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Intelligent Transportation System
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Intelligent Transportation System

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 2854

Special Issue Editors

Prof. Dr. Soi-Hoi Michael Lam

E-Mail Website
Guest Editor
Faculty of Science and Technology, University of Macau, E11Avenida da Universidade, Taipa, Macau, China
Interests: transportation planning and policy; smart mobility and ITS; public transit planning; transportation management and control
Dr. Rodrigo Da Rosa Righi

E-Mail Website
Guest Editor
Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos, Sao Leopoldo, Brazil
Interests: distributed systems; load balacing; high-performance computing; internet of things

Special Issue Information

Dear Colleagues,

Electric vehicles (EVs) are a key to Intelligent Transportation Systems (ITS) and are gaining share in urban commuting, especially they are becoming a sustainable way of travel in urban areas promoting smart mobility and combating climate change.  Furthermore, recent technological advances have made EVs more efficient, more reliable, and more convenient to use.  Electric mobility which centers around the use of EVs as a strategy to achieve a zero-carbon energy future has also been achieving higher popularity worldwide.  Green and smart traveling also find great opportunities in ITS through communication technologies such as vehicle-to-vehicle and vehicle-to-infrastructure communication with the omnipresence of the Internet-of-Things (IoT). These have facilitated a worldwide trend in deploying more EVs in transportation, not only in private vehicles but also in public transport modes, in passenger as well as freight transportation, to achieve overall sustainability.  

In this special issue, the following issues and opportunities surrounding enhanced utilization of electric vehicles in an intelligent transportation system will be explored and discussed.

  • Electric mobility and EV charging within ITS
  • International cases of deployments and business models of electric vehicles in ITS
  • Methodologies to examine the efficiency and effects of electric vehicles
  • Networked EV, IoT, smart grids, and their developments in ITS
  • Policies and strategies to promote and implement electric mobility

Prof. Dr. Soi-Hoi Michael Lam
Dr. Rodrigo Da Rosa Righi
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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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.

Published Papers (2 papers)

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Research

14 pages, 6593 KiB  
Article
High-Accuracy, High-Efficiency, and Comfortable Car-Following Strategy Based on TD3 for Wide-to-Narrow Road Sections
by Pinpin Qin, Fumao Wu, Shenglin Bin, Xing Li and Fuming Ya
World Electr. Veh. J. 2023, 14(9), 244; https://doi.org/10.3390/wevj14090244 - 03 Sep 2023
Viewed by 1060
Abstract
To address traffic congestion in urban expressways during the transition from wide to narrow sections, this study proposed a car-following strategy based on deep reinforcement learning. Firstly, a car-following strategy was developed based on a twin-delayed deep deterministic policy gradient (TD3) algorithm, and [...] Read more.
To address traffic congestion in urban expressways during the transition from wide to narrow sections, this study proposed a car-following strategy based on deep reinforcement learning. Firstly, a car-following strategy was developed based on a twin-delayed deep deterministic policy gradient (TD3) algorithm, and a multi-objective constrained reward function was designed by comprehensively considering safety, traffic efficiency, and ride comfort. Secondly, 214 car-following periods and 13 platoon-following periods were selected from the natural driving database for the strategies training and testing. Finally, the effectiveness of the proposed strategy was verified through simulation experiments of car-following and platoon-following. The results showed that compared to human-driven vehicles (HDV), the TD3 and deep deterministic policy gradient (DDPG)-based strategies enhanced traffic efficiency by over 29% and ride comfort by more than 60%. Furthermore, compared to DDPG, the relative errors between the following distance and desired safety distance using TD3 could be reduced by 1.28% and 1.37% in simulation experiments of car-following and platoon-following, respectively. This study provides a new approach to alleviate traffic congestion for wide-to-narrow road sections in urban expressways. Full article
(This article belongs to the Special Issue Intelligent Transportation System)
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13 pages, 670 KiB  
Article
An Indicator-Based Methodological Framework for Assessing an eMaaS Scheme
by Anastasia Nikolaidou, Efthymis Papadopoulos, Ioannis Politis and Socrates Basbas
World Electr. Veh. J. 2023, 14(7), 186; https://doi.org/10.3390/wevj14070186 - 14 Jul 2023
Viewed by 1224
Abstract
Mobility as a Service (MaaS) and, more recently, electric Mobility as a Service (eMaaS) have increasingly been put forward to meet the economic, social, and environmental challenges linked to mobility. First, however, monitoring and evaluating such a scheme’s performance is crucial, mainly through [...] Read more.
Mobility as a Service (MaaS) and, more recently, electric Mobility as a Service (eMaaS) have increasingly been put forward to meet the economic, social, and environmental challenges linked to mobility. First, however, monitoring and evaluating such a scheme’s performance is crucial, mainly through the definition of appropriate indicators. In this study, a standardised methodological approach is presented for the assessment of an eMaaS scheme. In addition, this methodological approach contains a range of innovative Key Performance Indicators (KPIs). The proposed KPIs refer to the evaluation of the scheme based on four pillars: (a) society, (b) users, (c) operators, and (d) internal operation. The methodology for evaluating the proposed KPIs includes identifying the available sources for data collection. For example, data can be collected through questionnaire surveys, focus group discussions, and the system’s central dashboard. An appropriate set of indicators to evaluate a system from various perspectives is necessary to assess an eMaaS scheme in real-life conditions. Furthermore, the evaluation of the overall operation of the scheme will contribute to drawing valid conclusions (e.g., user acceptance, economic viability) for the implementation of eMaaS in urban areas. Full article
(This article belongs to the Special Issue Intelligent Transportation System)
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