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Sustainability
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Autonomous Systems and Intelligent Transportation Systems
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Autonomous Systems and Intelligent Transportation Systems

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

Deadline for manuscript submissions: closed (5 April 2024) | Viewed by 3287

Special Issue Editors

Prof. Eduardo Mojica-Nava

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, Universidad Nacional de Colombia, 111321 Bogotá, Colombia
Interests: network and control optimization; autonomous systems; intelligent systems
Dr. Juan Manuel Rey

E-Mail Website
Guest Editor
Electrical, Electronic and Telecommunications Engineering School, Universidad Industrial de Santander, Bucaramanga, Colombia
Interests: sustainability; renewable energy; intelligent control

Special Issue Information

Dear Colleagues,

The transportation system is a large-scale complex and structured system that impacts almost all of the components of a local society. The transportation system is also recognized as one of the largest contributors to emissions. Intelligent transportation systems (ITS) have emerged as the integration of advanced transportation technologies to crucially contribute to a greener, safer, and more efficient future transportation system. On the other hand, autonomous systems will transform ITS with several technological enablers such as vehicle IoT, big data, autonomous driving, machine learning, and advanced networked control methods to accomplish safer, coordinated, and sustainable traffic management. Several theoretical and technological aspects have to be considered in the future deployment of autonomous systems in ITS such as uncertainties and nonlinearities in models, sensitivity to perturbations, and the fact that the control involves multiple heterogeneous agents that are distributed and hierarchical. 

This Special Issue covers different topics that address the most recent advances in autonomous systems for ITS. The articles are expected to describe original findings or innovative concepts addressing different aspects of autonomous systems such as real-time network optimization, cloud and fog computing, and autonomous driving for ITS challenges for a greener and smarter transportation system.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Autonomous driving for sustainable ITS
  2. Multiagent machine learning for large-scale traffic flow control
  3. Traffic management through the vehicular to everything communication (V2X)
  4. Decision-making for vehicular networks in dynamic environment
  5. Federated learning-based ITS
  6. Cloud and Fog computing for decision-making in ITS
  7. Real-time network optimization in ITS

We look forward to receiving your contributions.

Prof. Dr. Eduardo Mojica-Nava
Dr. Juan Manuel Rey
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

  • autonomous driving
  • intelligent transportation systems
  • federated learning in vehicle networks
  • network optimization
  • traffic management

Published Papers (3 papers)

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Research

23 pages, 7908 KiB  
Article
Assessing the Impact of CAV Driving Strategies on Mixed Traffic on the Ring Road and Freeway
by Haizhen Li, Claudio Roncoli, Weiming Zhao and Yongfeng Ju
Sustainability 2024, 16(8), 3179; https://doi.org/10.3390/su16083179 - 10 Apr 2024
Viewed by 452
Abstract
The increasing traffic congestion has led to several negative consequences, with traffic oscillation being a major contributor to the problem. To mitigate traffic waves, the impact of the connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC), FollowerStopper (FS), and jam-absorption driving [...] Read more.
The increasing traffic congestion has led to several negative consequences, with traffic oscillation being a major contributor to the problem. To mitigate traffic waves, the impact of the connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC), FollowerStopper (FS), and jam-absorption driving (JAD) strategies on circular and linear scenarios have been evaluated. The manual vehicle is the intelligent driver model (IDM) and human driver model (HDM), respectively. The results suggest that on the ring road, the traffic performance of mixed traffic improves gradually with the increase of the proportion of CAVs under the ACC. Moreover, the traffic performance for the JAD strategy does not improve infinitely with the increase in the number of CAVs. Conversely, the FS strategy suppresses traffic waves at the cost of reducing traffic flow, and more CAVs are not beneficial for mixed traffic. It is interesting to note that under optimal performance in these three strategies, the FS strategy has the lowest number of CAVs, while the ACC strategy has the highest number of CAVs. For the linear road, it demonstrates that the JAD strategy exhibits a superior performance compared to the ACC. However, the FS strategy cannot dissipate traffic waves due to an insufficient buffer gap. Different models have varying effects on different strategies. Full article
(This article belongs to the Special Issue Autonomous Systems and Intelligent Transportation Systems)
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19 pages, 912 KiB  
Article
An Improved Big Data Analytics Architecture Using Federated Learning for IoT-Enabled Urban Intelligent Transportation Systems
by Sarah Kaleem, Adnan Sohail, Muhammad Usman Tariq and Muhammad Asim
Sustainability 2023, 15(21), 15333; https://doi.org/10.3390/su152115333 - 26 Oct 2023
Cited by 3 | Viewed by 1265
Abstract
The exponential growth of the Internet of Things has precipitated a revolution in Intelligent Transportation Systems, notably in urban environments. An ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. At the same time, [...] Read more.
The exponential growth of the Internet of Things has precipitated a revolution in Intelligent Transportation Systems, notably in urban environments. An ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. At the same time, these IoT-enabled ITSs generate a vast array of complex data classified as Big Data. Traditional data analytics frameworks need help to efficiently process these Big Data due to its sheer volume, velocity, variety, and significant data privacy concerns. Federated Learning, known for its privacy-preserving attributes, is a promising technology for implementation within ITSs for IoT-generated Big Data. Nevertheless, the system faces challenges due to the variable nature of devices, the heterogeneity of data, and the dynamic conditions in which ITS operates. Recent efforts to mitigate these challenges focus on the practical selection of an averaging mechanism during the server’s aggregation phase and practical dynamic client training. Despite these efforts, existing research still relies on personalized FL with personalized averaging and client training. This paper presents a personalized architecture, including an optimized Federated Averaging strategy that leverages FL for efficient and real-time Big Data analytics in IoT-enabled ITSs. Various personalization methods are applied to enhance the traditional averaging algorithm. Local fine-tuning and weighted averaging tailor the global model to individual client data. Custom learning rates are utilized to boost the performance further. Regular evaluations are advised to maintain model efficacy. The proposed architecture addresses critical challenges like real-life federated environment settings, data integration, and significant data privacy, offering a comprehensive solution for modern urban transportation systems using Big Data. Using the Udacity Self-Driving Car Dataset foe vehicle detection, we apply the proposed approaches to demonstrate the efficacy of our model. Our empirical findings validate the superiority of our architecture in terms of scalability, real-time decision-making capabilities, and data privacy preservation. We attained accuracy levels of 93.27%, 92.89%, and 92.96% for our proposed model in a Federated Learning architecture with 10 nodes, 20 nodes, and 30 nodes, respectively. Full article
(This article belongs to the Special Issue Autonomous Systems and Intelligent Transportation Systems)
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21 pages, 2125 KiB  
Article
Cooperative Cruise Control for Intelligent Connected Vehicles: A Bargaining Game Approach
by Miguel F. Arevalo-Castiblanco, Jaime Pachon, Duvan Tellez-Castro and Eduardo Mojica-Nava
Sustainability 2023, 15(15), 11898; https://doi.org/10.3390/su151511898 - 02 Aug 2023
Cited by 1 | Viewed by 878
Abstract
Intelligent transportation systems (ITSs) are at the forefront of advancements in transportation, offering enhanced efficiency, safety, and environmental friendliness. To enable ITSs, autonomous systems play a pivotal role, contributing to the development of autonomous driving, data-driven modeling, and multiagent control strategies to establish [...] Read more.
Intelligent transportation systems (ITSs) are at the forefront of advancements in transportation, offering enhanced efficiency, safety, and environmental friendliness. To enable ITSs, autonomous systems play a pivotal role, contributing to the development of autonomous driving, data-driven modeling, and multiagent control strategies to establish sustainable and coordinated traffic management. The integration of networked and automated vehicles has garnered significant attention as a potential solution for alleviating traffic congestion and improving fuel economy, achieved through global route optimization and cooperative driving. This study focuses on a predictive control perspective to address the cooperative cruise control problem. Online decision making is employed during the driving process, utilizing information gathered from the network. By employing bargaining games to establish an operating agreement among vehicles, we formalize a synchronization approach based on predictive control theory. Ultimately, these findings are put to the test in an emulation environment within a hardware-in-the-loop system. The results revealed that the proposed cruise control successfully achieved convergence toward the desired reference signal. These results demonstrate the effectiveness of our approach in achieving synchronized platoon behavior and correct bargaining outcomes. These findings underscore the effectiveness and potential of DMPC with bargaining games in coordinating and optimizing vehicular networks. This paves the way for future research and development in this promising area. Full article
(This article belongs to the Special Issue Autonomous Systems and Intelligent Transportation Systems)
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