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Sustainable Urban Transportation: Vehicle to Everything (V2X) and Applications of...
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Sustainable Urban Transportation: Vehicle to Everything (V2X) and Applications of Big Data

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 7056

Special Issue Editors

Dr. Ali Moradi Amani

E-Mail Website
Guest Editor
School of Engineering, RMIT University, Melbourne, VIC 3001, Australia
Interests: control systems; smart algorithms in active power distribution grids; electric vehicles and electrification of public transport systems
Dr. Hamid Khayyam

E-Mail Website
Guest Editor
Department of Mechanical and Automotive Engineering, School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
Interests: artificial intelligence; machine learning and adaptive intelligent systems; limited and big data modelling; Industry 4.0 and Education 4.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy security and environmental concerns are forcing governments to change both energy and transportation systems in the context of smart grids and smart cities. The transport sector is responsible for about 20% of greenhouse emissions, which is why many countries are pushing the uptake of EVs in both the public and private sectors. This is the point where mobility meets electricity and brings new challenges and opportunities to smart cities. In addition to these new technologies, autonomous vehicles (AVs) have been a hot research topic in both academia and industry throughout the last decade.

In order to sustainably integrate these transportation facilities into smart cities, intelligent and data-driven mobility algorithms, such as dynamic routing and bus route allocation technics, alongside efficient energy management systems for control of AVs and smart charging of EVs, for instance, should be developed. This requires a significant communication payload between infrastructure and vehicles inside the city. Thanks to the advancement of measurement and communication technologies and the emergence of IoT, massive amounts of data from different parts of a city are now accessible, which can compensate for a lack of precise models of the complicated environment of smart cities. With the significant progress in the field of artificial intelligence and machine learning, we are now at the point of being able to efficiently use the data and improve the performance of model-based algorithms.

Despite several research efforts in addressing the aforementioned concerns, there are still significant knowledge gaps in sustainable, reliable, integrated and optimal solutions for Vehicle-to-Everything (V2X) solutions. The aim of this Special Issue is to address data-driven and intelligent approaches to address V2X problems in a smart city. Original research articles and reviews are welcome. Research areas, in the context of the interaction of EVs and AVs with city infrastructure, may include (but are not limited to) the following:

  • Zero-emission urban mobility using V2X;
  • Advanced mobility and traffic management using V2X;
  • Technical issues and standards for V2X (including Vehicle-to-Infrastructure, -Vehicle, -Pedestrian, -Grid, and -Network);
  • Artificial intelligence and machine learning for V2X;
  • Connected Vehicles and urban traffic management systems;
  • Implementation of V2X;
  • Energy efficiency improvement using V2X;
  • Smart bi-directional charging of electric vehicles and E-buses;
  • Optimal size and location of EV and E-bus charging stations;
  • Improving resilience and reliability of urban mobility using V2X.

We look forward to receiving your contributions.

Dr. Ali Moradi Amani
Dr. Hamid Khayyam
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

  • sustainable mobility
  • autonomous vehicles
  • electric vehicles
  • vehicle-to-everything
  • big data
  • artificial intelligence
  • machine learning
  • electric vehicles

Published Papers (3 papers)

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Research

21 pages, 6669 KiB  
Article
An Efficient Approach to Monocular Depth Estimation for Autonomous Vehicle Perception Systems
by Mehrnaz Farokhnejad Afshar, Zahra Shirmohammadi, Seyyed Amir Ali Ghafourian Ghahramani, Azadeh Noorparvar and Ali Mohammad Afshin Hemmatyar
Sustainability 2023, 15(11), 8897; https://doi.org/10.3390/su15118897 - 31 May 2023
Cited by 1 | Viewed by 1997
Abstract
Depth estimation is critical for autonomous vehicles (AVs) to perceive their surrounding environment. However, the majority of current approaches rely on costly sensors, making wide-scale deployment or integration with present-day transportation difficult. This issue highlights the camera as the most affordable and readily [...] Read more.
Depth estimation is critical for autonomous vehicles (AVs) to perceive their surrounding environment. However, the majority of current approaches rely on costly sensors, making wide-scale deployment or integration with present-day transportation difficult. This issue highlights the camera as the most affordable and readily available sensor for AVs. To overcome this limitation, this paper uses monocular depth estimation as a low-cost, data-driven strategy for approximating depth from an RGB image. To achieve low complexity, we approximate the distance of vehicles within the frontal view in two stages: firstly, the YOLOv7 algorithm is utilized to detect vehicles and their front and rear lights; secondly, a nonlinear model maps this detection to the corresponding radial depth information. It is also demonstrated how the attention mechanism can be used to enhance detection precision. Our simulation results show an excellent blend of accuracy and speed, with the mean squared error converging to 0.1. The results of defined distance metrics on the KITTI dataset show that our approach is highly competitive with existing models and outperforms current state-of-the-art approaches that only use the detected vehicle’s height to determine depth. Full article
(This article belongs to the Special Issue Sustainable Urban Transportation: Vehicle to Everything (V2X) and Applications of Big Data)
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16 pages, 2983 KiB  
Article
Data-Driven Modeling of Vehicle-to-Grid Flexibility in Korea
by Moon-Jong Jang, Taehoon Kim and Eunsung Oh
Sustainability 2023, 15(10), 7938; https://doi.org/10.3390/su15107938 - 12 May 2023
Viewed by 1273
Abstract
With the widespread use of electric vehicles (EVs), the potential to utilize them as flexible resources has increased. However, the existing vehicle-to-grid (V2G) studies have focused on V2G operation methods. The operational performance is limited by the amount of availability resources, which represents [...] Read more.
With the widespread use of electric vehicles (EVs), the potential to utilize them as flexible resources has increased. However, the existing vehicle-to-grid (V2G) studies have focused on V2G operation methods. The operational performance is limited by the amount of availability resources, which represents the flexibility. This study proposes a data-driven modeling method to estimate the V2G flexibility. A charging station is a control point connected to a power grid for V2G operation. Therefore, the charging stations’ statuses were analyzed by applying the basic queuing model with a dataset of 1008 chargers (785 AC chargers and 223 DC chargers) from 500 charging stations recorded in Korea. The basic queuing model obtained the long-term average status values of the stations over the entire time period. To estimate the V2G flexibility over time, a charging station status modeling method was proposed within a time interval. In the proposed method, the arrival rate and service time were modified according to the time interval, and the station status was expressed in a propagated form that considered the current and previous time slots. The simulation results showed that the proposed method effectively estimated the actual value within a 10% mean absolute percentage error. Moreover, the determination of V2G flexibility based on the charging station status is discussed herein. According to the results, the charging station status in the next time slot, as well as that in the current time slot, is affected by the V2G. Therefore, to estimate the V2G flexibility, the propagation effect must be considered. Full article
(This article belongs to the Special Issue Sustainable Urban Transportation: Vehicle to Everything (V2X) and Applications of Big Data)
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22 pages, 7360 KiB  
Article
Noise Pollution Analysis Using Geographic Information System, Agglomerative Hierarchical Clustering and Principal Component Analysis in Urban Sustainability (Case Study: Tehran)
by Amir Esmael Forouhid, Shahrzad Khosravi and Jafar Mahmoudi
Sustainability 2023, 15(3), 2112; https://doi.org/10.3390/su15032112 - 22 Jan 2023
Cited by 1 | Viewed by 3173
Abstract
In this study, a new approach has been used with SPSS and MATLAB analysis to study urban road traffic noise distribution mapping, to obtain the representative road traffic noise maps. The observation has been performed at a high traffic highway. The factors influencing [...] Read more.
In this study, a new approach has been used with SPSS and MATLAB analysis to study urban road traffic noise distribution mapping, to obtain the representative road traffic noise maps. The observation has been performed at a high traffic highway. The factors influencing noise level (traffic, road width, slope, and residential or administrative–commercial land) use were surveyed and recorded for each point and their local and time dependencies were computed. According to the analysis, the maximum value of goodness of fit index for the traffic and noise level relationship was 0.64, followed by 0.489 for the percentage of residential land use. The result of this study showed that the vehicle speed, width of the road, and the land use can affect different sound levels emitted by moving vehicles on road. The model predicts that by increasing one vehicle per hour, an increase in noise level by 0.002 dB will happen. Full article
(This article belongs to the Special Issue Sustainable Urban Transportation: Vehicle to Everything (V2X) and Applications of Big Data)
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