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Sustainable Smart Urban Transport System
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Sustainable Smart Urban Transport System

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 10439

Special Issue Editors

Prof. Dr. Péter Gáspár

E-Mail Website
Guest Editor
Systems and Control Laboratory, Institute for Computer Science and Control, Kende u. 13–17, 1111 Budapest, Hungary
Interests: control theory; vehicle dynamics and control
Special Issues, Collections and Topics in MDPI journals
Dr. Tamás Bécsi

E-Mail Website
Guest Editor
Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, Muegyetem rkp. 3, 1111 Budapest, Hungary
Interests: reinforcement learning; intelligent transportation systems; autonomous vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The research problems of smart urban transport systems, together with connected and automated vehicles (CAVs), have come to focus in recent decades, with many open issues in the field of environment sensing and understanding of decision making and control. In all these areas, strict safety requirements apply, and their environmental impact is also considerable. Therefore, the vehicles of the future need to interact cooperatively to achieve a reduction in ecological effects. The utilization of V2X communication and cloud computing also enables research on cooperative control design for multiple vehicles to achieve globally optimal traffic in terms of the above criteria.

This Special Issue invites all topic-related results addressing these challenges, such as environment perception, machine learning, model-driven decision-making methods, and the cooperative control of vehicle groups. Although most of the research focuses on autonomous cars, automated and energy-efficient solutions for all means of transportation are also welcome. Additionally, as the-machine learning-based approaches of self-driving vehicles require high computational resources and a large amount of data for testing and validation, the development of different frameworks, such as hardware-in-the-loop, simulations, and virtual and augmented reality, is also welcome.

Authors are invited to submit their work, such as original research articles, case studies, or surveys on the subject.

Prof. Dr. Péter Gáspár
Dr. Tamás Bécsi
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

  • urban transport systems
  • intelligent transportation systems
  • self-driving cars
  • automatic train operation
  • energy-efficient control
  • machine learning
  • reinforcement learning
  • robust control
  • cooperative control
  • testing and validation
  • development frameworks

Published Papers (4 papers)

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Research

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20 pages, 1932 KiB  
Article
Improving Sustainable Safe Transport via Automated Vehicle Control with Closed-Loop Matching
by Tamás Hegedűs, Dániel Fényes, Balázs Németh and Péter Gáspár
Sustainability 2021, 13(20), 11264; https://0-doi-org.brum.beds.ac.uk/10.3390/su132011264 - 13 Oct 2021
Cited by 7 | Viewed by 1523
Abstract
The concept of vehicle automation is a promising approach to achieve sustainable transport systems, especially in an urban context. Automation requires the integration of learning-based approaches and methods in control theory. Through the integration, a high amount of information in automation can be [...] Read more.
The concept of vehicle automation is a promising approach to achieve sustainable transport systems, especially in an urban context. Automation requires the integration of learning-based approaches and methods in control theory. Through the integration, a high amount of information in automation can be incorporated. Thus, a sustainable operation, i.e., energy-efficient and safe motion with automated vehicles, can be achieved. Despite the advantages of integration with learning-based approaches, enhanced vehicle automation poses crucial safety challenges. In this paper, a novel closed-loop matching method for control-oriented purposes in the context of vehicle control systems is presented. The goal of the method is to match the nonlinear vehicle dynamics to the dynamics of a linear system in a predefined structure; thus, a control-oriented model is obtained. The matching is achieved by an additional control input from a neural network, which is designed based on the input–output signals of the nonlinear vehicle system. In this paper, the process of closed-loop matching, i.e., the dataset generation, the training, and the evaluation of the neural network, is proposed. The evaluation process of the neural network through data-driven reachability analysis and statistical performance analysis methods is carried out. The proposed method is applied to achieve the path following functionality, in which the nonlinearities of the lateral vehicle dynamics are handled. The effectiveness of the closed-loop matching and the designed control functionality through high fidelity CarMaker simulations is illustrated. Full article
(This article belongs to the Special Issue Sustainable Smart Urban Transport System)
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18 pages, 1359 KiB  
Article
Traffic Signal Control via Reinforcement Learning for Reducing Global Vehicle Emission
by Bálint Kővári, Lászlo Szőke, Tamás Bécsi, Szilárd Aradi and Péter Gáspár
Sustainability 2021, 13(20), 11254; https://0-doi-org.brum.beds.ac.uk/10.3390/su132011254 - 12 Oct 2021
Cited by 10 | Viewed by 2304
Abstract
The traffic signal control problem is an extensively researched area providing different approaches, from classic methods to machine learning based ones. Different aspects can be considered to find an optima, from which this paper emphasises emission reduction. The core of our solution is [...] Read more.
The traffic signal control problem is an extensively researched area providing different approaches, from classic methods to machine learning based ones. Different aspects can be considered to find an optima, from which this paper emphasises emission reduction. The core of our solution is a novel rewarding concept for deep reinforcement learning (DRL) which does not utilize any reward shaping, hence exposes new insights into the traffic signal control (TSC) problem. Despite the omission of the standard measures in the rewarding scheme, the proposed approach can outperform a modern actuated control method in classic performance measures such as waiting time and queue length. Moreover, the sustainability of the realized controls is also placed under investigation to evaluate their environmental impacts. Our results show that the proposed solution goes beyond the actuated control not just in the classic measures but in emission-related measures too. Full article
(This article belongs to the Special Issue Sustainable Smart Urban Transport System)
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Review

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34 pages, 15260 KiB  
Review
Autonomous Vehicles in Mixed Traffic Conditions—A Bibliometric Analysis
by Muhammad Azam, Sitti Asmah Hassan and Othman Che Puan
Sustainability 2022, 14(17), 10743; https://0-doi-org.brum.beds.ac.uk/10.3390/su141710743 - 29 Aug 2022
Cited by 3 | Viewed by 3142
Abstract
Autonomous Vehicles (AVs) with their immaculate sensing and navigating capabilities are expected to revolutionize urban mobility. Despite the expected benefits, this emerging technology has certain implications pertaining to their deployment in mixed traffic streams, owing to different driving logics than Human-driven Vehicles (HVs). [...] Read more.
Autonomous Vehicles (AVs) with their immaculate sensing and navigating capabilities are expected to revolutionize urban mobility. Despite the expected benefits, this emerging technology has certain implications pertaining to their deployment in mixed traffic streams, owing to different driving logics than Human-driven Vehicles (HVs). Many researchers have been working to devise a sustainable urban transport system by considering the operational and safety aspects of mixed traffic during the transition phase. However, limited scholarly attention has been devoted to mapping an overview of this research area. This paper attempts to map the state of the art of scientific production about autonomous vehicles in mixed traffic conditions, using a bibliometric analysis of 374 documents extracted from the Scopus database from 1999 to 2021. The VOSviewer 1.1.18 and Biblioshiny 3.1 software were used to demonstrate the progress status of the publications concerned. The analysis revealed that the number of publications has continuously increased during the last five years. The text analysis showed that the author keywords “autonomous vehicles” and “mixed traffic” dominated the other author keywords because of their frequent occurrence. From thematic analysis, three research stages associated with AVs were identified; pre-development (1999–2017), development (2017–2020) and deployment (2021). The study highlighted the potential research areas, such as involvement of autonomous vehicles in transportation planning, interaction between autonomous vehicles and human driven vehicles, traffic and energy efficiencies associated with automated driving, penetration rates for autonomous vehicles in mixed traffic scenarios, and safe and efficient operation of autonomous vehicles in mixed traffic environment. Additionally, discussion on the three key aspects was conducted, including the impacts of AVs, their driving characteristics and strategies for their successful deployment in context of mixed traffic. This paper provides ample future directions to the people willing to work in this area of autonomous vehicles in mixed traffic conditions. The study also revealed current trends as well as potential future hotspots in the area of autonomous vehicles in mixed traffic. Full article
(This article belongs to the Special Issue Sustainable Smart Urban Transport System)
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19 pages, 292 KiB  
Review
Theory and Experiment of Cooperative Control at Multi-Intersections in Intelligent Connected Vehicle Environment: Review and Perspectives
by Linan Zhang, Yizhe Wang and Huaizhong Zhu
Sustainability 2022, 14(3), 1542; https://0-doi-org.brum.beds.ac.uk/10.3390/su14031542 - 28 Jan 2022
Cited by 13 | Viewed by 2774
Abstract
A heterogeneous traffic flow consists of regular vehicles, and intelligent connected vehicles having interactive functions is updating the composition of the current urban-road network traffic flow. It has been a growing trend and will continue to be so. Because of the urgent demand, [...] Read more.
A heterogeneous traffic flow consists of regular vehicles, and intelligent connected vehicles having interactive functions is updating the composition of the current urban-road network traffic flow. It has been a growing trend and will continue to be so. Because of the urgent demand, the research focused on three main parts of cooperative control methods under intelligent connected vehicles environment, typical traffic control application scenarios and experimental validation in intelligent connected vehicles conditions, and intersection-oriented hybrid traffic control mechanism for urban road. For heterogeneous interrupted traffic flow of intelligent connected vehicles, to analyze the characteristics and information extraction method of heterogeneous traffic flow of intelligent connected vehicles under different conditions, the research examined driving modes of regular vehicles and intelligent connected vehicles, including car following and lane changing. This study summarized control modes of traffic-signal control, active control of intelligent connected vehicles, and indirect control of regular vehicles through intelligent vehicles to study the active control mechanism and multi-intersection coordinated control strategy for intelligent connected vehicle heterogeneous traffic flow. With the combination of coordinated control theory, this work overviewed integrated experiment of information interaction and coordinated control under intelligent-connected-vehicle heterogeneous traffic-flow environments. Full article
(This article belongs to the Special Issue Sustainable Smart Urban Transport System)
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