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Intelligent Systems and Consciousness Society: Sustainable Transportation and Decision Making
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Intelligent Systems and Consciousness Society: Sustainable Transportation and Decision Making

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 4330

Special Issue Editors

Prof. Dr. Yorgos Stephanedes

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Patras, Rio, 26500 Patras, Greece
Interests: sustainability; ITS; C-ITS; smart cities; multimodal transport; freight transport; traffic management
Prof. Dr. Nikos Karacapilidis

E-Mail Website
Guest Editor
Mechanical Engineering & Aeronautics Department, University of Patras, Rio, 26500 Patras, Greece
Interests: informed decision making; computational argumentation; business intelligence; knowledge management

Special Issue Information

Dear Colleagues,

Intelligent Systems are a key pillar in state-of-the-art research and technology to improve the quality of life of all citizens.

Intelligent Transportation Systems (ITS) are the foundation of advanced transportation, infrastructure and networks and ensure sustainable, safe, fast and comfortable movement of people and goods in smart cities and communities.

Intelligent Decision Making Systems are at the root of advanced methods for decision and policy making and are augmented by Machine Learning (ML) and Natural Language Processing (NLP) tools and technologies.

The scope of this Special Issue is to elaborate novel solutions in augmented Intelligent Decision Making, e.g., based on ML and NLP, and in the planning and design of urban road networks, motorways and transport hubs, e.g., ports and parking areas, within the framework of ITS, Cooperative Intelligent Transport Systems (C-ITS) and smart cities, thus empowering consciousness and improving decision/policy making in the above areas.

This Special Issue aims to bring together theoretical and empirical studies aiming to enhance system sustainability, intelligence, safety and security, while reducing the environmental, social and economic impacts.

We invite papers that address (but are not limited to) the integration of augmented Intelligent Decision Making, augmented policy making, ITS and C-ITS in dynamic transport and traffic systems, traffic management, traffic congestion, logistics and supply chain management, smart cities and communities, safety and security, communications, digital data management (big data and IoT) and environmental and health impacts.

We look forward to receiving your contributions.

Prof. Dr. Yorgos Stephanedes
Prof. Dr. Nikos Karacapilidis
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. 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 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

  • Intelligent Transportation Systems (ITS) 
  • C-ITS • Artificial Intelligence 
  • Machine Learning (ML) 
  • Natural Language Processing (NLP)
  • decision/policy making
  • dynamic transport
  • traffic systems
  • traffic management
  • safety and security
  • logistics
  • digital data management
  • sustainability
  • smart cities
  • impact

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

31 pages, 12260 KiB  
Article
Transport-Related Synthetic Time Series: Developing and Applying a Quality Assessment Framework
by Ayelet Gal-Tzur
Sustainability 2025, 17(3), 1212; https://doi.org/10.3390/su17031212 - 2 Feb 2025
Viewed by 852
Abstract
Data scarcity and privacy concerns in various fields, including transportation, have fueled a growing interest in synthetic data generation. Synthetic datasets offer a practical solution to address data limitations, such as the underrepresentation of minority classes, while maintaining privacy when needed. Notably, recent [...] Read more.
Data scarcity and privacy concerns in various fields, including transportation, have fueled a growing interest in synthetic data generation. Synthetic datasets offer a practical solution to address data limitations, such as the underrepresentation of minority classes, while maintaining privacy when needed. Notably, recent studies have highlighted the potential of combining real and synthetic data to enhance the accuracy of demand predictions for shared transport services, thereby improving service quality and advancing sustainable transportation. This study introduces a systematic methodology for evaluating the quality of synthetic transport-related time series datasets. The framework incorporates multiple performance indicators addressing six aspects of quality: fidelity, distribution matching, diversity, coverage, and novelty. By combining distributional measures like Hellinger distance with time-series-specific metrics such as dynamic time warping and cosine similarity, the methodology ensures a comprehensive assessment. A clustering-based evaluation is also included to analyze the representation of distinct sub-groups within the data. The methodology was applied to two datasets: passenger counts on an intercity bus route and vehicle speeds along an urban road. While the synthetic speed dataset adequately captured the diversity and patterns of the real data, the passenger count dataset failed to represent key cluster-specific variations. These findings demonstrate the proposed methodology’s ability to identify both satisfactory and unsatisfactory synthetic datasets. Moreover, its sequential design enables the detection of gaps in deeper layers of similarity, going beyond basic distributional alignment. This work underscores the value of tailored evaluation frameworks for synthetic time series, advancing their utility in transportation research and practice. Full article
(This article belongs to the Special Issue Intelligent Systems and Consciousness Society: Sustainable Transportation and Decision Making)
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18 pages, 3580 KiB  
Article
Connected Intelligent Transportation System Model to Minimize Societal Cost of Travel in Urban Networks
by Athanasios I. Koukounaris and Yorgos J. Stephanedes
Sustainability 2023, 15(21), 15383; https://doi.org/10.3390/su152115383 - 27 Oct 2023
Cited by 4 | Viewed by 1365
Abstract
The increasing societal cost of vehicle travel in urban networks is causing higher social and environmental impacts on road users and urban residents. The societal cost of travel can be reduced through implementation of more efficient traffic management solutions, deeper integration of connected [...] Read more.
The increasing societal cost of vehicle travel in urban networks is causing higher social and environmental impacts on road users and urban residents. The societal cost of travel can be reduced through implementation of more efficient traffic management solutions, deeper integration of connected vehicles in the traffic stream, and increased deployment of vehicle-to-infrastructure (V2I) systems. This work proposes an innovative traffic management solution, based on Urban Connected Intelligent Transport Systems. The solution dynamically manages traffic by controlling for speed and acceleration in connected vehicles through V2I to minimize societal cost in urban networks. This is achieved by minimizing all four components of societal cost of travel, i.e., traffic accidents, fuel consumption, pollutant emissions and travel time. By minimizing societal cost, this research contributes to safer, greener and more sustainable transport in urban networks, while reducing the adverse environmental and economic impact. Experimental and field data as well as data from simulation were used to test the proposed solution at an urban coastal area in Patras, Greece. Full article
(This article belongs to the Special Issue Intelligent Systems and Consciousness Society: Sustainable Transportation and Decision Making)
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20 pages, 2094 KiB  
Article
Dynamic Management of Urban Coastal Traffic and Port Access Control
by Konstantina P. Marousi and Yorgos J. Stephanedes
Sustainability 2023, 15(20), 14871; https://doi.org/10.3390/su152014871 - 13 Oct 2023
Cited by 1 | Viewed by 1354
Abstract
Urban traffic congestion and vehicle/passenger port recurring delays are major obstacles of coastal urban area sustainability. Most research in coastal urban road management has focused on congestion detection without the effective integration of the dynamic interactions with port queueing systems. For securing coastal [...] Read more.
Urban traffic congestion and vehicle/passenger port recurring delays are major obstacles of coastal urban area sustainability. Most research in coastal urban road management has focused on congestion detection without the effective integration of the dynamic interactions with port queueing systems. For securing coastal city environmental, social and economic efficiency, this paper develops and tests a dynamic urban coastal traffic and port management system. The integrated system controls traffic and port gates’ operations based on ITS/C-ITS methodologies. The system integrates dynamic models for congestion detection, using ANN and a parameterized model, on a coastal urban road network that leads to a city port and identifies optimal solutions for road traffic and port queuing gate control. The system communicates with users via connected vehicles and VMS. The system was tested in a coastal urban road leading to Patras Southern Port, Greece, and at port control gates. Field and simulation data were used to assess system performance and social–environmental impacts. The results reveal that the system’s application offers benefits to the individual driver moving towards the Port to board a ship (gaining at least 7 min and consuming 0.306 L less fuel) as well as to society (39.72% increase in traffic safety) and environment (1,445,132 g CO2 emission reduction). Full article
(This article belongs to the Special Issue Intelligent Systems and Consciousness Society: Sustainable Transportation and Decision Making)
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