science, Norrof University College, 4608 Kristiansand, Norway
Interests: Data science and applied mathematics
Dr. Deepak Gupta
E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Maharaja Agrasen institute of Technology (GGSIPU), Delhi 110086, India
Interests: software engineering; software usability; human computer interaction; algorithm computing; soft computing; neural networks; testing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on collecting research and review articles that are related to intelligent and autonomous traffic monitoring, control, and management systems. The Special Issue aims to explore and identify advanced research and applications for sustainable transportation in smart cities. The existing state-of-the-art studies have demonstrated that integrating intelligent and autonomous frameworks, models, algorithms, and architectures in transportation systems significantly impacts these systems' quality, sustainability, reliability, costs, and efficiency. Accordingly, the development of innovative Intelligent Transportation Systems (ITS) nowadays links with the collaboration of interdisciplinary smart systems that entail novel, ground-breaking, and sustainable solutions. The solutions might comprise Artificial Intelligence, Ambient Intelligence, Internet of Things (IoT), Fog Computing, Cloud Computing, Edge Processing or Edge Computing, Blockchain, Big Data Analytics, Machine Learning, Deep Learning, Reinforcement Learning, Autonomous Agent, Multi-agent Systems and Adjustable Autonomy as key technologies. These solutions and technologies are meant to facilitate operations and services in smart city environments to increase efficiency and safety and improve life quality. The targeted articles of this Special Issue include but are not limited to the following themes:

  • Big data analytics in ITS and smart city
  • Ambient Intelligence and IoT in ITS and smart city
  • Cloud computing and fog computing in ITS and smart city
  • Sustainable technology in ITS and smart cities
  • Environmental impact analysis in ITS and smart city
  • Efficient transportation solutions in ITS and smart city
  • Routing optimization in ITS and smart city
  • Ad hoc network (MANET, VANET & FANET) in ITS and smart city
  • Robotics and autonomous systems in ITS and smart city
  • Human-machine interaction in ITS and smart city
  • Computational solutions for COVID-19 in ITS and smart city
  • Advanced traffic monitoring in ITS and smart city
  • Advanced traffic control system in ITS and smart city
  • Advanced traffic management in ITS and smart city
  • Machine learning and Deep learning models in ITS and smart city
  • Decision-support system for the sustainability of ITS and smart city
  • Data and text mining for the sustainability of ITS and smart city

Dr. Salama A. Mostafa
Chief Guest Editor

Dr. Mazin Abed Mohammed
Prof. Dr. Seifedine Kadry
Dr. Deepak Gupta
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 papers will be 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. Sustainability 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 1900 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

  • ITS
  • smart city
  • big data analytics
  • ambient intelligence
  • cloud computing and fog computing
  • sustainable technology
  • environmental impact analysis
  • efficient transportation solutions
  • robotics and autonomous systems
  • advanced traffic monitoring
  • advanced traffic control system

Published Papers (2 papers)

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Research

Article
An Intelligent and Autonomous Sight Distance Evaluation Framework for Sustainable Transportation
Sustainability 2021, 13(16), 8885; https://doi.org/10.3390/su13168885 - 09 Aug 2021
Viewed by 197
Abstract
Railways are facing a serious problem of road vehicle–train collisions at unmanned railway level crossings. The purpose of the study is the development of a safe stopping sight distance and sight distance from road to rail track model with appropriate computation and analysis. [...] Read more.
Railways are facing a serious problem of road vehicle–train collisions at unmanned railway level crossings. The purpose of the study is the development of a safe stopping sight distance and sight distance from road to rail track model with appropriate computation and analysis. The scope of the study lies in avoiding road vehicle–train collisions at unmanned railway level crossings. An intelligent and autonomous framework is being developed using supervised machine learning regression algorithms. Further, a sight distance from road to rail track model is being developed for road vehicles of 0.5 to 10 m length using the observed geometric characteristics of the route. The model prediction accuracy obtained better results in the development of a stopping sight distance model in comparison to other intelligent algorithms. The developed model suggested an increment of approximately 23% in the current safe stopping sight distance on all unmanned railway level crossings. Further, the feature analysis indicates the ‘approach road gradient’ to be the major contributing parameter for safe stopping sight distance determination. The accident prediction study finally indicates that, as the safe stopping sight distance is increased by following the developed model, it is predicted to decrease road vehicle–train collisions. Full article
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Article
A Cyber-Physical System and Graph-Based Approach for Transportation Management in Smart Cities
Sustainability 2021, 13(14), 7606; https://doi.org/10.3390/su13147606 - 07 Jul 2021
Viewed by 487
Abstract
In the last decade, technological advancements in the cyber-physical system have set the basis for real-time and context-aware services to ease human lives. The citizens, especially travelers, want to experience a safe, healthy, and timely journey to their destination. Smart and on-ground real-time [...] Read more.
In the last decade, technological advancements in the cyber-physical system have set the basis for real-time and context-aware services to ease human lives. The citizens, especially travelers, want to experience a safe, healthy, and timely journey to their destination. Smart and on-ground real-time traffic analysis helps authorities further improve decision-making to ensure safe and convenient traveling. In this paper, we proposed a transport-control model that exploits cyber-physical systems (CPS) and sensor-technology to continuously monitor and mine the big city data for smart decision-making. The system makes use of travel-time, traffic intensity, vehicle’s speed, and current road conditions to construct a weighted city graph representing the road network. Traditional graph algorithms with efficient implementation technologies are employed to respond to commuters’ and authorities’ needs in order to achieve a smart and optimum transportation system. To efficiently process the incoming big data streams, the proposed architecture uses the Apache GraphX tool with several parallel processing nodes, along with Spark and Hadoop that ultimately provide better performance against various state-of-the-art solutions. The system is thoroughly evaluated in terms of system throughput and processing time, revealing that the proposed system is efficient, robust, and scalable. Full article
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